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faizrashis1995 · 6 years ago

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R vs. Python for Data Science

Hello! This Web page is aimed at shedding some light on the perennial R-vs.-Python debates in the Data Science community. As a professional computer scientist and statistician, I hope to shed some useful light on the topic.

I have potential bias: I've written four R-related books, I've given keynote talks at useR! and other R conferences; I currently serve as Editor-in-Chief of the R Journal; etc. But I am also an enthusiastic Python coder, have been for many years. I hope this analysis will be considered fair and helpful. Elegance

Clear win for Python.

This is subjective, of course, but having written (and taught) in many different programming languages, I really appreciate Python's greatly reduced use of parentheses and braces:

if x > y:

z = 5

w = 8

vs.

if (x > y)

{

z = 5

w = 8

}

Python is sleek!

Learning curve

Huge win for R.

This is of particular interest to me, as an educator. I've taught a number of subjects -- math, stat, CS and even English As a Second Language -- and have given intense thought to the learning process for many, many years.

To even get started in Data Science with Python, one must learn a lot of material not in base Python, e.g., NumPy, Pandas and matplotlib. These libraries require a fair amount of computer systems sophistication.

By contrast, matrix types and basic graphics are built-in to base R. The novice can be doing simple data analyses within minutes.

Python libraries can be tricky to configure, even for the systems-savvy, while most R packages run right out of the box.

Available libraries for Data Science

Slight edge to R.

CRAN has over 14,000 packages. PyPI has over 183,000, but it seems thin on Data Science.

For example, I once needed code to do fast calculation of nearest-neighbors of a given data point. (NOT code using that to do classification.) I was able to immediately find not one but two packages in CRAN to do this. By contrast, recently I tried to find nearest-neighbor code for Python and at least with my cursory search in PyPi, came up empty-handed; there was just one implementation that described itself as simple and straightforward, nothing fast.

The following (again, cursory) searches in PyPI turned up nothing: EM algorithm; log-linear model; Poisson regression; instrumental variables; spatial data; familywise error rate; etc.

This is not to say no Python libraries exist for these things; I am simply saying that they are not easily found in PyPI, whereas it is easy to find them in CRAN.

And the fact that R has a canonical package structure is a big advantage. When installing a new package, one knows exactly what to expect. Similarly, R's generic functions are an enormous plus for R. When I'm using a new package, I know that I can probably use print(), plot(), summary(), and so on, while I am exploring; All these form a "universal language" for packages.

Machine learning

Slight edge to Python here.

The R-vs.-Python debate is largely a statistics-vs.-CS debate, and since most research in neural networks has come from CS, available software for NNs is mostly in Python. RStudio has done some excellent work in developing a Keras implementation, but so far R is limited in this realm.

On the other hand, random forest research has been mainly pursued by the stat community, and in this realm I'd submit that R has the superior software. R also has excellent packages for gradient boosting.

I give the edge to Python here because for many people, machine learning means NNs.

Statistical sophistication

Big win for R.

In my book, the Art of R Programming, I made the statement, "R is written by statisticians, for statisticians," a line which I've been pleased to see used by others on occasion. It's important!

To be frank, I find the machine learning people, who mostly advocate Python, often have a poor understanding of, and in some cases even a disdain for, the statistical issues in ML. I was shocked recently, for instance, to see one of the most prominent ML people state in his otherwise superb book that standardizing the data to mean-0, variance-1 means one is assuming the data are Gaussian — absolutely false and misleading.

Parallel computation

Let's call it a tie.

Neither the base version of R nor Python have good support for multicore computation. Threads in Python are nice for I/O, but multicore computation using them is impossible, due to the infamous Global Interpreter Lock. Python's multiprocessing package is not a good workaround, nor is R's 'parallel' package. External libraries supporting cluster computation are OK in both languages.

Currently Python has better interfaces to GPUs.

C/C++ interface and performance enhancement

Slight win for R.

