#plotly express table

[spreadsheetautomation]

What is Python Automation?

In the rapidly evolving world of technology, automation stands as a cornerstone, significantly enhancing efficiency, accuracy and productivity in various domains. At the heart of this transformation lies Python automation, a powerful approach to scripting and automating repetitive tasks that otherwise consume valuable time and resources.

The Essence of Python Automation

Python automation leverages the simplicity and versatility of the Python programming language to create scripts that perform tasks automatically. This can range from data analysis, file management and network configuration, to web scraping. The beauty of Python lies in its extensive libraries and frameworks that cater to different automation needs, making it accessible to both beginners and seasoned developers. Its syntax is clear and concise, reducing the complexity of writing automation scripts and making the process more intuitive.

Automating Excel Using Python

One of the most practical applications of Python automation is managing and manipulating Excel files, a task known as "automating Excel with Python". This involves using libraries such as Pandas and OpenPyXL to read, write and modify Excel spreadsheets without the need for manual input. Automating Excel using Python not only speeds up data processing tasks but also minimizes errors, ensuring that data management is both efficient and reliable.

In workplaces where Excel is a staple for reporting and data analysis, this aspect of Python automation proves invaluable. It allows users to automate data entry, formatting and even complex calculations, turning hours of manual work into a few minutes of script execution.

Conclusion

Python automation is transforming the landscape of digital workflows, offering scalable and efficient solutions to mundane tasks. From web development to automating Excel with Python, its applications are vast and varied, catering to the needs of a wide range of industries. As we move forward, the role of Python in automation is set to grow, highlighting its importance in driving productivity and innovation in the digital age.

Read a similar article about Python for healthcare here at this page.

[aashi-heartfilia]

A BREATH OF FRESH AIR??!! OCHAKO'S -BIGGEST DEVELOPMENT YET?!!

So, it was refreshing to say the least and if I'm being completely unbiased, I didn't like where Ochako's character was going. She was reduced to a side character.

But this chapter brings one of her biggest character dev to the table. It's like even after her grand speech, you can clearly see the impact this war had on her. On the surface however, she still pretends to be normal...

Comforting Mina (which is such an adorable thing to do), going along with Iida and Bakugo etc. Coming up with the plan and initiating Deku's rescue operation.

She looks okay but deep inside, when she's alone you can clearly spot her melancholy...

She was literally standing all alone just gazing into the destroyed city falling apart maybe just to keep herself from falling apart and think those so called useless things. After Deku thanked her notice how fast her expression changes back to normal.

And the moment of silence follows...

"I'm quite the oddball"

I really liked this statement of hers. It's simple and yet leaves a strong impression. She thinks she's an oddball and why shouldn't she?

They live in a society where heroes are worshipped and villains killed. She was told from her childhood and these knight in shining armors, these are heroes..

And yet not knowing Himiko's perspective makes her uncomfortable. Like what if she isn't the monster you imagine her to be? But Ocha doesn't want to go into a battle half hearted and that's why she is looking at the city again and again.

For Ocha is the kind of person that loves to see other people happy. So when during war she saw so many people in pain, she saw that Toga is the reason for their pain as well (aka The League of Villains). She just told her what she felt was right and yet after Toga left, her sorrowful expression was struck in Ochakos mind.

This side of hers appeared again after the sports festival when she confronted Deku and Iida about her desire to become strong and how she felt embarrassed about herself as a hero.

It's a side, we as an audience are familiar with!

It is a side we know Ocha has, deep inside that she represses!

Even now, she's doing everything possible to not let her desires come in the way.

A normal person would call this rational and maybe it really is tbh!

All the choices she has made so far..

She pushed her feelings for Deku aside in order to focus more on her hero work.

She continuously represses herself from doing too much and hence taking less risks.

In the Bonus Material it was stated that she doesn't even eat to save money and endure the heat to save electricity (keyword: ENDURE)

She's so rational that she wanted to do hero work for money despite loving it for so long

Even now... she's repressing... telling herself to take a look, again! At these fallen skyscrapers...all this destruction....

And she has seen it all...

So basically,

She understands that LoV are responsible for all this chaos but she is also uncomfortable with not knowing Toga's perspective.

Which is honestly, one of the more believable storylines lately. It's easy to see why Ochako wants to empathize with Toga but forcing herself not to by gawkin at the crumbling city.

She is forcing herself again to shut down Saving and keep fighting. And it is a bit different than Deku's version of Saving Shigaraki but that's for another blog!

Right now, it's an Uravity appreciation post andi liked where the chapter went with her despite having one of the weakest build-ups.

Predictions: Toga dies!

Remember this cover? See the similarities?

Now do you know what this Tarot symbolises? We have already seen Toga's quirk away coming from her desire to become like the people she holds dear.

What if it's Ocha's turn now?

Awakening: she can make things float without touching!

Now that would be OP as heck!! This time when Toga and Chalo are gonna battle it will be a clash of ideals and what if after understanding Toga's POV Ochako wants to save her? But well, things don't go as planned and with some third party intervention, Toga's life is at risk (OFA betrayal plotline) and OCHAKO'S Awakening for saving Toga?

But Toga dies in the end anyway saving Ochako and being her hero. Toga wanted to change and live a normal life like other girls I her age. But she doesn't gets that because of her quirk.

And yet she sacrifices herself to save Ocha? This can lead to Uravity Rising and she stops bottling her feelings!

She still believes she's an oddball for wanting to save villains. So it will be one hell of a ride to see that change!

[isearchgoood]

May 26, 2020 at 10:00PM - The Complete Python Data Science Bundle (96% discount) Ashraf

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Today’s companies collect and utilize a staggering amount of data to guide their business decisions. But, it needs to be properly cleaned and organized before it can be put to use. Enter NumPy, a core library in the Python data science stack used by data science gurus to wrangle vast amounts of multidimensional data. This course will take you through NumPy’s basic operations, universal functions, and more as you learn from hands-on examples.

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[holytheoristtastemaker]

First of all, nobody expected a lockdown. Nobody expected all the businesses will be paused or shifted to a total remote mode. And if most of the professions suffer from the lost opportunities and quarantine restrictions, developers (as they did so before for many years already) are the most used to ‘work from home’ people. And the new situation affected them not as much as others.

However, many of my friends working with aviation or travel, stayed without work. I hope, you all are ok.

I haven’t released March JS digest because of the start of the quarantine — I had a lot of work to do, and I suppose, many of you were busy with more important stuff than comparing and seeking for the best open source projects.

But now the situation is a bit better and I found some time to monitor what exactly had happened with JS repositories on GitHub in these two months, and what developers prefer for their projects when working from home.

Hence, we can grasp an overall situation and predict some trends to be followed in May. Stay safe!

Most popular JS repositories in March and April 2020

Gatsby is a free and open source framework based on React that helps developers build websites and apps. 34,978 stars by now.

marked.js is a markdown parser and compiler. Built for speed. 22,199 stars by now.

AVA is a test runner for Node.js with a concise API, detailed error output, embrace of new language features, and process isolation. 17,842 stars by now.

Immer (German for: always) is a tiny package that allows you to work with immutable state in a more convenient way. It is based on the copy-on-write mechanism. 16,266 stars by now.

Playwright is a Node library to automate Chromium, Firefox, and WebKit with a single API. Playwright is built to enable cross-browser web automation that is ever-green, capable, reliable, and fast. 11,667 stars by now.

plotly.js is an open-source JavaScript charting library behind Plotly and Dash. 11,600 stars by now.

FullCalendar is a full-sized drag & drop JavaScript event calendar. 11,168 stars by now.

Trianglify is a library that creates algorithmically generated triangle art (SVG background). 9,302 stars by now.

Nano ID is a tiny (108 bytes), secure, URL-friendly, unique string ID generator for JavaScript. 9,129 stars by now.

MDX is an authorable format that lets you seamlessly use JSX in your markdown documents. You can import components, like interactive charts or notifications, and export metadata. 8,705 stars by now.

Bull is a Redis-based queue package for handling distributed jobs and messages in NodeJS. 8,237 stars by now.

Rome is an experimental JavaScript toolchain. It includes a compiler, linter, formatter, bundler, testing framework, and more. It aims to be a comprehensive tool for anything related to the processing of JavaScript source code. 8,193 stars by now.

ioredis is a robust, performance-focused, and full-featured Redis client for Node.js. 7,505 stars by now.

Tippy.js is a tooltip, popover, dropdown, and menu solution for the web. 7,352 stars by now.

Alpine.js is a rugged, minimal framework for composing JavaScript behavior in your markup. 7,050 stars by now.

ts-node is a TypeScript execution and REPL for Node.js. 6,630 stars by now.

Rickshaw is a JavaScript toolkit for creating interactive time-series graphs, developed at Shutterstock. 6,446 stars by now.

Excalidraw is a whiteboard tool that lets you easily sketch diagrams with a hand-drawn feel. 6,115 stars by now.

fkill-cli library stands for ‘Fabulously kill processes’. Cross-platform. 6,077 stars by now.

ora is an elegant terminal spinner. 5,927 stars by now.

Prompts is a library that stands for lightweight, beautiful, and user-friendly interactive prompts. 5,800 stars by now.

query-string helps you to parse and stringify URL query strings. 4,722 stars by now.

isomorphic-git is a pure JavaScript reimplementation of git that works in both Node.js and browser JavaScript environments. It can read and write to git repositories, fetch from and push to git remotes (such as GitHub), all without any native C++ module dependencies. 4,696 stars by now.

node-notifier is a Node.js module for sending notifications on native Mac, Windows, and Linux (or Growl as fallback). 4,454 stars by now.

Backstage is an open platform for building developer portals. It unifies all your infrastructure tooling, services, and documentation with a single, consistent UI. 4,011 stars by now.

react-ga is a JavaScript module that can be used to include Google Analytics tracking code in a website or app that uses React for its frontend codebase. It does not currently use any React code internally but has been written for use with a number of Mozilla Foundation websites that are using React, as a way to standardize our GA Instrumentation across projects. 3,723 stars by now.

jExcel is a lightweight vanilla javascript plugin to create web-based interactive tables and spreadsheets compatible with Excel or any other spreadsheet software. 3,629 stars by now.

AutoCannon is an HTTP/1.1 benchmarking tool written in Node, with support for HTTP pipelining and HTTPS. 3,604 stars by now.

Dinero.js is a library for working with monetary values in JavaScript. 3,590 stars by now.

