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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

9 Free Data Science Books to Read in 2021

Abhiraj Suresh
Abhiraj Suresh Last Updated : 22 Dec, 2020
5 min read

Overview

  • Here are 9 free data science books to get started and upgrade yourself on various fronts
  • By no means is this an exhaustive list. Feel free to add more free data science books in the comments below

 

Introduction

“Data indeed is the new oil”

When I heard this for the first time many years ago, I mocked and ignored the statement. And, I am pretty sure many people like me are now thinking about how accurate this statement was.

Currently, Data science has taken over all the industries without leaving any stones unturned. Every firm is trying to leverage a panoply of data at each and every step of its operations to obtain ultimate efficiency. It only makes sense for people to familiarize themselves with at least basic algorithms and tools to analyze the data in their respective domains to better understand the trends and in turn, make better decisions.

And if you are already in your data science journey, you must have realized how important it is to upgrade yourself and practically implement complex algorithms for better results.

But like every time, you always wonder where to begin. That’s where I come to your rescue. In this article I am sharing 9 of the top free Data Science Books people must add to their list by the end of 2020.

 

 

1. Statistical Learning with Sparsity: The Lasso and Generalizations by Trevor Hastie, Robert Tibshirani, Martin Wainwright

The book includes all the major branches of statistical learning. For each topic, the authors first give a concise introduction of the basic problem, evaluate conventional methods, pointing out their deficiencies, and then introduce a method based on sparsity.

free data science books - statistical learning with sparsity

It always first discusses regularized models based on equations, followed by example applications, before ending with a bibliography section detailing the historical development of the given method.

You can download the book from here.

 

2. The Field Guide to Data Science by Booz Allen Hamilton

This book built by several hands by Booz Allen Hamilton employees introduces the theme of Data Science, presents the tools necessary to work with the area, and expands the background a little. It basically works as an introduction to the subject, but it is very well written, with infographics and illustrations that are especially creative. And there is a section that should be printed by everyone working in the field, a guide on how to choose the right technique for each piece of the problem.

free data science books -Booz Allen Field Guide to Data Science by [Booz Allen Hamilton]

You can download the book from here.

 

3. An Introduction to Statistical Learning by Gareth M. James, Daniela Witten, Trevor Hastie, Robert Tibshirani

An all-time classic. This book is recommended or referenced in most machine learning courses I’ve come across, it’s just that well written. It covers basic statistics as well as machine learning techniques.

ISLR

The awesome thing about this book is that each concept is explained with case studies in R. So once you have a handle on programming, you can always come back and try out each concept again. What better way to ingrain a concept than by practicing it multiple times?

You can download the book from here.

4. Convex Optimization 1st Edition by Stephen Boy

This is book Is not for beginners. The book introduces you to the concept of Convex Optimization which is used by almost all Machine Learning and Deep Learning Algorithms to reach the optimal parameters.

convex optimization

This is an appropriate book for someone looking to enter the world of machine learning through optimization. This is another approach apart from the journey via statistics.

You can download the book from here.

 

5. Data Mining and Analysis by Mohammed J. Zaki and Wagner Meira, Jr

The book provides a very good primer on the mathematical background for data mining and foundational statistical machine learning. This textbook provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification.

Data Mining and Analysis

The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks.

You can download the book from here

 

6. Data Science for Business by Tom Fawcett and Foster Provost

The book introduces the fundamental principles of data science and walks you through the “data-analytic thinking” necessary for extracting useful knowledge and business value from the data you collect.

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

This book is recommended for beginners and intermediate professionals who would like to learn data analytics without the usage of mathematics.

You can download the book from here.

 

7. Deep Learning (Adaptive Computation and Machine Learning series) by IAN Goodfellow

If a man like Elon Musk says that this is the most comprehensive book on the topic, we don’t think you need to refer to any further sources for the topic.

Deep learning ian goodman

The topics in the book are comprehensive, it covers almost every edge related to deep learning, the authors made a good justification in covering some of the intricate topics that need close attention that isn’t so readily available on the internet.

You can download the book from here.

 

 

8. Deep Learning with Python by Francois Chollet

deep learning with python

Is there even an alternative way to learn Deep Learning than from learning through the book written by the Keras creator and Google AI Researcher Francois Chollet? The book deep dives into Deep Learning and teaches concepts by practically implementing in python in Python.

I also recommend following Francois on Twitter – there is a lot we can learn from him.

You can download the book from here.

 

9. Artificial Intelligence: Foundations of Computational Agents,  2nd Edition by David Poole,
Alan Mackworth

This is a book about the science of artificial intelligence (AI). It presents artificial intelligence as the study of the design of intelligent computational agents.

AIFCA cover

The book balances theory and experiment, showing how to link them intimately together. It develops the science of AI together with its engineering applications.

You can download the book from here.

 

End Notes

This article introduces you to free data science books that can help you kick start or upgrade your current position in your data science journey.

We recommend you to check out the list of books that can help you begin a career in Business analytics too-

Feel free to add other books in the comments that you think should have made the list.

 

 

free data science books
Abhiraj Suresh
Abhiraj Suresh

My name is Abhiraj. I am currently a manager for the Instruction Design team at Analytics Vidhya. My interests include badminton, voracious reading, and meeting new people. On a daily basis I love learning new things and spreading my knowledge.

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Responses From Readers

Clear

Rakesh Kumar
Rakesh Kumar

Wonderful, very handy and will help a lot to many. Thanks!

123

Praveen
Praveen

Thanks for sharing! Few links are books on Amazon, so not free.

123

Pascal
Pascal

Some of the links point to Amazon with no option for free access. The islr book has no links but is very worthwhike indeed. Impressive list nevertheless

123

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