Though there are tools like SWIG etc. for interfacing Python to C/C++, as far is I know there is nothing remotely as powerful as R's Rcpp for this at present. The Pybind11 package is being developed.

In addition, R's new ALTREP idea has great potential for enhancing performance and usability.

On the other hand, the Cython and PyPy variants of Python can in some cases obviate the need for explicit C/C++ interface in the first place; indeed some would say Cython IS a C/C++ interface.

Object orientation, metaprogramming

Slight win for R.

For instance, though functions are objects in both languages, R takes that further than does Python. Whenever I work in Python, I'm annoyed by the fact that I cannot directly print a function to the terminal or edit it, which I do a lot in R.

Python has just one OOP paradigm. In R, you have your choice of several (S3, S4, R6 etc.), though some may debate whether this is a good thing.

Given R's magic metaprogramming features (code that produces code), computer scientists ought to be drooling over R. But most CS people are unaware of it.

Language unity

Sad loss for R.

Python is currently undergoing a transition from version 2.7 to 3.x. This will cause some disruption, but nothing too elaborate.

By contrast, R is rapidly devolving into two mutually unintelligible dialects, ordinary R and the Tidyverse. I, as a seasoned R programmer, cannot read Tidy code, as it calls numerous Tidyverse functions that I don't know. Conversely, as one person in the Twitter discussion of this document noted (approvingly), "One can code in the Tidyverse while knowing very little R."

I've been a skeptic on Tidyverse. For instance,I question the claim that it makes R more accessible to nonprogrammers.

Linked data structures

Win for Python.

Not a big issue in Data Science, but it does come up in some contexts.

Classical computer science data structures, e.g. binary trees, are easy to implement in Python. It is not part of base R, but can be done in various ways, e.g. the datastructures package, which wraps the widely-used Boost C++ library.

Online help

Big win for R.

To begin with, R's basic help() function is much more informative than Python's. It's nicely supplemented by example(). And most important, the custom of writing vignettes in R packages makes R a hands-down winner in this aspect.

R/Python interoperability

RStudio is to be commended for developing the reticulate package, to serve as a bridge between Python and R. It's an outstanding effort, and works well for pure computation. But as far as I can tell, it does not solve the knotty problems that arise in Python, e.g. virtual environments and the like.

At present, I do not recommend writing mixed Python/R code.[Source]-https://github.com/matloff/R-vs.-Python-for-Data-Science

Advanced level python course with 100% Job Assistance Guarantee Provided. We Have 3 Sessions Per Week And 90 Hours Certified Basic Python Classes In Thane Training Offered By Asterix Solution

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nicaurybenitezcortorreal-blog · 8 years ago

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Getting Started with Machine Learning in One Hour!

By Abhijit Annaldas, Microsoft.

I was planning agenda for my one hour talk. Conveying the learning paths, setting up the environment and explaining the important machine learning concepts finally made it to agenda after a lot of contemplation and thought. I initially thought about various ways this talk could have been done including - hands on python with linear regression, explaining linear regression in detail, or just sharing my learning journey that I went through past 18 months almost. But I wanted to start something that leaves the audience with lots of new information and questions to work on. Create curiosity and interest in them. And I guess I was able to do that to a decent level. Basically, to get them started with Machine Learning. That’s how this guide ended up being called Getting Started with Machine Learning in one hour.

The notes for the talk were great for an introductory learning path, but were structured only for myself to help with the talk. Hence I wrote a machine learning getting started guide out of it and here it is. I’m very happy the way this ended up taking shape and I’m excited to share this!

There are two main approaches to learn Machine Learning. Theoretical Machine Learning approach and Applied Machine Learning approach. I’ve written about it in my earlier blog post.

Theoretical Machine Learning

Below are the subjects that you can start with (ordered as I think they are appropriate). For theoretical approach of learning Machine Learning, below subjects should be studied with great rigor and in depths.

Linear Algebra - MIT, IISc. Bangalore

Calculus - Basics, Coursera, Advanced, Coursera

Statistical Learning Theory - MIT, Stanford

Machine Learning - Coursera, Caltech

Programming language to implement machine learning research ideas.