Redwood is an opinionated, full-stack, serverless web application framework that will allow you to build and deploy JAMstack applications with ease. 3,341 stars by now.

franc is s natural language detection. 3,334 stars by now.

webpack-blocks is a library that helps you by providing functional building blocks for your webpack config: easier way to configure webpack and to share configuration between projects. 2,820 stars by now.

hotkey to trigger an action on a target element when a key or sequence of keys is pressed on the keyboard. This triggers a focus event on form fields or a click event on others. 2,041 stars by now.

Serialize JavaScript to a superset of JSON that includes regular expressions and functions. 2,012 stars by now.

React Easy State is a simple React state management. 2,006 stars by now.

Qoa is a minimal interactive command-line prompts. The library utilizes a simple & minimal usage syntax and contains 7 configurable console interfaces, such as plain text, confirmation & password/secret prompts as well as single keypress, quiz & multiple-choice navigable menus. 1,931 stars by now.

kasaya is a “WYSIWYG” scripting language and runtime for browser automation. 1,808 stars by now.

match-sorter is a simple, expected, and deterministic best-match sorting of an array in JavaScript. 1,788 stars by now.

Crank.js helps you to write JSX-driven components with functions, promises, and generators. 1,622 stars by now.

Ervy brings charts to terminal. 1,481 stars by now.

iHateRegex.io is a regex cheat sheet for the haters. This project gives you a visual representation of regular expressions, embed regular expression visualization on your sites, code highlighting and validation, and more. 1,479 stars by now.

Stryker is a mutation testing for JavaScript and friends. 1,469 stars by now.

react-enroute is a simple React router with a small footprint for modern browsers. This package is not meant to be a drop-in replacement for react-router, just a smaller simpler alternative. 1,441 stars by now.

OpenChakra is a visual editor and code generator for React using Chakra UI. You can draft components with the simple drag and drop UI. 1,429 stars by now.

jest-dom stands for custom jest matchers to test the state of the DOM. 1,417 stars by now.

Notyf is a minimalistic JavaScript library for toast notifications. It’s responsive, A11Y compatible, dependency-free and tiny (~3KB). Easy integration with React, Angular, and Vue. 1,361 stars by now.

on-change allows you to watch an object or array for changes. 1,354 stars by now.

React Awesome Slider is a 60fps content transition slider that renders an animated set of production-ready UI general-purpose sliders. 1,317 stars by now.

Panolens.js is an event-driven and WebGL based panorama viewer. Lightweight and flexible. It's built on top of Three.JS. 1,254 stars by now.

Uppload is a JavaScript image uploader. It’s highly customizable with 30+ plugins, completely free and open-source, and can be used with any file uploading backend. 1,235 stars by now.

telebot is a library supporting an easy way to write Telegram bots in Node.js. 898 stars by now.

Thank you for reading!

[genexsea]

Gene Expression and Enrichment Analysis (R code)

library(edgeR) library(limma) library(Glimma) library(gplots) library(org.Mm.eg.db) library(RColorBrewer) library(RnaSeqGeneEdgeRQL) library(statmod) library(sva) library(bladderbatch) library(pamr) library(limma) library(ggplot2) library(gridExtra) library(plotly) library(tidyverse) library(gridExtra) library(grid) library(png) library(downloader) library(grDevices) library(cowplot) library(ggplot2)

library(snpStats) getwd()

#Load the Sample information, Batches and the conditions. This will correspond to the Phenotype data. #The variables are stored in the phenotype data slot and can be obtained as follows:

sampleinfo <- read.delim("Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/targets_red_blue.txt",stringsAsFactors = default.stringsAsFactors(),row.names = "samples")

#Load the expression Data / Reading in count-data #The expression data can be obtained from count matrix as follows: seqdata_all <- read.delim("Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/FP_TR_red_blue.txt",       ��                  row.names="ORF19_ID",stringsAsFactors = FALSE)

#seqdata_all <- seqdata_all[,c(1:36)]

#Removing the 'Gene Length' column from the Expression dataset

seqdata_all <- seqdata_all[,-c(1)]

#Check if the row names of 'sampleinfo' is the same as the column names of 'seqdata' stopifnot(all(rownames(sampleinfo) == colnames(seqdata_all)))

group_isolate <- factor(sampleinfo$isolate, levels = unique(sampleinfo$isolate)) group_isolate

group_type <- factor(sampleinfo$culture_type, levels = unique(sampleinfo$culture_type)) group_type

group_plate <- factor(sampleinfo$plate_info, levels = unique(sampleinfo$plate_info)) group_plate

group_flowcell <- factor(sampleinfo$Flowcell, levels = unique(sampleinfo$Flowcell)) group_flowcell

#Converting the read data (Expression data) into a DGEList-object using the DGEList function.

x <- DGEList(seqdata_all) class(x) dim(x) x colnames(x)

# Updating the group and batch information to the samples in DGElist x$samples$group_isolate <- group_isolate x$samples$group_type <- group_type x$samples$group_plate <- group_plate x$samples$group_flowcell <- group_flowcell

x

################################################################################################

#####################################Adding annotations to the DGElist############################### # load the file Containing the Gene names and Gene Ids of all the annotated Candida genes

IDs <- read.delim("Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/C_albicans_genes_allids_desc.txt", row.names="ORF19_ID") #Merging the ID dataframe to DGElist dataframe containing the gene details x$genes <- merge(x$genes, IDs, by="row.names", all=T)

#Convert the values in a "orf19" column into row names in DGElist existing data frame containing gene information

samp2 <- x$genes[,-1]

x$genes <- samp2

head(x$genes) dim(x)

###########################################################################################

#################################Pre-processing the data################

#Transforming the raw read counts to counts per million (cpm) #This transformation accounts for the differences in the library size #CPM and log-CPM transformations do not account for gene length differences as RPKM and FPKM values do #raw counts are converted to CPM and log-CPM values using the cpm function in edgeR.

cpm_x <- cpm(x) lcpm_x <- cpm(x, log=TRUE, prior.count=2) head(cpm_x)[,1] head(lcpm_x)[,1] dim(lcpm_x)

L_x <- mean(x$samples$lib.size) * 1e-6 M_x <- median(x$samples$lib.size) * 1e-6 c(L_x, M_x)

# minimum log-CPM value for each sample becomes log2(2/3.923991) = -3.18 summary(lcpm_x)

#######################################################################

######################Removing lowly expressed Candida genes##################################

table(rowSums(x$counts==0)==20)

#############################################################################################

#########################Filtering low-expressed genes######################################

#The **filterByExpr** function in the edgeR package provides an automatic way to filter genes, while keeping as many genes as possible with worthwhile counts. keep.exprs_x <- filterByExpr(x, group=group_isolate) x <- x[keep.exprs_x,, keep.lib.sizes=FALSE] dim(x)

#Plotting the distribution log-CPM values shows that a sizeable proportion of genes within each sample are either unexpressed or lowly-expressed with log-CPM values that are small or negative lcpm.cutoff_x <- log2(10/M_x + 2/L_x) library(RColorBrewer) nsamples_x <- ncol(x) col_x <- brewer.pal(nsamples_x, "Paired") par(mfrow=c(1,2)) plot(density(lcpm_x[,1]), col=col_x[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="") title(main="A. Raw data", xlab="Log-cpm") abline(v=lcpm.cutoff_x, lty=) for (i in 2:nsamples_x){  den <- density(lcpm_x[,i])  lines(den$x, den$y, col=col_x[i], lwd=2) } legend("topright", colnames(x), text.col=col_x, bty="n", cex = 0.3, ncol=2)

#The density of log-CPM values for post-filtered data are shown for each sample

lcpm_x <- cpm(x, log=TRUE) head(lcpm_x)[,1] plot(density(lcpm_x[,1]), col=col_x[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="") title(main="B. Filtered data", xlab="Log-cpm") abline(v=lcpm.cutoff_x, lty=3) for (i in 2:nsamples_x){  den <- density(lcpm_x[,i])  lines(den$x, den$y, col=col_x[i], lwd=2) } legend("topright", colnames(x), text.col=col_x, bty="n", cex = 0.3, ncol=2)

##################################################################################################

######################Qulaity control steps##################################################

#### Determining the sample library sizes #First, we can check how many reads we have for each sample in **x**(DGElist-object) x$samples$lib.size

#We can also plot the library sizes as a barplot to see whether there are any major discrepancies between the samples more easily. # The names argument tells the barplot to use the sample names on the x-axis # The las argument rotates the axis names

par(mfrow=c(1,1)) barplot(x$samples$lib.size,names=colnames(x),las=2, cex.axis = 0.8,cex.names = 0.3) # Add a title to the plot title("Barplot of library sizes")

#### Normalising gene expression distributions for compositional Bias x <- calcNormFactors(x, method = "TMM") x$samples$norm.factors x

#####################Observing the effects of normalization##########################

# x2 <- x # x2$samples$norm.factors <- 1 # x2$counts[,1] <- ceiling(x2$counts[,1]*0.05) # x2$counts[,2] <- x2$counts[,2]*5 # # lcpm_x <- cpm(x2, log=TRUE,prior.count=2) # head(lcpm_x)[,1] # par(mfrow=c(1,2)) # boxplot(lcpm_x, las=2, col=col_x, main="") # title(main="A. Example: Unnormalised data",ylab="Log-cpm") # x2 <- calcNormFactors(x2)   # x2$samples$norm.factors # # lcpm_x <- cpm(x2, log=TRUE,prior.count=2) # head(lcpm_x)[,1] # boxplot(lcpm_x, las=2, col=col_x, main="") # title(main="B. Example: Normalised data",ylab="Log-cpm")

lcpm_x <- cpm(x, log=TRUE, prior.count = 2) head(lcpm_x)[,1]

############Adjusting for the batch effects###########################33

#Simple way

mod = model.matrix(~as.factor(group_type) + as.factor(group_flowcell)) fit = lm.fit(mod, t(lcpm_x)) hist(fit$coefficients[2,], col=2,breaks=100)

table(group_isolate, group_flowcell)

# using combat

batch = group_flowcell modcombat = model.matrix(~1, data=sampleinfo) modtype = model.matrix(~0+group_type) combat_lcpm = ComBat(dat=lcpm_x, batch = batch, mod=modcombat,par.prior = T, prior.plots = FALSE) combat_fit = lm.fit(modtype, t(combat_lcpm)) hist(combat_fit$coefficients[2,], col=2, breaks = 100)

#plotting coefficients from the original liner model vs Combat fit

plot(fit$coefficients[2,],combat_fit$coefficients[2,], col=2, xlab="Linear Model",     ylab="Combat", xlim=c(-5,5), ylim=c(-5,5))

abline(c(0,1),col=1, lwd=3)

par(mfrow=c(1,1))

