#python numpy random uniform function

[govindhtech]

OneAPI Math Kernel Library (oneMKL): Intel MKL’s Successor

The upgraded and enlarged Intel oneAPI Math Kernel Library supports numerical processing not only on CPUs but also on GPUs, FPGAs, and other accelerators that are now standard components of heterogeneous computing environments.

In order to assist you decide if upgrading from traditional  Intel MKL is the better option for you, this blog will provide you with a brief summary of the maths library.

Why just oneMKL?

The vast array of mathematical functions in oneMKL can be used for a wide range of tasks, from straightforward ones like linear algebra and equation solving to more intricate ones like data fitting and summary statistics.

Several scientific computing functions, including vector math, fast Fourier transforms (FFT), random number generation (RNG), dense and sparse Basic Linear Algebra Subprograms (BLAS), Linear Algebra Package (LAPLACK), and vector math, can all be applied using it as a common medium while adhering to uniform API conventions. Together with GPU offload and SYCL support, all of these are offered in C and Fortran interfaces.

Additionally, when used with  Intel Distribution for Python, oneAPI Math Kernel Library speeds up Python computations (NumPy and SciPy).

Intel MKL Advanced with oneMKL

A refined variant of the standard  Intel MKL is called oneMKL. What sets it apart from its predecessor is its improved support for SYCL and GPU offload. Allow me to quickly go over these two distinctions.

GPU Offload Support for oneMKL

GPU offloading for SYCL and OpenMP computations is supported by oneMKL. With its main functionalities configured natively for Intel GPU offload, it may thus take use of parallel-execution kernels of GPU architectures.

oneMKL adheres to the General Purpose GPU (GPGPU) offload concept that is included in the Intel Graphics Compute Runtime for OpenCL Driver and oneAPI Level Zero. The fundamental execution mechanism is as follows: the host CPU is coupled to one or more compute devices, each of which has several GPU Compute Engines (CE).

SYCL API for oneMKL

OneMKL’s SYCL API component is a part of oneAPI, an open, standards-based, multi-architecture, unified framework that spans industries. (Khronos Group’s SYCL integrates the SYCL specification with language extensions created through an open community approach.) Therefore, its advantages can be reaped on a variety of computing devices, including FPGAs, CPUs, GPUs, and other accelerators. The SYCL API’s functionality has been divided into a number of domains, each with a corresponding code sample available at the oneAPI GitHub repository and its own namespace.

OneMKL Assistance for the Most Recent Hardware

On cutting-edge architectures and upcoming hardware generations, you can benefit from oneMKL functionality and optimizations. Some examples of how oneMKL enables you to fully utilize the capabilities of your hardware setup are as follows:

It supports the 4th generation  Intel Xeon Scalable Processors’ float16 data type via  Intel Advanced Vector Extensions 512 (Intel AVX-512) and optimised bfloat16 and int8 data types via Intel Advanced Matrix Extensions (Intel AMX).

It offers matrix multiply optimisations on the upcoming generation of CPUs and GPUs, including Single Precision General Matrix Multiplication (SGEMM), Double Precision General Matrix Multiplication (DGEMM), RNG functions, and much more.

For a number of features and optimisations on the Intel Data Centre GPU Max Series, it supports Intel Xe Matrix Extensions (Intel XMX).

For memory-bound dense and sparse linear algebra, vector math, FFT, spline computations, and various other scientific computations, it makes use of the hardware capabilities of  Intel Xeon processors and  Intel Data Centre GPUs.

Additional Terms and Context

The brief explanation of terminology provided below could also help you understand oneMKL and how it fits into the heterogeneous-compute ecosystem.

The C++ with SYCL interfaces for performance math library functions are defined in the oneAPI Specification for oneMKL. The oneMKL specification has the potential to change more quickly and often than its implementations.

The specification is implemented in an open-source manner by the oneAPI Math Kernel Library (oneMKL) Interfaces project. With this project, we hope to show that the SYCL interfaces described in the oneMKL specification may be implemented for any target hardware and math library.

The intention is to gradually expand the implementation, even though the one offered here might not be the complete implementation of the specification. We welcome community participation in this project, as well as assistance in expanding support to more math libraries and a variety of hardware targets.

With C++ and SYCL interfaces, as well as comparable capabilities with C and Fortran interfaces, oneMKL is the  Intel product implementation of the specification. For Intel CPU and  Intel GPU hardware, it is extremely optimized.

Next up, what?

Launch oneMKL now to begin speeding up your numerical calculations like never before! Leverage oneMKL’s powerful features to expedite math processing operations and improve application performance while reducing development time for both current and future Intel platforms.

Keep in mind that oneMKL is rapidly evolving even while you utilize the present features and optimizations! In an effort to keep up with the latest Intel technology, we continuously implement new optimizations and support for sophisticated math functions.

They also invite you to explore the  AI, HPC, and Rendering capabilities available in  Intel’s software portfolio that is driven by oneAPI.