The way forward could be reading research papers, implementing research work/new algorithms, developing expertise and picking a specialization further on to the research path.

Applied Machine Learning

Good understanding of the basics of above subjects (1 to 4).

Machine Learning (imp concepts explained below): Coursera, Caltech

Learn to use popular machine learning, data manipulation and visualization libraries in the chosen programming language. I personally use Python programming language, hence I’ll elaborate on that below.

Must know Python Libraries: numpy, , scikit-learn,

Other popular python libraries: , XGBoost, CatBoost

Quick Start Option

If you want to get a taste of what is Machine Learning about and what it could be like. You can start this way for experimenting, getting quick hands on. Not an ideal way if you want to get serious about Data Science in long run.

Know Machine Learning Concepts Overview (below)

Learn Python or R

Understand and learn to use popular libraries in your language of choice

Python Environment setup

Python

Python.org Download, Learn OR

Anaconda Download, Learn

Code Editor / IDE

Visual Studio Code (Search and install python extension, pick the most downloaded one)

Notepad++

Installing python packages

Managing packages with pip, python’s native tool: pip install

Managing packages with anaconda: conda install

Managing Python (native) virtual environments (if multiple environments are needed)

Create virtual environment: python -m venv c:\path\to\env\folder

Command help: python -m venv -h

Switch environments: activate.bat script located in the virtual environment folder

Managing Anaconda virtual environments (if multiple environments are needed)

Default conda environment - root

List available environments - conda env list

Create new environment - conda create --name environment_name

Switch to environment - activate environment_name or source activate environment_name

Machine Learning Concepts Overview

Machine Learning: Is an approach to find patterns from a large set of data through a function f(x) which effectively generalizes to unseen x to find learned patterns in unseen data and make the inferences the Machine Learning Model was trained for.

Dataset: Data being used to apply machine learning and find patterns from. For supervised type of machine learning applications, the dataset contains both x (input/attributes/independent variables) and y (target/labels/dependent variables) data. For unsupervised data it’s just x, input and the output of the data is some sort of learned patterns (like clusters, groups, etc.)

Train set: A subset of Dataset fed to (train) machine learning algorithm to learn patterns

Evaluation / Validation / Cross Validation Set: Subset of Dataset not in Train set used to evaluate how the machine learning algorithm is doing.

Test set: Dataset to predict learned insights for. For supervised problems, target/label y like in train set is to be predicted and hence it isn’t a part of train set. For unsupervised, train and test sets can be identical.

Types:

Supervised: In supervised problems, the historical data includes the labels (target attribute, outcomes) that need to be predicted for future/unseen data. For example, for housing price prediction we have data about house (area, # of bedrooms, location, etc.) and price. Here the after training a machine learning model with given data (X - data) and price (Y - labels), in future, price (Y) will be predicted for new/unseen data (X).

Unsupervised: In unsupervised learning, there is no label or target attribute. A typical example would be clustering data based on learned patterns. Like for a dataset of house details (area, location, price, # of bedrooms, # of floors, built date, etc.) the algorithm needs to find if there is any hidden patterns. For example some houses are very expensive while some others are of usual price. Some houses are very big while some houses are of usual size. With these patterns, records/data is clustered into groups like Luxury-Homes, Non-Luxury Homes, Bunglows, Apartment, etc.

Reinforcement: In Reinforcement Learning, an ‘Agent’ acts in an ‘Environment’ and receives positive or negative feedback. Positive feedback tells an agent that it has done well, and agent proceeds on similar plan/action. Negative feedback tells an agent that it has done something wrong, and should change it’s course of action. The agent and the environment are software/programmed implementations. The core of reinforcement learning is building an agent (or agent’s behaviour in some way) that learns to successfully accomplish a specific task in an environment.