#Using SVA package to infer the batch effects

mod=model.matrix(~0 + group_type, data=sampleinfo) # Build model matrix with the outcome you care about mod0=model.matrix(~1, data=sampleinfo)  # Null model n.sv = num.sv(lcpm_x,mod)

sva1=sva(lcpm_x,mod,mod0) names(sva1) dim(sva1$sv)

#correlating batch effects with the observed with actual observed batch variable

summary(lm(sva1$sv ~ sampleinfo$Flowcell)) boxplot(sva1$sv[,4] ~ sampleinfo$Flowcell)

points(sva1$sv[,4] ~ jitter(as.numeric(sampleinfo$Flowcell)), col=as.numeric(sampleinfo$Flowcell)) modsv = cbind(mod,sva1$sv)

fitsv = lm.fit(modsv, t(lcpm_x))

# Comparing SVA and Combat par(mfrow=c(1,2))

plot(fitsv$coefficients[2,],combat_fit$coefficients[2,],col=2,     xlab="SVA", ylab="Combat", xlim=c(-5,5), ylim=c(-5,5)) abline(c(0,1), col=1, lwd=3)

# Comparing SVA and liner adjusted model

plot(fitsv$coefficients[2,],fit$coefficients[2,],col=2,     xlab="SVA", ylab="linear model", xlim=c(-5,5), ylim=c(-5,5)) abline(c(0,1), col=1, lwd=3)

cleaningP = function(x, mod, sva1,  P=ncol(mod)) {  X=cbind(mod,sva1$sv)  Hat=solve(t(X)%*%X)%*%t(X)  beta=(Hat%*%t(x))  cleany=x-t(as.matrix(X[,-c(1:P)])%*%beta[-c(1:P),])  return(cleany) }

sva_lcpm = cleaningP(lcpm_x, mod, sva1) sva_lcpm

#write.csv(sva_lcpm, "Desktop/Abhilash/study/Post-Doc/Lab_notebook/eQTL/Input_3/sva_lcpm.csv")

################Checking for the variation between sample with PCA plot

par(mfrow=c(1,2))

pca_lcpm_x = prcomp(t(lcpm_x), scale=FALSE) plot(pca_lcpm_x$x[,c(1,2)],col=sampleinfo$isolate,pch=16) text(pca_lcpm_x$x[,c(1,2)],labels=sampleinfo$isolate,pos=3,cex=0.5) title("PC1 vs PC2- No correction for batch effect")

pca_combat_lcpm = prcomp(t(combat_lcpm), scale=FALSE) plot(pca_combat_lcpm$x[,c(1,2)],col=sampleinfo$isolate,pch=16) text(pca_combat_lcpm$x[,c(1,2)],labels=sampleinfo$isolate,pos=3,cex=0.5) title("PC1 vs PC2- Correction with Combat")

pca_sva_lcpm = prcomp(t(sva_lcpm), scale=FALSE) plot(pca_sva_lcpm$x[,c(1,2)],col=sampleinfo$isolate,pch=16) text(pca_sva_lcpm$x[,c(1,2)],labels=sampleinfo$isolate,pos=3,cex=0.5) title("PC1 vs PC2 - Correction with SVA")

par(mfrow=c(1,2))

hist(lcpm_x[,2], col=2, breaks = 100)

hist(sva_lcpm[,2], col=2, breaks = 100)

plot(density(lcpm_x[,2]))

lines(density(sva_lcpm[,2]),col="green")

mod_type=model.matrix(~0+group_type) v <- voom(x, mod_type, plot=TRUE) v col.group <- group_type nb.cols <- 2 levels(col.group) <-  colorRampPalette(brewer.pal(nlevels(col.group), "Set1"))(nb.cols) col.group <- as.character(col.group) #pch <- c(0,0,1,1,2,2,3,3,3,3,3,3,4,4,5,5,6,6,7,7,8,8,8,8,9,9,10,10,11,11,11,11,11,11,11)

plotMDS(v$E, labels=group_type, col=col.group, cex = 0.5) #plotMDS(v$E, col=col.group, pch=pch, cex = 0.4)

col.group <- group_type nb.cols <- 2 levels(col.group) <-  colorRampPalette(brewer.pal(nlevels(col.group), "Set1"))(nb.cols) col.group <- as.character(col.group)

plotMDS(v$E, labels=group_type, col=col.group, cex = 0.5)

#Using SVA package to infer the batch effects

mod=model.matrix(~0+group_type) # Build model matrix with the outcome you care about mod0=model.matrix(~1, data=sampleinfo)  # Null model n.sv = num.sv(v$E,mod)

sva1=sva(v$E,mod,mod0) names(sva1) dim(sva1$sv)

#correlating batch effects with the observed with actual observed batch variable

summary(lm(sva1$sv ~ sampleinfo$Flowcell)) boxplot(sva1$sv[,1] ~ sampleinfo$Flowcell)

points(sva1$sv[,1] ~ jitter(as.numeric(sampleinfo$Flowcell)), col=as.numeric(sampleinfo$Flowcell)) modsv = cbind(mod,sva1$sv)

fitsv = lm.fit(modsv, t(v$E))

cleaningP = function(x, mod, sva1,  P=ncol(mod)) {  X=cbind(mod,sva1$sv)  Hat=solve(t(X)%*%X)%*%t(X)  beta=(Hat%*%t(x))  cleany=x-t(as.matrix(X[,-c(1:P)])%*%beta[-c(1:P),])  return(cleany) }

counts_sva = cleaningP(v$E, mod, sva1) counts_sva

#combat_v_E = ComBat(dat=v$E, batch = batch, mod=modcombat,par.prior = T, prior.plots = FALSE) #combat_fit = lm.fit(modisolate, t(combat_lcpm))

################Checking for the variation between sample with PCA plot

par(mfrow=c(1,2))

pca_V_E = prcomp(t(v$E), scale=FALSE) plot(pca_V_E$x[,c(1,2)],col=sampleinfo$isolate,pch=16) text(pca_V_E$x[,c(1,2)],labels=sampleinfo$isolate,pos=3,cex=0.5) title("PC1 vs PC2- No correction for batch effect")

pca_counts_sva = prcomp(t(counts_sva), scale=FALSE) plot(pca_counts_sva$x[,c(1,2)],col=sampleinfo$isolate,pch=16) text(pca_counts_sva$x[,c(1,2)],labels=sampleinfo$isolate,pos=3,cex=0.5) title("PC1 vs PC2 - Correction with SVA")

# pca_combat_v_E = prcomp(t(combat_v_E), scale=FALSE) # plot(pca_combat_v_E$x[,c(1,2)],col=sampleinfo$isolate,pch=16) # text(pca_combat_v_E$x[,c(1,2)],labels=sampleinfo$isolate,pos=3,cex=0.5) # title("PC1 vs PC2 - Correction with Combat")

par(mfrow=c(1,2))

plot(density(lcpm_x[,1]), col=col_x[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="") title(main="A. Notcorrected for Batch effect", xlab="Log-cpm") abline(v=lcpm.cutoff_x, lty=) for (i in 2:nsamples_x){  den <- density(lcpm_x[,i])  lines(den$x, den$y, col=col_x[i], lwd=2) }

legend("topright", colnames(x), text.col=col_x, bty="n", cex = 0.3, ncol=2)

plot(density(v$E[,1]), col=col_x[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="") title(main="B. Batch_corrected", xlab="Voom-transformed(Log-cpm)") abline(v=lcpm.cutoff_x, lty=) for (i in 2:nsamples_x){  den <- density(v$E[,i])  lines(den$x, den$y, col=col_x[i], lwd=2) }

legend("topright", colnames(x), text.col=col_x, bty="n", cex = 0.3, ncol=2)

plot(density(counts_sva[,1]), col=col_x[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="") title(main="B. Batch_corrected", xlab="Voom-transformed(Log-cpm)") abline(v=lcpm.cutoff_x, lty=) for (i in 2:nsamples_x){  den <- density(counts_sva[,i])  lines(den$x, den$y, col=col_x[i], lwd=2) }

legend("topright", colnames(x), text.col=col_x, bty="n", cex = 0.3, ncol=2)

colnames(mod_type) <- gsub("group_type", "", colnames(mod_type)) mod_type

#Creating contrast matrix contr.matrix_type <- makeContrasts(  MonoculturevsCoculture =Monoculture-Coculture,  levels = colnames(mod_type)) contr.matrix_type

#Replacing the corrected expression values to the voom objects

v[["E"]] <- counts_sva

vfit_nosv <- lmFit(v, mod_type) vfit_nosv <- contrasts.fit(vfit_nosv, contrasts=contr.matrix_type) efit_nosv <- eBayes(vfit_nosv) plotSA(efit_nosv, main="Model after batch effect removal: Mean-variance trend") de_nosv <- decideTests(efit_nosv,lfc = 0.3, genewise.p.value = 0.05) summary(decideTests(efit_nosv))

tfit_nosv <- treat(vfit_nosv, lfc=1) dt_nosv <- decideTests(tfit_nosv) summary(dt_nosv)

FPvsTR <- topTable(efit_nosv, coef=1, number=nrow(v$E))

write.csv(FPvsTR, "Desktop/Abhilash/study/Post-Doc/Presentations/TIM/redvsblue/results/FPvsTR.csv")

glMDPlot(efit_nosv, coef=1, status=de_nosv, main=colnames(efit_nosv)[1],         side.main="Genename", counts=v$E, groups=group_type, launch=TRUE,ylim=c(--10,13))

library(edgeR) library(limma) library(Glimma) library(gplots) library(org.Mm.eg.db) library(RColorBrewer) library(RnaSeqGeneEdgeRQL) library(statmod) library(sva) library(bladderbatch) library(pamr) library(limma) library(ggplot2) library(gridExtra) library(plotly) library(tidyverse) library(gridExtra) library(grid) library(png) library(downloader) library(grDevices) library(cowplot) library(ggplot2) library(DOSE)

library(AnnotationHub) hub <- AnnotationHub()

query(hub, "Candida albicans") Candida_alb <- hub[["AH73663"]] Candida_alb

library(clusterProfiler) FPvsTR_enrich_sig_enrich = read.csv("Desktop/Abhilash/study/Post-Doc/Presentations/TIM/redvsblue/results/FPvsTR.csv") FPvsTR_enrich_sig <- FPvsTR_enrich_sig_enrich %>% filter(FPvsTR_enrich_sig_enrich$adj.P.Val < 0.05) FPvsTR_enrich_sig_down <- FPvsTR_enrich_sig %>% filter(FPvsTR_enrich_sig$logFC < -0.5) dim(FPvsTR_enrich_sig_down)