Read more on govindhtech.com

[myprogrammingsolver]

Lab 1 : Python Introduction

import numpy as np Q1) [20 Marks] Random Number Generation Using commands np.random.randint and np.random.rand, generate: 100 random integers in the interval −10 to 10. uniform random numbers in the interval [0, 1]. Q2) [40 Marks] Operations with Vectors [20 Marks]: Write a function which accepts integer n as input and outputs a data set of n points of form (xi, yi)ni=1 in the 2-dimensional…

[data-science-lovers]

!! Numpy Random Module !!

[programmingsolver]

Lab 1 : Python Introduction

  import numpy as np   Q1) [20 Marks] Random Number Generation   Using commands np.random.randint and np.random.rand, generate:   100 random integers in the interval −10 to 10.   uniform random numbers in the interval [0, 1]. Q2) [40 Marks] Operations with Vectors   [20 Marks]: Write a function which accepts integer n as input and outputs a data set of n points of form (xi, yi)ni=1 in the…

[greysxtra]

Random float netlogo

#Random float netlogo Patch#

The cookies is used to store the user consent for the cookies in the category "Necessary". This cookie is set by GDPR Cookie Consent plugin. The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". The cookie is used to store the user consent for the cookies in the category "Analytics". These cookies ensure basic functionalities and security features of the website, anonymously. Necessary cookies are absolutely essential for the website to function properly. (Some of these commands will not execute successfully.) print 2+2 print 2 + 2 let x -2 print x let x -2 print x let x -2 print -x let x -2 print – x let x -2 print (- x) Enter each of the following lines in the Command Center, in the order presented. How to start NetLogo with let and printcommands? The color reported may be only an approximation, since the NetLogo color space does not include all possible colors. All three inputs should be in the range 0 to 255. Reports a number in the range 0 to 140, not including 140 itself, that represents the given color, specified in the RGB spectrum, in NetLogo’s color space. What is the correct color range for NetLogo? If you want a floating point answer, you must now use random-float instead. Note: In versions of NetLogo prior to version 2.0, this primitive reported a floating point number if given a non-integer input. When to use floating point answer in NetLogo? If number is zero, the result is always 0 as well. If number is negative, reports a random integer less than or equal to 0, but strictly greater than number. If number is positive, reports a random integer greater than or equal to 0, but strictly less than number.

#Random float netlogo Patch#

(The patch variable begins with “p” so it doesn’t get confused with the turtle variable, since turtles have direct access to patch variables.) When does NetLogo report a positive random integer? For example, all turtles and links have a color variable, and all patches have a pcolor variable. How do you declare a variable in NetLogo? It’s easy and the results are immediate and visible – one of NetLogo’s many strengths. Also, inside the move-turtles procedure you can try changing right random 360 to right random 45. You can use the random.uniform(a, b) function to generate a pseudorandom floating-point number n such that a pen-down into the Command Center and then pressing the go button. How do I generate random float numbers in Numpy? To get a random number between 0.0 and 1.0, first cast the int return by rand() to a float, then divide by RAND_MAX. Rand() return a int between 0 and RAND_MAX. How do you generate a random floating-point number? Most often, globals is used to define variables or constants that need to be used in many parts of the program. Global variables are “global” because they are accessible by all agents and can be used anywhere in a model. For example, It can generate a random float number between 10 to 100 Or from 50.50 to 75.5. uniform() function returns a random floating-point number between a given range in Python. Uniform() to get a random float number within a range. If number is zero, the result is always 0. By using random-float, we are ensuring that the dart could land at every possible point on the dartboard, not just integer points.Random-float number If number is positive, reports a random floating point number greater than or equal to 0 but strictly less than number. In the model example below, we use random-float to randomly place a dart somewhere on (or off of) a dartboard. The highest number random-float could report is 4.999999.

Because random-float will return a number between 0 and (n-1), random-float 5.0 could report 0.0, but it could never report 5.0.

In the common case where you want to get a random value that is somewhere in between the minimum and maximum x or y coordinates, you can use the procedures random-xcor and random-ycor, which also report back non-integer values.

For example, if we wanted to generate a random floating point number between 4 and 7, we would write the following code: 4 + random-float 3.

If you want to generate a random number between a custom range, you can use the following format: minnumber + (random-float (maxnumber - minnumber)).

Things to keep in mind when using random-float: For example, if we wanted to create a model where we wanted to have people with various heights, we could write the following code, which would make each person have a random height between 5 feet to 7 feet: create-people 100 [ random-float is very useful in modeling phenomena that require continuous numbers. Random-float is a mathematics primitive that reports a random floating point number anywhere between 0 and the given number.

[thedatasciencehyderabad]

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.

Navigate to Address: 360DigiTMG - Data Analytics, Data Science Course Training Hyderabad 2-56/2/19, 3rd floor,, Vijaya towers, near Meridian school,, Ayyappa Society Rd, Madhapur,, Hyderabad, Telangana 500081 099899 94319

[nicaurybenitezcortorreal-blog]

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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