Popular Algorithms: Linear Regression, Logistic Regression, Support Vector Machines, K-Nearest-Neighbors, Decision Trees, Random Forest, Gradient Boosting, Ensemble Learning

Preprocessing: In real world scenario data is rarely clean and neat in a state that Machine Learning algorithms can be directly applied on. Preprocessing is a process of cleaning data to feed to machine learning algorithm. Some of the common preprocessing steps are…

Missing Value: When some of the values are missing, they are usually dealt by adding median/mean values or deleting corresponding row, or using the value from the previous row, etc. There are many ways of doing this. What exactly needs to be done depends on the kind of data, problem being solved and business goals.

Categorical Variables: Discrete finite set of values. Like ‘car type’, ‘department’, etc. These values are converted either into numbers or vectors. Conversion to vectors is known as One-Hot Encoding. There are numerous ways of doing this in python. Some machine learning algorithms/libraries themselves handle categorical columns by encoding internally. One way of encoding is using in scikit-learn.

Scaling: Proportionately reducing values in columns into a common scale like 0 to 1. Having values in all columns in a common range might improve accuracy and training speed to some extent.

Text: Text needs to be processed using Natural Language Processing techniques (out of scope of this guide), when it isn’t preprocessed, it is usually excluded from the training data that is fed to a machine learning algorithm.

Imbalanced datasets: The data shouldn’t be biased, skewed. For e.g., consider a classification task where an algorithm classifies data into 3 different classes - A, B and C. If the dataset has very few/high records of one class w.r.t. others it is said to be biased/imbalanced. Usually data is oversampled in such cases by synthetically generating more random data from existing data. Some machine learning algorithms/libraries allow providing weights or some parameter to balance out the skew internally without us doing the heavy lifting of fixing a skewed dataset. For example, SVM: Separating hyperplane for unbalanced classes in scikit-learn.

Outliers: Outliers need to be dealt with on a case by case basis based on the problem and business case.

Data Transformation: When a column/attribute in a dataset doesn’t have an inherent pattern, it is transformed into something like log(values), sqrt(values), etc. where the transformed values might have interesting pattern/uniformity that can be learned. This is again, obviously case by case basis and needs data exploration to find a right fit.

Feature Engineering: Feature Engineering is a process of deriving hidden insights from existing data. Consider a housing price prediction dataset which has columns ‘plot-width’, ‘plot-length’, ‘number of bedrooms’ and ‘price’. Here we see a key attribute area of the house is missing, but can be calculated based on ‘plot-width’ and ‘plot-length’. So a calculated column, ‘area’ is added to the dataset. This is known as feature engineering. Feature Engineering might be of different difficulty level, sometimes a derived attribute is right in front of sight like here, sometimes it’s really hidden and needs lot of thinking.

Training: This is a main step where the machine learning algorithm is trained on the given data to find generalized patterns to be applied on unseen data. Below are some important nitty-gritty details of this phase…

Feature Selection: Not all features/columns contribute to the learning. These are the columns where the data in them don’t affect the outcome. Such features are removed from the dataset. What features to train on and what features to exclude is decided based on feature importance given by a machine learning algorithm being applied. Most of the modern algorithms do provide the feature importances. If an algorithm doesn’t provide, scikit-learn has capabilities which can help in feature selection. Also correlated features are removed.

Dimensionality Reduction: Dimentionality reduction also aims to find the most important features of all the features, aiming to reduce the dimensionality of the data. The main difference w.r.t. feature importance based feature selection is that, in Dimensionality Reduction, a subset of features and/or derived features are selected. In other words, we may not be able to map the extracted features to the original features. You can find more about dimensionality reduction in scikit-learn here.

Feature Selection vs Dimensionality Reduction: In my opinion, one of the two ways should solve the purpose. If we do both feature selection based on feature importance and dimensionality reduction, we should first do based on feature importances. And then introduce dimensionality reduction. It goes without saying that we should evaluate the performance at every step to understand what’s working and what’s not. Feature selection based on feature importance is easy to interpret as the selected features are subset of all, which isn’t a case with dimensionality reduction.