#Top_upregulated #FPvsTR_significant_Upregulated (FDR<0.05 and Lfc > 0.5) FPvsTR_enrich_sig_up <- FPvsTR_enrich_sig %>% filter(FPvsTR_enrich_sig$logFC > 0.5) dim(FPvsTR_enrich_sig_up)

#Significant_downregulated #FPvsTR_Downregulated_all (FDR<0.05 and Lfc <0) FPvsTR_enrich_sig_down_all <- FPvsTR_enrich_sig %>% filter(FPvsTR_enrich_sig$logFC < 0) dim(FPvsTR_enrich_sig_down_all)

#Significant_upregulated #FPvsTR_Upregulated_all (FDR<0.05 and Lfc > 0) FPvsTR_enrich_sig_up_all <- FPvsTR_enrich_sig %>% filter(FPvsTR_enrich_sig$logFC > 0) dim(FPvsTR_enrich_sig_up_all)

####code for producing the genelists

#Creating genelists for different comaparisons #1. FPvsTR geneList_FPvsTR_enrich_sig_logFC <- FPvsTR_enrich_sig$logFC names(geneList_FPvsTR_enrich_sig_logFC) <- FPvsTR_enrich_sig$EntrezID length(geneList_FPvsTR_enrich_sig_logFC)

### Gene Set enrichment analysis

#all terms together #CEC3609_3hrsvsCEC3609_6hrs #all significant (FDR < 0.05) gse_FPvsTR_enrich_sig_all <- gseGO(geneList=sort(geneList_FPvsTR_enrich_sig_logFC,decreasing = T),ont = "ALL", OrgDb=Candida_alb, verbose=F,pvalueCutoff = 1, nPerm = 10000) head(summary(gse_FPvsTR_enrich_sig_all)) write.csv(gse_FPvsTR_enrich_sig_all@result,file = "Desktop/Abhilash/study/Post-Doc/Presentations/TIM/redvsblue/results/gse_FPvsTR_enrich_sig_all.csv")

#significant Biological process (FDR < 0.05)

gse_FPvsTR_enrich_sig_BP <- gseGO(geneList=sort(geneList_FPvsTR_enrich_sig_logFC,decreasing = T),ont = "BP", OrgDb=Candida_alb, verbose=F,pvalueCutoff = 1, nPerm = 10000) head(summary(gse_FPvsTR_enrich_sig_BP)) write.csv(gse_FPvsTR_enrich_sig_BP@result,file = "Desktop/Abhilash/study/Post-Doc/Presentations/TIM/redvsblue/results/gse_FPvsTR_enrich_sig_BP.csv")

gse_FPvsTR_enrich_sig_MF <- gseGO(geneList=sort(geneList_FPvsTR_enrich_sig_logFC,decreasing = T),ont = "MF", OrgDb=Candida_alb, verbose=F,pvalueCutoff = 1, nPerm = 10000) head(summary(gse_FPvsTR_enrich_sig_MF)) write.csv(gse_FPvsTR_enrich_sig_MF@result,file = "Desktop/Abhilash/study/Post-Doc/Presentations/TIM/redvsblue/results/gse_FPvsTR_enrich_sig_MF.csv")

gse_FPvsTR_enrich_sig_CC <- gseGO(geneList=sort(geneList_FPvsTR_enrich_sig_logFC,decreasing = T),ont = "CC", OrgDb=Candida_alb, verbose=F,pvalueCutoff = 1, nPerm = 10000) head(summary(gse_FPvsTR_enrich_sig_CC)) write.csv(gse_FPvsTR_enrich_sig_CC@result,file = "Desktop/Abhilash/study/Post-Doc/Presentations/TIM/redvsblue/results/gse_FPvsTR_enrich_sig_CC.csv")

### Visualization of Enrichment results

# ridgeplot will visualize expression distributions of core enriched genes for GSEA enriched categories. It helps users to interpret up/down-regulated pathways.

#FPvsTR #all significant (FDR < 0.05) #all GO ridgeplot(gse_FPvsTR_enrich_sig_all, fill = "p.adjust", showCategory = 10) #BP ridgeplot(gse_FPvsTR_enrich_sig_BP, fill = "p.adjust", showCategory = 10) #MF ridgeplot(gse_FPvsTR_enrich_sig_MF, fill = "p.adjust", showCategory = 10) #CC ridgeplot(gse_FPvsTR_enrich_sig_CC, fill = "p.adjust", showCategory = 10)

### GESEAplot

BiocManager::install("enrichplot") library(enrichplot)

#all significant (FDR < 0.05) gseaplot2(gse_FPvsTR_enrich_sig_all, geneSetID = 1:3, pvalue_table = TRUE, color = c("#E495A5", "#86B875", "#7DB0DD"), ES_geom = "dot") #BP gseaplot2(gse_FPvsTR_enrich_sig_BP, geneSetID = 1:3, pvalue_table = TRUE, color = c("#E495A5", "#86B875", "#7DB0DD"), ES_geom = "dot") #MF gseaplot2(gse_FPvsTR_enrich_sig_MF, geneSetID = 1:3, pvalue_table = TRUE, color = c("#E495A5", "#86B875", "#7DB0DD"), ES_geom = "dot") #CC gseaplot2(gse_FPvsTR_enrich_sig_CC, geneSetID = 1:3, pvalue_table = TRUE, color = c("#E495A5", "#86B875", "#7DB0DD"), ES_geom = "dot")

library(ggplot2) library(cowplot)

#CEC3609_3hrsvsCEC3609_6hrs pp <- lapply(1:20, function(i) {  anno <- gse_FPvsTR_enrich_sig_all[i, c("NES", "pvalue", "p.adjust")]  lab <- paste0(names(anno), "=",  round(anno, 3), collapse="\n")

 gsearank(gse_FPvsTR_enrich_sig_all, i, gse_FPvsTR_enrich_sig_all[i, 2]) + xlab(NULL) +ylab(NULL) +    annotate("text", 0, gse_FPvsTR_enrich_sig_all[i, "enrichmentScore"] * .9, label = lab, hjust=0, vjust=0) }) plot_grid(plotlist=pp, ncol=4, nrow = 5) #BP gseaplot2(gse_FPvsTR_enrich_sig_BP, geneSetID = c("GO:0007155","GO:0030447","GO:0006811","GO:0006696"), pvalue_table = T, color = c("#E495A5", "#86B875", "#7DB0DD"), ES_geom = "dot")

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January 25, 2020 at 10:00PM - The Complete Python Data Science Bundle (96% discount) Ashraf

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It’s no secret that data scientists stand to make a pretty penny in today’s data-driven world; but if you’re keen on becoming one, you’ll need to master the appropriate tools. Pandas is one of the most popular of the Python data science libraries for working with mounds of data. By expressing data in a tabular format, Pandas makes it easy to perform data cleaning, aggregations and other analyses. Built around hands-on demos, this course will walk you through using Pandas and what it can do as you take on series, data frames, importing/exporting data, and more.

Access 23 lectures & 2.5 hours of content 24/7

Explore Panda’s built-in functions for common data manipulation techniques

Learn how to work with data frames & manage data

Deepen your understanding w/ example-driven lessons

Today’s companies collect and utilize a staggering amount of data to guide their business decisions. But, it needs to be properly cleaned and organized before it can be put to use. Enter NumPy, a core library in the Python data science stack used by data science gurus to wrangle vast amounts of multidimensional data. This course will take you through NumPy’s basic operations, universal functions, and more as you learn from hands-on examples.

Access 27 lectures & 2.5 hours of content 24/7

Familiarize yourself w/ NumPy’s basic operations & universal functions

Learn how to properly manage data w/ hands-on examples

Validate your training w/ a certificate of completion

From tech to medicine and finance, data plays a pivotal role in guiding today’s businesses. But, it needs to be properly broken down and visualized before you can get any sort of actionable insights. That’s where Seaborn comes into play. Designed for enhanced data visualization, this Python-based library helps bridge the gap between vast swathes of data and the valuable insights they contain. This course acts as your Seaborne guide, walking you through what it can do and how you can use it to display information, find relationships, and much more.

Access 16 lectures & 1.5 hours of content 24/7

Familiarize yourself w/ Seaborn via hands-on examples

Discover Seaborn’s enhanced data visualization capabilities

Explore histograms, linear relationships & more visualization concepts

Before a data scientist can properly analyze their data, they must first visualize it and understand any relationships that might exist in the information. To this end, many data professionals use Matplotlib, an industry-favorite Python library for visualizing data. Highly customizable and packed with powerful features for building graphs and plots, Matplotlib is an essential tool for any aspiring data scientist, and this course will show you how it ticks.

Access 30 lectures & 3 hours of content 24/7

Explore the anatomy of a Matplotlib figure & its customizable parts

Dive into figures, axes, subplots & more components

Learn how to draw statistical insights from data

Understand different ways of conveying statistical information

One of the most popular data analytics engines out there, Spark has become a staple in many a data scientist’s toolbox; and the latest version, Spark 2.x, brings more efficient and intuitive features to the table. Jump into this comprehensive course, and you’ll learn how to better analyze mounds of data, extract valuable insights, and more with Spark 2.x. Plus, this course comes loaded with hands-on examples to refine your knowledge, as you analyze data from restaurants listed on Zomato and churn through historical data from the Olympics and the FIFA world cup!

Access 27 lectures & 3 hours of content 24/7

Explore what Spark 2.x can do via hands-on projects

Learn how to analyze data at scale & extract insights w/ Spark transformations and actions

Deepen your understanding of data frames & Resilient Distributed Datasets

You don’t need to be a programming prodigy to get started in data science. Easy to use and highly accessible, Plotly is library in Python that lets you create complex plots and graphs with minimal programming know-how. From creating basic charts to adding motion to your visualizations, this course will walk you through the Plotly essentials with hands-on examples that you can follow.

Access 27 lectures & 2 hours of content 24/7

Learn how to build line charts, bar charts, histograms, pie charts & other basic visualizations

Explore visualizing data in more than two dimensions

Discover how to add motion to your graphs

Work w/ plots on your local machine or share them via the Plotly Cloud

In addition to handling vast amounts of batch data, Spark has extremely powerful support for continuous applications, or those with streaming data that is constantly updated and changes in real-time. Using the new and improved Spark 2.x, this course offers a deep dive into stream architectures and analyzing continuous data. You’ll also follow along a number of real-world examples, like analyzing data from restaurants listed on Zomato and real-time Twitter data.

