Grab These Books for Free to Learn Data Science and Machine Learning Like an Expert
It’s not just you. Or me. Everyone wants to learn data science and machine learning. Data Science and Machine learning are two of the most in-demand technologies in today’s world. Over the last few years, data science has become one of the highest-paid fields for professionals in the IT industry. As we see, more and more businesses around the world are adopting these latest technologies, and also there is a growing demand for skilled data scientists and machine learning engineers.
Therefore, if you plan to make a successful career in this field, or already a data scientist who wants to remain on top of your game, here is the list of books for you to remain on top of your game — always!
1. Learn Python 3 The Hard Way
Author — Zed Shaw
This book is perfect for absolute beginners or for those who have zero programming knowledge. This book is also good for those professionals who haven’t written a code in years. You will learn Python by doing 52 excellent exercises given in this book. This covers a lot of topics such as installing a complete Python environment, variables, strings, object-oriented programming, and many others.
Grab your copy here or download the pdf version here.
2. Python Data Science Handbook
Author — Jake VanderPlas
For many data scientists and machine learning engineers, Python is the number one choice when it comes to solving crucial data science problems. Python comes in handy mainly because of its libraries for getting, cleaning, and gaining valuable insights from data. This book is great for those who have already mastered the basics of Python and are ready to explore and work with various Python libraries. While many resources exist to explore these libraries, but only the Python Data Science Handbook has them all — including IPython and Jupyter, NumPy, Pandas, Matplotlib, and Scikit- Learn.
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3. Introduction to Machine Learning with Python: A Guide for Data Scientists
Author(s) — Andreas C. Müller and Sarah Guido
This book is a great option for those who want to kickstart their journey into the fascinating world of machine learning. Written by Andreas C. Müller and Sarah Guido, this book provides a clear explanation of fundamental concepts in both data science and machine learning. You don’t need to have any prior knowledge of Python, machine learning, and data science. This book covers fundamental as well as advanced techniques for collecting your data, choosing the right model, suggestions for improving your data science and machine learning skills, and many more things.
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4. Python Machine Learning
Author(s) — Sebastian Raschka and Vahid Mirjalili
Machine learning is one of the fastest-growing fields. This book will help you understand the tools and techniques of machine learning, neural networks, and deep learning. This book is for advanced learners. This book includes the latest open source technologies and frameworks such as Scikit-Learn, Keras, and TensorFlow. This offers the practical knowledge you need to create an effective machine learning and deep learning applications in Python. The 3rd edition of this book includes TensorFlow 2, GANs, and Reinforcement Learning.
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5. Practical Statistics for Data Scientists
Author(s) — Peter Bruce, Andrew Bruce, and Peter Gedeck
This book is also ideal for those who are just starting their journey. Statistical analysis is also a key part of data science and machine learning, and this book covers all the basic concepts of it. Using this book, you will learn, why EDA or exploratory data analysis is important and how to approach it. It also gives an insight into how random sampling can reduce bias, regression analysis, and how to detect anomalies, and statistical machine learning methods. Other than these concepts, this book has code examples in both R and Python, which makes this book a great source to learn statistics.
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6. Python for Data Analysis
Author — Wes McKinney
Written by Wes McKinney, creator of the Python Pandas project, this book gives a modern introduction to the concepts of data analytics. Apart from data science and machine learning, Python is also the first choice for Data Analytics. This book covers every method used for data analysis along with the basics of the Python language. The book includes — use of Ipython and Jupyter notebook for EDA, NumPy, data analysis with Pandas, data visualization with Matplotlib, and much more. The author gives you some idea of what you should be doing while working as a data analyst.
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7. Data Science from Scratch
Author — Joel Grus
This book is also a great option for those who are getting started in data science and machine learning. This book not only helps you to understand data science tools — data science libraries, frameworks but also helps you to understand the important concepts underlying them. This book helps you get comfortable with math and statistics at the core of data science. This book includes all the material on deep learning, statistics, and natural language processing.
Grab your copy here or download the pdf version here.
8. The Hundred-Page Machine Learning Book
Author — Andriy Burkov
This book is a must-read for those who want to understand what exactly machine learning is. You could finish this book in a day, or two. This book is suitable for everyone. Whether you are just starting in the field or you are an individual with some experience, this book is for everyone. This will not teach you everything about machine learning, but this will give you an idea about machine learning. Also, this book gives you an introduction to Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Reinforcement Learning, regression techniques, and some machine learning algorithms.
Grab your copy here or download the pdf version here.
9. R for Data Science
Author(s) — Hadley Wickham and Garrett Grolemund
R is another popular programming language for data science and machine learning. For those who have already worked on Python, the next step is to learn how to implement data science applications in R. R for Data Science is the perfect book to learn coding in R. You will learn how to use R to turn raw data into meaningful insights. This book will teach you how to install R, RStudio, and R packages used to make data science fun.