Evaluation Metric: Evaluation metric is a metric used to evaluate predictions for their correctness. A machine learning algorithm while training uses an evaluation metric to evaluate, compute cost and optimize on the cost convex function. Though each algorithm has a default evaluation metric, it is recommended to specify the exact evaluation metric as per the business case/problem. Like some problems can afford false positives, but cannot afford any false negatives. By specifying the evaluation metric, these nitty gritty details of the model can be controlled.

Parameter tuning: Though most of the today’s state of the art algorithms have sensible default values for the parameters, it always helps to tune the parameters to control the accuracy of a model and improve overall predictions. Parameter tuning can be done on a trial and error basis by repeatedly changing and assessing the accuracy. Alternatively a set of parameter values can be provided to try all/different permutations of those parameters and find the best parameter combination. This can be done using some helper functions called .

Overfitting (Bias): Overfitting is a state where the machine learning model almost memorizes all the training data and predicts almost accurately on data that’s already in training set. This is a state where the model fails to generalize and predict on unseen data. This is also known as model having high bias. Overfitting can be dealt with using Regularization, tuning hyperparameters if configured inappropriately, holding off partial dataset to use correct cross validation(1)(2) strategy.

Underfitting (Variance): Underfitting is a state where the machine learning model’s predictions don’t do well even when predicting on data already in the training set. This is also known as model having high variance. Underfitting can be dealt with adding more data, adding/removing features, trying different machine learning algorithm, etc.

Bias and Variance trade-off (sweet spot): The goal of model training is to find a sweet spot where the model cross validation error is minimum. Initially both cross validation and train error are high (Underfitting/high variance). As the model is training, the error keeps dropping to a certain point where cross validation is minimum and also close to train error (sweet spot). This is optimal spot. After this point, if the model further keeps reducing error (on train set), it almost memorizes the train set ends up overfitting which means higher error on unseen data.

Regularization: At some point when the model is trying to learn further (reducing error, tending towards overfitting), regularization helps in countering the overfitting effects. Regularization is usually a parameter that’s added during cost/error calculation. Machine learning algorithms may not always provide regularization parameter explictly. In such case, usually there are other parameters that can be tuned to introduce regularization to the extent required.

Prediction: To make predictions with trained machine learning model, the prediction method of the model is called by providing the test dataset as parameter. The test dataset should be preprocessed exactly the way it was done on the training dataset. In other words, in the same format of training data which was fed to the machine learning model for training.

Other terminologies:

Model Stacking: When single machine learning algorithm doesn’t do well, multiple machine learning algorithms are used to make predictions and the predictions are combined together in different ways. Most simplest being a weighted predictions. Sometimes, other machine learning model (meta-model) is used on top of the predictions of the first level models. This could go to any level of complexity and can have different pipelines.

Deep Learning

Fun fact is that a majority (over 90% I guess) of all the machine learning problems solved today are solved using just Random Forests, Gradient Boosted Decision Trees, SVM, KNN, Linear Regression, Logistic Regression.

But, there are some set of problems that cannot be solved using above techniques. Problems like image classification, image recognition, natural language processing, audio processing, etc. are solved using a technique called Deep Learning. Before starting deep learning, I believe it’s essential to master all of the above concepts first.

Good Deep Learning resources…

Fast.ai – thanks for the suggestion Pranay Tiwari!

If you know deep learning concepts and want to get your hands dirty, some popular Deep Learning Libraries are: Keras, , Tensorflow, , , , ,

Practice

Yes, practice is the most important thing and this guide would have been incomplete without mentioning about practicing machine learning. To practice and master your skills further, below are the things you can do…

Get datasets from various online data sources. One such popular data source is UCI Machine Learning Repository. Additionally, you can search ‘datasets for machine learning’.

Participate in online machine learning/data science hackathons. Some of the popular ones are - Kaggle, HackerEarth, etc. If you end up starting with something that’s very difficult, try persisting a bit. If it still feels difficult, park it aside and find other. There’s no need to be disappointed. Usually problems on online hackathon have some level of difficulty which may not always be suitable for beginners.