Access 36 lectures & 2.5 hours of content 24/7

Familiarize yourself w/ Spark 2.x & its support for continuous applications

Learn how to analyze data from real-world streams

Analyze data from restaurants listed on Zomato & real-time Twitter data

More companies are using the power of deep learning and neural networks to create advanced AI that learns on its own. From speech recognition software to recommendation systems, deep learning frameworks, like PyTorch, make creating these products easier. Jump in, and you’ll get up to speed with PyTorch and its capabilities as you analyze a host of real-world datasets and build your own machine learning models.

Access 41 lectures & 3.5 hours of content 24/7

Understand neurons & neural networks and how they factor into machine learning

Explore the basic steps involved in training a neural network

Familiarize yourself w/ PyTorch & Python 3

Analyze air quality data, salary data & more real-world datasets

Fast, scalable, and packed with an intuitive API for machine learning, Apache MXNet is a deep learning framework that makes it easy to build machine learning applications that learn quickly and can run on a variety of devices. This course walks you through the Apache MXNet essentials so you can start creating your own neural networks, the building blocks that allow AI to learn on their own.

Access 31 lectures & 2 hours of content 24/7

Explore neurons & neural networks and how they factor into machine learning

Walk through the basic steps of training a neural network

Dive into building neural networks for classifying images & voices

Refine your training w/ real-world examples & datasets

Python is a general-purpose programming language which can be used to solve a wide variety of problems, be they in data analysis, machine learning, or web development. This course lays a foundation to start using Python, which considered one of the best first programming languages to learn. Even if you’ve never even thought about coding, this course will serve as your diving board to jump right in.

Access 28 lectures & 3 hours of content 24/7

Gain a fundamental understanding of Python loops, data structures, functions, classes, & more

Learn how to solve basic programming tasks

Apply your skills confidently to solve real problems

Classification models play a key role in helping computers accurately predict outcomes, like when a banking program identifies loan applicants as low, medium, or high credit risks. This course offers an overview of machine learning with a focus on implementing classification models via Python’s scikit-learn. If you’re an aspiring developer or data scientist looking to take your machine learning knowledge further, this course is for you.

Access 17 lectures & 2 hours of content 24/7

Tackle basic machine learning concepts, including supervised & unsupervised learning, regression, and classification

Learn about support vector machines, decision trees & random forests using real data sets

Discover how to use decision trees to get better results

Deep learning isn’t just about helping computers learn from data—it’s about helping those machines determine what’s important in those datasets. This is what allows for Tesla’s Model S to drive on its own and for Siri to determine where the best brunch spots are. Using the machine learning workhorse that is TensorFlow, this course will show you how to build deep learning models and explore advanced AI capabilities with neural networks.

Access 62 lectures & 8.5 hours of content 24/7

Understand the anatomy of a TensorFlow program & basic constructs such as graphs, tensors, and constants

Create regression models w/ TensorFlow

Learn how to streamline building & evaluating models w/ TensorFlow’s estimator API

Use deep neural networks to build classification & regression models

from Active Sales – SharewareOnSale https://ift.tt/2OYNcYd https://ift.tt/eA8V8J via Blogger https://ift.tt/37trlCb #blogger #bloggingtips #bloggerlife #bloggersgetsocial #ontheblog #writersofinstagram #writingprompt #instapoetry #writerscommunity #writersofig #writersblock #writerlife #writtenword #instawriters #spilledink #wordgasm #creativewriting #poetsofinstagram #blackoutpoetry #poetsofig

[isearchgoood]

December 25, 2019 at 10:00PM - The Complete Python Data Science Bundle (96% discount) Ashraf

The Complete Python Data Science Bundle (96% discount) Hurry Offer Only Last For HoursSometime. Don't ever forget to share this post on Your Social media to be the first to tell your firends. This is not a fake stuff its real.

It’s no secret that data scientists stand to make a pretty penny in today’s data-driven world; but if you’re keen on becoming one, you’ll need to master the appropriate tools. Pandas is one of the most popular of the Python data science libraries for working with mounds of data. By expressing data in a tabular format, Pandas makes it easy to perform data cleaning, aggregations and other analyses. Built around hands-on demos, this course will walk you through using Pandas and what it can do as you take on series, data frames, importing/exporting data, and more.

Access 23 lectures & 2.5 hours of content 24/7

Explore Panda’s built-in functions for common data manipulation techniques

Learn how to work with data frames & manage data

Deepen your understanding w/ example-driven lessons

Today’s companies collect and utilize a staggering amount of data to guide their business decisions. But, it needs to be properly cleaned and organized before it can be put to use. Enter NumPy, a core library in the Python data science stack used by data science gurus to wrangle vast amounts of multidimensional data. This course will take you through NumPy’s basic operations, universal functions, and more as you learn from hands-on examples.

Access 27 lectures & 2.5 hours of content 24/7

Familiarize yourself w/ NumPy’s basic operations & universal functions

Learn how to properly manage data w/ hands-on examples

Validate your training w/ a certificate of completion

From tech to medicine and finance, data plays a pivotal role in guiding today’s businesses. But, it needs to be properly broken down and visualized before you can get any sort of actionable insights. That’s where Seaborn comes into play. Designed for enhanced data visualization, this Python-based library helps bridge the gap between vast swathes of data and the valuable insights they contain. This course acts as your Seaborne guide, walking you through what it can do and how you can use it to display information, find relationships, and much more.

Access 16 lectures & 1.5 hours of content 24/7

Familiarize yourself w/ Seaborn via hands-on examples

Discover Seaborn’s enhanced data visualization capabilities

Explore histograms, linear relationships & more visualization concepts

Before a data scientist can properly analyze their data, they must first visualize it and understand any relationships that might exist in the information. To this end, many data professionals use Matplotlib, an industry-favorite Python library for visualizing data. Highly customizable and packed with powerful features for building graphs and plots, Matplotlib is an essential tool for any aspiring data scientist, and this course will show you how it ticks.

Access 30 lectures & 3 hours of content 24/7

Explore the anatomy of a Matplotlib figure & its customizable parts

Dive into figures, axes, subplots & more components

Learn how to draw statistical insights from data

Understand different ways of conveying statistical information

One of the most popular data analytics engines out there, Spark has become a staple in many a data scientist’s toolbox; and the latest version, Spark 2.x, brings more efficient and intuitive features to the table. Jump into this comprehensive course, and you’ll learn how to better analyze mounds of data, extract valuable insights, and more with Spark 2.x. Plus, this course comes loaded with hands-on examples to refine your knowledge, as you analyze data from restaurants listed on Zomato and churn through historical data from the Olympics and the FIFA world cup!

Access 27 lectures & 3 hours of content 24/7

Explore what Spark 2.x can do via hands-on projects

Learn how to analyze data at scale & extract insights w/ Spark transformations and actions

Deepen your understanding of data frames & Resilient Distributed Datasets

You don’t need to be a programming prodigy to get started in data science. Easy to use and highly accessible, Plotly is library in Python that lets you create complex plots and graphs with minimal programming know-how. From creating basic charts to adding motion to your visualizations, this course will walk you through the Plotly essentials with hands-on examples that you can follow.

Access 27 lectures & 2 hours of content 24/7

Learn how to build line charts, bar charts, histograms, pie charts & other basic visualizations

Explore visualizing data in more than two dimensions

Discover how to add motion to your graphs

Work w/ plots on your local machine or share them via the Plotly Cloud

In addition to handling vast amounts of batch data, Spark has extremely powerful support for continuous applications, or those with streaming data that is constantly updated and changes in real-time. Using the new and improved Spark 2.x, this course offers a deep dive into stream architectures and analyzing continuous data. You’ll also follow along a number of real-world examples, like analyzing data from restaurants listed on Zomato and real-time Twitter data.

Access 36 lectures & 2.5 hours of content 24/7

Familiarize yourself w/ Spark 2.x & its support for continuous applications

Learn how to analyze data from real-world streams

Analyze data from restaurants listed on Zomato & real-time Twitter data

More companies are using the power of deep learning and neural networks to create advanced AI that learns on its own. From speech recognition software to recommendation systems, deep learning frameworks, like PyTorch, make creating these products easier. Jump in, and you’ll get up to speed with PyTorch and its capabilities as you analyze a host of real-world datasets and build your own machine learning models.

Access 41 lectures & 3.5 hours of content 24/7

Understand neurons & neural networks and how they factor into machine learning

Explore the basic steps involved in training a neural network

Familiarize yourself w/ PyTorch & Python 3

Analyze air quality data, salary data & more real-world datasets

Fast, scalable, and packed with an intuitive API for machine learning, Apache MXNet is a deep learning framework that makes it easy to build machine learning applications that learn quickly and can run on a variety of devices. This course walks you through the Apache MXNet essentials so you can start creating your own neural networks, the building blocks that allow AI to learn on their own.

Access 31 lectures & 2 hours of content 24/7

Explore neurons & neural networks and how they factor into machine learning

Walk through the basic steps of training a neural network

Dive into building neural networks for classifying images & voices

Refine your training w/ real-world examples & datasets

Python is a general-purpose programming language which can be used to solve a wide variety of problems, be they in data analysis, machine learning, or web development. This course lays a foundation to start using Python, which considered one of the best first programming languages to learn. Even if you’ve never even thought about coding, this course will serve as your diving board to jump right in.

Access 28 lectures & 3 hours of content 24/7

Gain a fundamental understanding of Python loops, data structures, functions, classes, & more

Learn how to solve basic programming tasks

Apply your skills confidently to solve real problems

Classification models play a key role in helping computers accurately predict outcomes, like when a banking program identifies loan applicants as low, medium, or high credit risks. This course offers an overview of machine learning with a focus on implementing classification models via Python’s scikit-learn. If you’re an aspiring developer or data scientist looking to take your machine learning knowledge further, this course is for you.

Access 17 lectures & 2 hours of content 24/7

Tackle basic machine learning concepts, including supervised & unsupervised learning, regression, and classification

Learn about support vector machines, decision trees & random forests using real data sets

Discover how to use decision trees to get better results

Deep learning isn’t just about helping computers learn from data—it’s about helping those machines determine what’s important in those datasets. This is what allows for Tesla’s Model S to drive on its own and for Siri to determine where the best brunch spots are. Using the machine learning workhorse that is TensorFlow, this course will show you how to build deep learning models and explore advanced AI capabilities with neural networks.