Blog about what you learn! It’ll help you solidify your understanding and thoughts about the subject.

Follow Data Science, Machine Learning topics on Quora, lot of great advice and questions/answers to learn from.

Start listening to podcasts (available on link below)

Closing thoughts

If you are considering the field of Machine Learning/Data Science seriously and you are thinking of making a career switch, think about the your motivations and why you’d like to do it.

If you are sure, I have one advice for you. Never ever ever give up or think if its all worth it. It’s definitely worth it and I can say that as I have walked that path since last 18 months… almost every day, every weekend and every spare hour of my time (except when I was travelling or I was totally drowned by my day job commitments). The road ahead to master data science isn’t easy. As they say, “Rome was not built in a day!”. You’ll need to learn lot of subjects. Juggle between different learning priorities. Even after learning a lot you’ll still find new things that you have never thought/heard about before. New concepts/techniques that you keep discovering might make you feel that you still don’t know a lot of things and there is a lot more ground to cover. This is common. Just stick with it. Set big goals, plan for small tasks and just focus on task at hand. If something new comes up, just scribble it down in your diary and get back to it later.

Thank You!

If you have been reading all the way till here, I appreciate your effort and the time you have invested. I hope this guide was useful to you and has made it little easier for you to get started on your own learning adventure. At some later point of time, if you think this guide has made some difference in your learning adventure, please please come back and leave a comment here. Or reach me at avannaldas .at. hotmail .dot. com. I’d love to hear from you. It’ll give me immense satisfaction to know that this has helped you, and my effort in putting this together was worthwhile.

This was my biggest write up ever. I have spent many hours writing, editing and reviewing this. If you see any mistakes or things that can be improved, please let me know in comments or via email. I’ll fix it the earliest I can and will attribute it to you. This will help everyone who reads this.

Thanks Again!

All the best!

Bio: Abhijit Annaldas is a Software Engineer and a voracious learner who has acquired Machine Learning knowledge and expertise to a fair extent. He is improving expertise day by day by learning new stuﬀ and relentless practice, and has extensive experience building enterprise scale applications in diﬀerent Microsoft and Open Source technologies as a Software Engineer at Microsoft, India since June 2012.

Original. Reposted with permission.

Source

https://www.kdnuggets.com/2017/11/getting-started-machine-learning-one-hour.html

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thedatasciencehyderabad · 4 years ago

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Courses You will learn picture processing techniques, noise discount using moving average strategies, various kinds of filters - smoothing the image by averaging, Gaussian filter and the disadvantages of correlation filters. You will study several types of filters, boundary effects, template matching, price of change within the intensity detection, several types of noise, image sampling and interpolation strategies. Learn about single-layered Perceptrons, Rosenblatt’s perceptron for weights and bias updation. Weights updating strategies - Windrow-Hoff Learning Rule & Rosenblatt’s Perceptron. You may have a excessive degree understanding of the human mind, significance of a number of layers in the Neural Network, extraction of features layers clever, composition of the data in Deep Learning utilizing an image, speech and text. Under Linear Algebra, you'll be taught sets, operate, scalar, vector, matrix, tensor, primary operations and totally different matrix operations. Under Probability one will study Uniform Distribution, Normal Distribution, Binomial Distribution, Discrete Random Variable, Cumulative Distribution Function and Continuous Random Variables. It is mostly geared toward choice makers and individuals who want to decide on what knowledge is price collecting and what is value analyzing. For example, an analyst can arrange an algorithm which can reach a conclusion routinely primarily based on extensive knowledge source. This course has been designed for people excited about extracting that means from written English textual content, although the knowledge may be applied to different human languages as nicely. Exercises after each matter have been actually helpful, regardless of there were too complicated at the finish. In common, the introduced materials was very attention-grabbing and involving! The coaching provided the best basis that permits us to further to broaden on, by exhibiting how concept and follow go hand in hand. Logistic regression Logistic Regression is likely one of the hottest ML algorithms, like Linear Regression. It is a straightforward classification algorithm to predict the categorical dependent variables with the assistance of impartial variables. This module will drive you through all the concepts of Logistic Regression utilized in Machine Learning. Multiple Variable Linear regression Linear Regression is among the most popular ML algorithms used for predictive analysis in Machine Learning, resulting in producing the best outcomes. It is a method assuming a linear relationship between the unbiased variable and dependent variable. Hypothesis Testing This module will teach you about Hypothesis Testing in Machine Learning utilizing Python. Hypothesis Testing is a needed process in Applied Statistics for doing experiments primarily based on the noticed/surveyed data. In this Machine Learning online course, we talk about supervised standalone fashions’ shortcomings and be taught a number of strategies, similar to Ensemble methods to overcome these shortcomings. Dimension Reduction-PCA Principal Component Analysis for Dimensional Reduction is a technique to scale back the complexity of a model like eliminating the variety of input variables for a predictive mannequin to avoid overfitting. I am very grateful to them for successfully and sincerely serving to me to seize first ever opportunity that came into my life. The Bureau of Labour Statistics predicts a development rate of 21 percent—a lot faster than common—by 2028 for software builders, together with the addition of 284,a hundred jobs. Software engineers additionally make a median wage of $eighty four,336 per yr, with potential increases for these with a specialty in AI. As these people are on the crux of development in AI, their job outlook could be very positive. The Department of AI @ IIT Hyderabad's mission is to produce college students with a sound understanding of the fundamentals of the idea and practise of Artificial Intelligence and Machine Learning. The mission can also be to enable students to turn out to be leaders in