Access 62 lectures & 8.5 hours of content 24/7

Understand the anatomy of a TensorFlow program & basic constructs such as graphs, tensors, and constants

Create regression models w/ TensorFlow

Learn how to streamline building & evaluating models w/ TensorFlow’s estimator API

Use deep neural networks to build classification & regression models

from Active Sales – SharewareOnSale https://ift.tt/2OYNcYd https://ift.tt/eA8V8J via Blogger https://ift.tt/2soIEVE #blogger #bloggingtips #bloggerlife #bloggersgetsocial #ontheblog #writersofinstagram #writingprompt #instapoetry #writerscommunity #writersofig #writersblock #writerlife #writtenword #instawriters #spilledink #wordgasm #creativewriting #poetsofinstagram #blackoutpoetry #poetsofig

[isearchgoood]

November 11, 2019 at 10:00PM - The Complete Python Data Science Bundle (96% discount) Ashraf

The Complete Python Data Science Bundle (96% discount) Hurry Offer Only Last For HoursSometime. Don't ever forget to share this post on Your Social media to be the first to tell your firends. This is not a fake stuff its real.

It’s no secret that data scientists stand to make a pretty penny in today’s data-driven world; but if you’re keen on becoming one, you’ll need to master the appropriate tools. Pandas is one of the most popular of the Python data science libraries for working with mounds of data. By expressing data in a tabular format, Pandas makes it easy to perform data cleaning, aggregations and other analyses. Built around hands-on demos, this course will walk you through using Pandas and what it can do as you take on series, data frames, importing/exporting data, and more.

Access 23 lectures & 2.5 hours of content 24/7

Explore Panda’s built-in functions for common data manipulation techniques

Learn how to work with data frames & manage data

Deepen your understanding w/ example-driven lessons

Today’s companies collect and utilize a staggering amount of data to guide their business decisions. But, it needs to be properly cleaned and organized before it can be put to use. Enter NumPy, a core library in the Python data science stack used by data science gurus to wrangle vast amounts of multidimensional data. This course will take you through NumPy’s basic operations, universal functions, and more as you learn from hands-on examples.

Access 27 lectures & 2.5 hours of content 24/7

Familiarize yourself w/ NumPy’s basic operations & universal functions

Learn how to properly manage data w/ hands-on examples

Validate your training w/ a certificate of completion

From tech to medicine and finance, data plays a pivotal role in guiding today’s businesses. But, it needs to be properly broken down and visualized before you can get any sort of actionable insights. That’s where Seaborn comes into play. Designed for enhanced data visualization, this Python-based library helps bridge the gap between vast swathes of data and the valuable insights they contain. This course acts as your Seaborne guide, walking you through what it can do and how you can use it to display information, find relationships, and much more.

Access 16 lectures & 1.5 hours of content 24/7

Familiarize yourself w/ Seaborn via hands-on examples

Discover Seaborn’s enhanced data visualization capabilities

Explore histograms, linear relationships & more visualization concepts

Before a data scientist can properly analyze their data, they must first visualize it and understand any relationships that might exist in the information. To this end, many data professionals use Matplotlib, an industry-favorite Python library for visualizing data. Highly customizable and packed with powerful features for building graphs and plots, Matplotlib is an essential tool for any aspiring data scientist, and this course will show you how it ticks.

Access 30 lectures & 3 hours of content 24/7

Explore the anatomy of a Matplotlib figure & its customizable parts

Dive into figures, axes, subplots & more components

Learn how to draw statistical insights from data

Understand different ways of conveying statistical information

One of the most popular data analytics engines out there, Spark has become a staple in many a data scientist’s toolbox; and the latest version, Spark 2.x, brings more efficient and intuitive features to the table. Jump into this comprehensive course, and you’ll learn how to better analyze mounds of data, extract valuable insights, and more with Spark 2.x. Plus, this course comes loaded with hands-on examples to refine your knowledge, as you analyze data from restaurants listed on Zomato and churn through historical data from the Olympics and the FIFA world cup!

Access 27 lectures & 3 hours of content 24/7

Explore what Spark 2.x can do via hands-on projects

Learn how to analyze data at scale & extract insights w/ Spark transformations and actions

Deepen your understanding of data frames & Resilient Distributed Datasets

You don’t need to be a programming prodigy to get started in data science. Easy to use and highly accessible, Plotly is library in Python that lets you create complex plots and graphs with minimal programming know-how. From creating basic charts to adding motion to your visualizations, this course will walk you through the Plotly essentials with hands-on examples that you can follow.

Access 27 lectures & 2 hours of content 24/7

Learn how to build line charts, bar charts, histograms, pie charts & other basic visualizations

Explore visualizing data in more than two dimensions

Discover how to add motion to your graphs

Work w/ plots on your local machine or share them via the Plotly Cloud

In addition to handling vast amounts of batch data, Spark has extremely powerful support for continuous applications, or those with streaming data that is constantly updated and changes in real-time. Using the new and improved Spark 2.x, this course offers a deep dive into stream architectures and analyzing continuous data. You’ll also follow along a number of real-world examples, like analyzing data from restaurants listed on Zomato and real-time Twitter data.

Access 36 lectures & 2.5 hours of content 24/7

Familiarize yourself w/ Spark 2.x & its support for continuous applications

Learn how to analyze data from real-world streams

Analyze data from restaurants listed on Zomato & real-time Twitter data

More companies are using the power of deep learning and neural networks to create advanced AI that learns on its own. From speech recognition software to recommendation systems, deep learning frameworks, like PyTorch, make creating these products easier. Jump in, and you’ll get up to speed with PyTorch and its capabilities as you analyze a host of real-world datasets and build your own machine learning models.

Access 41 lectures & 3.5 hours of content 24/7

Understand neurons & neural networks and how they factor into machine learning

Explore the basic steps involved in training a neural network

Familiarize yourself w/ PyTorch & Python 3

Analyze air quality data, salary data & more real-world datasets

Fast, scalable, and packed with an intuitive API for machine learning, Apache MXNet is a deep learning framework that makes it easy to build machine learning applications that learn quickly and can run on a variety of devices. This course walks you through the Apache MXNet essentials so you can start creating your own neural networks, the building blocks that allow AI to learn on their own.

Access 31 lectures & 2 hours of content 24/7

Explore neurons & neural networks and how they factor into machine learning

Walk through the basic steps of training a neural network

Dive into building neural networks for classifying images & voices

Refine your training w/ real-world examples & datasets

Python is a general-purpose programming language which can be used to solve a wide variety of problems, be they in data analysis, machine learning, or web development. This course lays a foundation to start using Python, which considered one of the best first programming languages to learn. Even if you’ve never even thought about coding, this course will serve as your diving board to jump right in.

Access 28 lectures & 3 hours of content 24/7

Gain a fundamental understanding of Python loops, data structures, functions, classes, & more

Learn how to solve basic programming tasks

Apply your skills confidently to solve real problems

Classification models play a key role in helping computers accurately predict outcomes, like when a banking program identifies loan applicants as low, medium, or high credit risks. This course offers an overview of machine learning with a focus on implementing classification models via Python’s scikit-learn. If you’re an aspiring developer or data scientist looking to take your machine learning knowledge further, this course is for you.

Access 17 lectures & 2 hours of content 24/7

Tackle basic machine learning concepts, including supervised & unsupervised learning, regression, and classification

Learn about support vector machines, decision trees & random forests using real data sets

Discover how to use decision trees to get better results

Deep learning isn’t just about helping computers learn from data—it’s about helping those machines determine what’s important in those datasets. This is what allows for Tesla’s Model S to drive on its own and for Siri to determine where the best brunch spots are. Using the machine learning workhorse that is TensorFlow, this course will show you how to build deep learning models and explore advanced AI capabilities with neural networks.

Access 62 lectures & 8.5 hours of content 24/7

Understand the anatomy of a TensorFlow program & basic constructs such as graphs, tensors, and constants

Create regression models w/ TensorFlow

Learn how to streamline building & evaluating models w/ TensorFlow’s estimator API

Use deep neural networks to build classification & regression models

from Active Sales – SharewareOnSale https://ift.tt/2OYNcYd https://ift.tt/eA8V8J via Blogger https://ift.tt/34U3Bpf #blogger #bloggingtips #bloggerlife #bloggersgetsocial #ontheblog #writersofinstagram #writingprompt #instapoetry #writerscommunity #writersofig #writersblock #writerlife #writtenword #instawriters #spilledink #wordgasm #creativewriting #poetsofinstagram #blackoutpoetry #poetsofig

[isearchgoood]

July 22, 2019 at 10:00PM - The Complete Python Data Science Bundle (96% discount) Ashraf

The Complete Python Data Science Bundle (96% discount) Hurry Offer Only Last For HoursSometime. Don't ever forget to share this post on Your Social media to be the first to tell your firends. This is not a fake stuff its real.

It’s no secret that data scientists stand to make a pretty penny in today’s data-driven world; but if you’re keen on becoming one, you’ll need to master the appropriate tools. Pandas is one of the most popular of the Python data science libraries for working with mounds of data. By expressing data in a tabular format, Pandas makes it easy to perform data cleaning, aggregations and other analyses. Built around hands-on demos, this course will walk you through using Pandas and what it can do as you take on series, data frames, importing/exporting data, and more.

Access 23 lectures & 2.5 hours of content 24/7

Explore Panda’s built-in functions for common data manipulation techniques

Learn how to work with data frames & manage data

Deepen your understanding w/ example-driven lessons

Today’s companies collect and utilize a staggering amount of data to guide their business decisions. But, it needs to be properly cleaned and organized before it can be put to use. Enter NumPy, a core library in the Python data science stack used by data science gurus to wrangle vast amounts of multidimensional data. This course will take you through NumPy’s basic operations, universal functions, and more as you learn from hands-on examples.

Access 27 lectures & 2.5 hours of content 24/7

Familiarize yourself w/ NumPy’s basic operations & universal functions

Learn how to properly manage data w/ hands-on examples

Validate your training w/ a certificate of completion

From tech to medicine and finance, data plays a pivotal role in guiding today’s businesses. But, it needs to be properly broken down and visualized before you can get any sort of actionable insights. That’s where Seaborn comes into play. Designed for enhanced data visualization, this Python-based library helps bridge the gap between vast swathes of data and the valuable insights they contain. This course acts as your Seaborne guide, walking you through what it can do and how you can use it to display information, find relationships, and much more.