the trade and academia nationally and internationally. Finally, the mission is to fulfill the pressing calls for of the nation in the areas of Artificial Intelligence and Machine Learning. They also interact area experts who are working in great MNC firms to coach the scholars on initiatives on weekends and likewise to mentor all the school members on the latest tendencies and main applied sciences. Also, the web training is to develop the worker requirements on a really professional efficiency level that in flip aggravates the important proficiency of all the corporates. They are broadly utilized in text mining and pure language processing tasks. Preprocessing text data Text preprocessing is the tactic to clean and put together text data. This module will teach you all the steps involved in preprocessing a text like Text Cleansing, Tokenization, Stemming, etc. Semantic segmentation The goal of semantic segmentation in computer vision is to label every pixel of the enter image with the respective class representing a specific object/physique. Collaborative filtering (User similarity & Item similarity) Collaborative Filtering is a joint utilization of algorithms where there are a number of methods to determine similar customers or objects to counsel the best suggestions. Popularity based mostly model Popularity based mostly model is a kind of advice system that works based on recognition or something that's presently trending. We lined lots of matters in the time and the trainer was all the time receptive to speaking more intimately or extra generally concerning the subjects and how they were related. I really feel the training has given me the tools to continue learning versus it being a one off session where studying stops as soon as you've finished which is essential given the dimensions and complexity of the subject. Inferential Statistics This module will let you explore elementary concepts of using information for estimation and assessing theories using Python. Pandas, NumPy, Matplotlib, Seaborn This module will give you a deep understanding of exploring data units utilizing Pandas, NumPy, Matplotlib, and Seaborn. Python capabilities, packages and routines Functions and Packages are used for code reusability and program modularity, respectively. Understanding the architecture of RBM and the method concerned in it. Understand and implement Long Short-Term Memory, which is used to maintain the data intact, unless the input makes them overlook. You may also be taught the components of LSTM - cell state, forget gate, input gate and the output gate together with the steps to course of the data. Learn the distinction between RNN and LSTM, Deep RNN and Deep LSTM and completely different terminologies. You will be taught to build an object detection model utilizing Fast R-CNN by utilizing bounding packing containers, understand why fast RCNN is a more sensible choice while dealing with object detection. You may even learn by occasion segmentation issues which could be prevented using Mask RCNN. It permits us to uncover patterns and insights, often with visual methods, inside knowledge. In this module, you will discover ways to collect information and predict the future worth of data specializing in its distinctive trends. Neural Machine Translation Neural Machine Translation is a task for machine translation that uses a synthetic neural network, which automatically converts supply textual content in a single language to the textual content in another language. Introduction to Sequential models A sequence, because the name suggests, is an ordered assortment of several objects. This module will train you how to use the TensorBoard library using Python for Machine Learning. This block will train you how TensorBoard offers the visualization and tooling required for machine studying experimentation. In this module, you'll learn how to improve the productivity of deploying your Machine Learning fashions. In this module, you will discover ways to improve your Machine Learning mannequin’s productiveness Using Flask.