Access 16 lectures & 1.5 hours of content 24/7

Familiarize yourself w/ Seaborn via hands-on examples

Discover Seaborn’s enhanced data visualization capabilities

Explore histograms, linear relationships & more visualization concepts

Before a data scientist can properly analyze their data, they must first visualize it and understand any relationships that might exist in the information. To this end, many data professionals use Matplotlib, an industry-favorite Python library for visualizing data. Highly customizable and packed with powerful features for building graphs and plots, Matplotlib is an essential tool for any aspiring data scientist, and this course will show you how it ticks.

Access 30 lectures & 3 hours of content 24/7

Explore the anatomy of a Matplotlib figure & its customizable parts

Dive into figures, axes, subplots & more components

Learn how to draw statistical insights from data

Understand different ways of conveying statistical information

One of the most popular data analytics engines out there, Spark has become a staple in many a data scientist’s toolbox; and the latest version, Spark 2.x, brings more efficient and intuitive features to the table. Jump into this comprehensive course, and you’ll learn how to better analyze mounds of data, extract valuable insights, and more with Spark 2.x. Plus, this course comes loaded with hands-on examples to refine your knowledge, as you analyze data from restaurants listed on Zomato and churn through historical data from the Olympics and the FIFA world cup!

Access 27 lectures & 3 hours of content 24/7

Explore what Spark 2.x can do via hands-on projects

Learn how to analyze data at scale & extract insights w/ Spark transformations and actions

Deepen your understanding of data frames & Resilient Distributed Datasets

You don’t need to be a programming prodigy to get started in data science. Easy to use and highly accessible, Plotly is library in Python that lets you create complex plots and graphs with minimal programming know-how. From creating basic charts to adding motion to your visualizations, this course will walk you through the Plotly essentials with hands-on examples that you can follow.

Access 27 lectures & 2 hours of content 24/7

Learn how to build line charts, bar charts, histograms, pie charts & other basic visualizations

Explore visualizing data in more than two dimensions

Discover how to add motion to your graphs

Work w/ plots on your local machine or share them via the Plotly Cloud

In addition to handling vast amounts of batch data, Spark has extremely powerful support for continuous applications, or those with streaming data that is constantly updated and changes in real-time. Using the new and improved Spark 2.x, this course offers a deep dive into stream architectures and analyzing continuous data. You’ll also follow along a number of real-world examples, like analyzing data from restaurants listed on Zomato and real-time Twitter data.

Access 36 lectures & 2.5 hours of content 24/7

Familiarize yourself w/ Spark 2.x & its support for continuous applications

Learn how to analyze data from real-world streams

Analyze data from restaurants listed on Zomato & real-time Twitter data

More companies are using the power of deep learning and neural networks to create advanced AI that learns on its own. From speech recognition software to recommendation systems, deep learning frameworks, like PyTorch, make creating these products easier. Jump in, and you’ll get up to speed with PyTorch and its capabilities as you analyze a host of real-world datasets and build your own machine learning models.

Access 41 lectures & 3.5 hours of content 24/7

Understand neurons & neural networks and how they factor into machine learning

Explore the basic steps involved in training a neural network

Familiarize yourself w/ PyTorch & Python 3

Analyze air quality data, salary data & more real-world datasets

Fast, scalable, and packed with an intuitive API for machine learning, Apache MXNet is a deep learning framework that makes it easy to build machine learning applications that learn quickly and can run on a variety of devices. This course walks you through the Apache MXNet essentials so you can start creating your own neural networks, the building blocks that allow AI to learn on their own.

Access 31 lectures & 2 hours of content 24/7

Explore neurons & neural networks and how they factor into machine learning

Walk through the basic steps of training a neural network

Dive into building neural networks for classifying images & voices

Refine your training w/ real-world examples & datasets

Python is a general-purpose programming language which can be used to solve a wide variety of problems, be they in data analysis, machine learning, or web development. This course lays a foundation to start using Python, which considered one of the best first programming languages to learn. Even if you’ve never even thought about coding, this course will serve as your diving board to jump right in.

Access 28 lectures & 3 hours of content 24/7

Gain a fundamental understanding of Python loops, data structures, functions, classes, & more

Learn how to solve basic programming tasks

Apply your skills confidently to solve real problems

Classification models play a key role in helping computers accurately predict outcomes, like when a banking program identifies loan applicants as low, medium, or high credit risks. This course offers an overview of machine learning with a focus on implementing classification models via Python’s scikit-learn. If you’re an aspiring developer or data scientist looking to take your machine learning knowledge further, this course is for you.

Access 17 lectures & 2 hours of content 24/7

Tackle basic machine learning concepts, including supervised & unsupervised learning, regression, and classification

Learn about support vector machines, decision trees & random forests using real data sets

Discover how to use decision trees to get better results

Deep learning isn’t just about helping computers learn from data—it’s about helping those machines determine what’s important in those datasets. This is what allows for Tesla’s Model S to drive on its own and for Siri to determine where the best brunch spots are. Using the machine learning workhorse that is TensorFlow, this course will show you how to build deep learning models and explore advanced AI capabilities with neural networks.

Access 62 lectures & 8.5 hours of content 24/7

Understand the anatomy of a TensorFlow program & basic constructs such as graphs, tensors, and constants

Create regression models w/ TensorFlow

Learn how to streamline building & evaluating models w/ TensorFlow’s estimator API

Use deep neural networks to build classification & regression models

from Active Sales – SharewareOnSale https://ift.tt/2OYNcYd https://ift.tt/eA8V8J via Blogger https://ift.tt/2Y6S3LD #blogger #bloggingtips #bloggerlife #bloggersgetsocial #ontheblog #writersofinstagram #writingprompt #instapoetry #writerscommunity #writersofig #writersblock #writerlife #writtenword #instawriters #spilledink #wordgasm #creativewriting #poetsofinstagram #blackoutpoetry #poetsofig

[isearchgoood]

July 07, 2019 at 10:06PM - The Complete Python Data Science Bundle (96% discount) Ashraf

The Complete Python Data Science Bundle (96% discount) Hurry Offer Only Last For HoursSometime. Don't ever forget to share this post on Your Social media to be the first to tell your firends. This is not a fake stuff its real.

It’s no secret that data scientists stand to make a pretty penny in today’s data-driven world; but if you’re keen on becoming one, you’ll need to master the appropriate tools. Pandas is one of the most popular of the Python data science libraries for working with mounds of data. By expressing data in a tabular format, Pandas makes it easy to perform data cleaning, aggregations and other analyses. Built around hands-on demos, this course will walk you through using Pandas and what it can do as you take on series, data frames, importing/exporting data, and more.

Access 23 lectures & 2.5 hours of content 24/7

Explore Panda’s built-in functions for common data manipulation techniques

Learn how to work with data frames & manage data

Deepen your understanding w/ example-driven lessons

Today’s companies collect and utilize a staggering amount of data to guide their business decisions. But, it needs to be properly cleaned and organized before it can be put to use. Enter NumPy, a core library in the Python data science stack used by data science gurus to wrangle vast amounts of multidimensional data. This course will take you through NumPy’s basic operations, universal functions, and more as you learn from hands-on examples.

Access 27 lectures & 2.5 hours of content 24/7

Familiarize yourself w/ NumPy’s basic operations & universal functions

Learn how to properly manage data w/ hands-on examples

Validate your training w/ a certificate of completion

From tech to medicine and finance, data plays a pivotal role in guiding today’s businesses. But, it needs to be properly broken down and visualized before you can get any sort of actionable insights. That’s where Seaborn comes into play. Designed for enhanced data visualization, this Python-based library helps bridge the gap between vast swathes of data and the valuable insights they contain. This course acts as your Seaborne guide, walking you through what it can do and how you can use it to display information, find relationships, and much more.

Access 16 lectures & 1.5 hours of content 24/7

Familiarize yourself w/ Seaborn via hands-on examples

Discover Seaborn’s enhanced data visualization capabilities

Explore histograms, linear relationships & more visualization concepts

Before a data scientist can properly analyze their data, they must first visualize it and understand any relationships that might exist in the information. To this end, many data professionals use Matplotlib, an industry-favorite Python library for visualizing data. Highly customizable and packed with powerful features for building graphs and plots, Matplotlib is an essential tool for any aspiring data scientist, and this course will show you how it ticks.

Access 30 lectures & 3 hours of content 24/7

Explore the anatomy of a Matplotlib figure & its customizable parts

Dive into figures, axes, subplots & more components

Learn how to draw statistical insights from data

Understand different ways of conveying statistical information

One of the most popular data analytics engines out there, Spark has become a staple in many a data scientist’s toolbox; and the latest version, Spark 2.x, brings more efficient and intuitive features to the table. Jump into this comprehensive course, and you’ll learn how to better analyze mounds of data, extract valuable insights, and more with Spark 2.x. Plus, this course comes loaded with hands-on examples to refine your knowledge, as you analyze data from restaurants listed on Zomato and churn through historical data from the Olympics and the FIFA world cup!

Access 27 lectures & 3 hours of content 24/7

Explore what Spark 2.x can do via hands-on projects

Learn how to analyze data at scale & extract insights w/ Spark transformations and actions

Deepen your understanding of data frames & Resilient Distributed Datasets

You don’t need to be a programming prodigy to get started in data science. Easy to use and highly accessible, Plotly is library in Python that lets you create complex plots and graphs with minimal programming know-how. From creating basic charts to adding motion to your visualizations, this course will walk you through the Plotly essentials with hands-on examples that you can follow.

Access 27 lectures & 2 hours of content 24/7

Learn how to build line charts, bar charts, histograms, pie charts & other basic visualizations

Explore visualizing data in more than two dimensions

Discover how to add motion to your graphs

Work w/ plots on your local machine or share them via the Plotly Cloud

In addition to handling vast amounts of batch data, Spark has extremely powerful support for continuous applications, or those with streaming data that is constantly updated and changes in real-time. Using the new and improved Spark 2.x, this course offers a deep dive into stream architectures and analyzing continuous data. You’ll also follow along a number of real-world examples, like analyzing data from restaurants listed on Zomato and real-time Twitter data.

Access 36 lectures & 2.5 hours of content 24/7

Familiarize yourself w/ Spark 2.x & its support for continuous applications

Learn how to analyze data from real-world streams

Analyze data from restaurants listed on Zomato & real-time Twitter data

More companies are using the power of deep learning and neural networks to create advanced AI that learns on its own. From speech recognition software to recommendation systems, deep learning frameworks, like PyTorch, make creating these products easier. Jump in, and you’ll get up to speed with PyTorch and its capabilities as you analyze a host of real-world datasets and build your own machine learning models.