Exploratory Data Analysis, or EDA, is essentially a kind of storytelling for statisticians.@ IIT Hyderabad's mission is to produce college students with a sound understanding of the fundamentals of the idea and practise of Artificial Intelligence and Machine Learning. The mission can also be to enable students to turn out to be leaders in the trade and academia nationally and internationally. Finally, the mission is to fulfill the pressing calls for of the nation in the areas of Artificial Intelligence and Machine Learning. They also interact area experts who are working in great MNC firms to coach the scholars on initiatives on weekends and likewise to mentor all the school members on the latest tendencies and main applied sciences. Also, the web training is to develop the worker requirements on a really professional efficiency level that in flip aggravates the important proficiency of all the corporates. They are broadly utilized in text mining and pure language processing tasks. Preprocessing text data Text preprocessing is the tactic to clean and put together text data. This module will teach you all the steps involved in preprocessing a text like Text Cleansing, Tokenization, Stemming, etc. Semantic segmentation The goal of semantic segmentation in computer vision is to label every pixel of the enter image with the respective class representing a specific object/physique. Collaborative filtering (User similarity & Item similarity) Collaborative Filtering is a joint utilization of algorithms where there are a number of methods to determine similar customers or objects to counsel the best suggestions. Popularity based mostly model Popularity based mostly model is a kind of advice system that works based on recognition or something that's presently trending. We lined lots of matters in the time and the trainer was all the time receptive to speaking more intimately or extra generally concerning the subjects and how they were related. I really feel the training has given me the tools to continue learning versus it being a one off session where studying stops as soon as you've finished which is essential given the dimensions and complexity of the subject. Inferential Statistics This module will let you explore elementary concepts of using information for estimation and assessing theories using Python. Pandas, NumPy, Matplotlib, Seaborn This module will give you a deep understanding of exploring data units utilizing Pandas, NumPy, Matplotlib, and Seaborn. Python capabilities, packages and routines Functions and Packages are used for code reusability and program modularity, respectively. Understanding the architecture of RBM and the method concerned in it. Understand and implement Long Short-Term Memory, which is used to maintain the data intact, unless the input makes them overlook. You may also be taught the components of LSTM - cell state, forget gate, input gate and the output gate together with the steps to course of the data. Learn the distinction between RNN and LSTM, Deep RNN and Deep LSTM and completely different terminologies. You will be taught to build an object detection model utilizing Fast R-CNN by utilizing bounding packing containers, understand why fast RCNN is a more sensible choice while dealing with object detection. You may even learn by occasion segmentation issues which could be prevented using Mask RCNN. It permits us to uncover patterns and insights, often with visual methods, inside knowledge. In this module, you will discover ways to collect information and predict the future worth of data specializing in its distinctive trends. Neural Machine Translation Neural Machine Translation is a task for machine translation that uses a synthetic neural network, which automatically converts supply textual content in a single language to the textual content in another language. Introduction to Sequential models A sequence, because the name suggests, is an ordered assortment of several objects. This module will train you how to use the TensorBoard library using Python for Machine

Learning. This block will train you how TensorBoard offers the visualization and tooling required for machine studying experimentation. In this module, you'll learn how to improve the productivity of deploying your Machine Learning fashions. In this module, you will discover ways to improve your Machine Learning mannequin’s productiveness Using Flask. Exploratory Da seeta Analysis, or EDA, is essentially a kind of storytelling for statisticians.

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