Access 41 lectures & 3.5 hours of content 24/7

Understand neurons & neural networks and how they factor into machine learning

Explore the basic steps involved in training a neural network

Familiarize yourself w/ PyTorch & Python 3

Analyze air quality data, salary data & more real-world datasets

Fast, scalable, and packed with an intuitive API for machine learning, Apache MXNet is a deep learning framework that makes it easy to build machine learning applications that learn quickly and can run on a variety of devices. This course walks you through the Apache MXNet essentials so you can start creating your own neural networks, the building blocks that allow AI to learn on their own.

Access 31 lectures & 2 hours of content 24/7

Explore neurons & neural networks and how they factor into machine learning

Walk through the basic steps of training a neural network

Dive into building neural networks for classifying images & voices

Refine your training w/ real-world examples & datasets

Python is a general-purpose programming language which can be used to solve a wide variety of problems, be they in data analysis, machine learning, or web development. This course lays a foundation to start using Python, which considered one of the best first programming languages to learn. Even if you’ve never even thought about coding, this course will serve as your diving board to jump right in.

Access 28 lectures & 3 hours of content 24/7

Gain a fundamental understanding of Python loops, data structures, functions, classes, & more

Learn how to solve basic programming tasks

Apply your skills confidently to solve real problems

Classification models play a key role in helping computers accurately predict outcomes, like when a banking program identifies loan applicants as low, medium, or high credit risks. This course offers an overview of machine learning with a focus on implementing classification models via Python’s scikit-learn. If you’re an aspiring developer or data scientist looking to take your machine learning knowledge further, this course is for you.

Access 17 lectures & 2 hours of content 24/7

Tackle basic machine learning concepts, including supervised & unsupervised learning, regression, and classification

Learn about support vector machines, decision trees & random forests using real data sets

Discover how to use decision trees to get better results

Deep learning isn’t just about helping computers learn from data—it’s about helping those machines determine what’s important in those datasets. This is what allows for Tesla’s Model S to drive on its own and for Siri to determine where the best brunch spots are. Using the machine learning workhorse that is TensorFlow, this course will show you how to build deep learning models and explore advanced AI capabilities with neural networks.

Access 62 lectures & 8.5 hours of content 24/7

Understand the anatomy of a TensorFlow program & basic constructs such as graphs, tensors, and constants

Create regression models w/ TensorFlow

Learn how to streamline building & evaluating models w/ TensorFlow’s estimator API

Use deep neural networks to build classification & regression models

from Active Sales – SharewareOnSale https://ift.tt/2OYNcYd https://ift.tt/eA8V8J via Blogger https://ift.tt/2Sb9NnP #blogger #bloggingtips #bloggerlife #bloggersgetsocial #ontheblog #writersofinstagram #writingprompt #instapoetry #writerscommunity #writersofig #writersblock #writerlife #writtenword #instawriters #spilledink #wordgasm #creativewriting #poetsofinstagram #blackoutpoetry #poetsofig

[isearchgoood]

March 24, 2019 at 10:02PM - The Complete Python Data Science Bundle (96% discount) Ashraf

The Complete Python Data Science Bundle (96% discount) Hurry Offer Only Last For HoursSometime. Don't ever forget to share this post on Your Social media to be the first to tell your firends. This is not a fake stuff its real.

It’s no secret that data scientists stand to make a pretty penny in today’s data-driven world; but if you’re keen on becoming one, you’ll need to master the appropriate tools. Pandas is one of the most popular of the Python data science libraries for working with mounds of data. By expressing data in a tabular format, Pandas makes it easy to perform data cleaning, aggregations and other analyses. Built around hands-on demos, this course will walk you through using Pandas and what it can do as you take on series, data frames, importing/exporting data, and more.

Access 23 lectures & 2.5 hours of content 24/7

Explore Panda’s built-in functions for common data manipulation techniques

Learn how to work with data frames & manage data

Deepen your understanding w/ example-driven lessons

Today’s companies collect and utilize a staggering amount of data to guide their business decisions. But, it needs to be properly cleaned and organized before it can be put to use. Enter NumPy, a core library in the Python data science stack used by data science gurus to wrangle vast amounts of multidimensional data. This course will take you through NumPy’s basic operations, universal functions, and more as you learn from hands-on examples.

Access 27 lectures & 2.5 hours of content 24/7

Familiarize yourself w/ NumPy’s basic operations & universal functions

Learn how to properly manage data w/ hands-on examples

Validate your training w/ a certificate of completion

From tech to medicine and finance, data plays a pivotal role in guiding today’s businesses. But, it needs to be properly broken down and visualized before you can get any sort of actionable insights. That’s where Seaborn comes into play. Designed for enhanced data visualization, this Python-based library helps bridge the gap between vast swathes of data and the valuable insights they contain. This course acts as your Seaborne guide, walking you through what it can do and how you can use it to display information, find relationships, and much more.

Access 16 lectures & 1.5 hours of content 24/7

Familiarize yourself w/ Seaborn via hands-on examples

Discover Seaborn’s enhanced data visualization capabilities

Explore histograms, linear relationships & more visualization concepts

Before a data scientist can properly analyze their data, they must first visualize it and understand any relationships that might exist in the information. To this end, many data professionals use Matplotlib, an industry-favorite Python library for visualizing data. Highly customizable and packed with powerful features for building graphs and plots, Matplotlib is an essential tool for any aspiring data scientist, and this course will show you how it ticks.

Access 30 lectures & 3 hours of content 24/7

Explore the anatomy of a Matplotlib figure & its customizable parts

Dive into figures, axes, subplots & more components

Learn how to draw statistical insights from data

Understand different ways of conveying statistical information

One of the most popular data analytics engines out there, Spark has become a staple in many a data scientist’s toolbox; and the latest version, Spark 2.x, brings more efficient and intuitive features to the table. Jump into this comprehensive course, and you’ll learn how to better analyze mounds of data, extract valuable insights, and more with Spark 2.x. Plus, this course comes loaded with hands-on examples to refine your knowledge, as you analyze data from restaurants listed on Zomato and churn through historical data from the Olympics and the FIFA world cup!

Access 27 lectures & 3 hours of content 24/7

Explore what Spark 2.x can do via hands-on projects

Learn how to analyze data at scale & extract insights w/ Spark transformations and actions

Deepen your understanding of data frames & Resilient Distributed Datasets

You don’t need to be a programming prodigy to get started in data science. Easy to use and highly accessible, Plotly is library in Python that lets you create complex plots and graphs with minimal programming know-how. From creating basic charts to adding motion to your visualizations, this course will walk you through the Plotly essentials with hands-on examples that you can follow.

Access 27 lectures & 2 hours of content 24/7

Learn how to build line charts, bar charts, histograms, pie charts & other basic visualizations

Explore visualizing data in more than two dimensions

Discover how to add motion to your graphs

Work w/ plots on your local machine or share them via the Plotly Cloud

In addition to handling vast amounts of batch data, Spark has extremely powerful support for continuous applications, or those with streaming data that is constantly updated and changes in real-time. Using the new and improved Spark 2.x, this course offers a deep dive into stream architectures and analyzing continuous data. You’ll also follow along a number of real-world examples, like analyzing data from restaurants listed on Zomato and real-time Twitter data.

Access 36 lectures & 2.5 hours of content 24/7

Familiarize yourself w/ Spark 2.x & its support for continuous applications

Learn how to analyze data from real-world streams

Analyze data from restaurants listed on Zomato & real-time Twitter data

More companies are using the power of deep learning and neural networks to create advanced AI that learns on its own. From speech recognition software to recommendation systems, deep learning frameworks, like PyTorch, make creating these products easier. Jump in, and you’ll get up to speed with PyTorch and its capabilities as you analyze a host of real-world datasets and build your own machine learning models.

Access 41 lectures & 3.5 hours of content 24/7

Understand neurons & neural networks and how they factor into machine learning

Explore the basic steps involved in training a neural network

Familiarize yourself w/ PyTorch & Python 3

Analyze air quality data, salary data & more real-world datasets

Fast, scalable, and packed with an intuitive API for machine learning, Apache MXNet is a deep learning framework that makes it easy to build machine learning applications that learn quickly and can run on a variety of devices. This course walks you through the Apache MXNet essentials so you can start creating your own neural networks, the building blocks that allow AI to learn on their own.

Access 31 lectures & 2 hours of content 24/7

Explore neurons & neural networks and how they factor into machine learning

Walk through the basic steps of training a neural network

Dive into building neural networks for classifying images & voices

Refine your training w/ real-world examples & datasets

Python is a general-purpose programming language which can be used to solve a wide variety of problems, be they in data analysis, machine learning, or web development. This course lays a foundation to start using Python, which considered one of the best first programming languages to learn. Even if you’ve never even thought about coding, this course will serve as your diving board to jump right in.

Access 28 lectures & 3 hours of content 24/7

Gain a fundamental understanding of Python loops, data structures, functions, classes, & more

Learn how to solve basic programming tasks

Apply your skills confidently to solve real problems

Classification models play a key role in helping computers accurately predict outcomes, like when a banking program identifies loan applicants as low, medium, or high credit risks. This course offers an overview of machine learning with a focus on implementing classification models via Python’s scikit-learn. If you’re an aspiring developer or data scientist looking to take your machine learning knowledge further, this course is for you.

Access 17 lectures & 2 hours of content 24/7

Tackle basic machine learning concepts, including supervised & unsupervised learning, regression, and classification

Learn about support vector machines, decision trees & random forests using real data sets

Discover how to use decision trees to get better results

Deep learning isn’t just about helping computers learn from data—it’s about helping those machines determine what’s important in those datasets. This is what allows for Tesla’s Model S to drive on its own and for Siri to determine where the best brunch spots are. Using the machine learning workhorse that is TensorFlow, this course will show you how to build deep learning models and explore advanced AI capabilities with neural networks.

Access 62 lectures & 8.5 hours of content 24/7

Understand the anatomy of a TensorFlow program & basic constructs such as graphs, tensors, and constants

Create regression models w/ TensorFlow

Learn how to streamline building & evaluating models w/ TensorFlow’s estimator API

Use deep neural networks to build classification & regression models

from Active Sales – SharewareOnSale https://ift.tt/2OYNcYd https://ift.tt/eA8V8J via Blogger https://ift.tt/2CBBQpD #blogger #bloggingtips #bloggerlife #bloggersgetsocial #ontheblog #writersofinstagram #writingprompt #instapoetry #writerscommunity #writersofig #writersblock #writerlife #writtenword #instawriters #spilledink #wordgasm #creativewriting #poetsofinstagram #blackoutpoetry #poetsofig