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10. Machine Learning with R
Author — Brett Lantz
This book begins with a brief introduction to the R programming language. It also sheds some light on Vectors, Data Frames, and Factors, which helps the reader to refresh their knowledge in R. This book is ideal for those who want to explore how to perform machine learning with R and derive new insights from raw data. Whether you are just starting or have some experience, this book will get you up and running quickly.
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11. Hands-On Deep Learning with R
Author(s) — Michael Pawlus and Rodger Devine
R programming language is becoming quite popular when it comes to machine learning and data science. Hands-On Machine Learning with R, starts with a brief overview of what machine learning and deep learning is and how to build your very first neural network using R. You will learn how to build deep learning models, optimize hyperparameters using modern R libraries such as TensorFlow, H2O, and MXNet. This book also covers various deep learning applications such as image processing, natural language processing, and much more. You should at least have basic knowledge of machine learning and R programming concepts.
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12. Hands-On Machine Learning with Scikit–Learn and TensorFlow
Author — Aurelien Geron
This book is an absolute gem for those who want to learn data science and machine learning. This book is recommended for both beginners as well as experts to gain useful insights from raw data. The following topics are included in this book — machine learning fundamentals, Scikit-Learn, pandas, neural networks, TensorFlow to build, test, train, and deploy machine learning models, and much more. After reading this book, you will surely learn how to solve real-world problems.
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13. Deep Learning with Python
Author — François Chollet
Deep Learning with Python introduces readers to the field of deep learning using Python and the powerful Keras library. Written by Google’s AI researcher and the creator of the famous deep learning API Keras, this book helps you to understand deep learning from scratch, also how-to set-up your own deep learning environment. You will learn various concepts behind deep learning applications such as computer vision, and NLP. By the time you are done with this book, you will have a better understanding of the deep learning concepts you will be able to apply some of those to solve the real-world challenging questions.
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14. Deep Learning
Author(s) — Ian Goodfellow, Yoshua Bengio, and Aaron Courville
This book is hands down the best when it comes to learning deep learning algorithms. Deep learning is becoming quite famous and it is used to extract meaningful insights from a large amount of data, known as Big Data. This book is not heavily coded with deep learning algorithms but also explains how to approach deep learning problems. The writers have written this book by keeping two types of audiences in their minds — one of these target audiences is university students learning about machine learning, including those who are beginning a career in deep learning and artificial intelligence research. The second target audience is software engineers who do not have a machine learning or statistics background but want to rapidly acquire one. This book covers a lot of ground — linear algebra, probability, numerical computation, deep feedforward networks, optimization for training deep models, convolutional networks, and much more.
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15. Pattern Recognition and Machine Learning
Author(s) — Christopher M. Bishop
If you have already read a few books on data science and machine learning, and if you want to further improve your skills in this domain, then this is the right book for you. This book dives deeper into machine learning algorithms and mathematics associates with them. This book expects you to have some prerequisite knowledge of calculus, probability, and a strong foundation in Python. Although this is the first book written on pattern recognition, it does not require you to have basic knowledge of the same.
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16. Natural Language Processing with Python
Author(s) — Steven Bird and Ewan Klein
This book is about natural language processing. Natural language means the language we use in our daily lives. It is a language mainly used for everyday communication by humans; languages such as — English, Hindi, or Spanish. NPL is growing rapidly. This book is aimed at those who are either new to programming or already programming in Python. This book will teach you how to extract unstructured texts, either to identify the texts or to do sentimental analysis. You will also learn how to gain practical experience in NPL using Python and its famous NLTK toolkit.
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17. The Art of Statistics: How to Learn from Data
Author — David Spiegelhalter
Statistics plays an important role while playing with data. This book allows readers to learn how to use the raw data to solve real-world problems while focusing on important math concepts. The writer guides the reader through the essential principles one needs to derive knowledge from data.
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Best free data science and machine learning books
18. An Introduction to Statistical Learning
Author(s): Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
This book is an excellent resource for everyone who does or does not have a strong mathematical background. This book gives you a good overview of the various concepts in statistical learning using the R programming language. Also, this book includes some of the most important modeling and prediction techniques, along with its relevant application in the domain. Topics include — linear regression, classification, SVMs, clustering, and much more. This book targeted all the statisticians and non-statisticians alike who want to use statistical methods to analyze their data and extract some valuable information.
Grab it for free here.
19. The Elements of Statistical Learning
Author(s) — Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Authored by some of the well-known professors of the renowned Stanford University, this book suggests that there is an enormous explosion of data in a variety of fields such as medicine, biology, finance, and marketing. This book is full of eye-catchy data visualizations and gives a lot of information on statistics, machine learning, data mining, and bioinformatics. Even though the book has a statistical approach, it covers a lot of concepts rather than just focusing on mathematics. This book covers a lot of topics such as supervised learning, unsupervised learning, random forests, ensemble methods, and much more.
Grab it for free here.
20. Think Bayes
Author — Allen Downey
When it comes to data science, Bayesian Statistics plays an important role that cant be avoided. This book makes it easy for readers to understand the important concepts behind Bayes. After reading this book, you will understand how to use Python to solve statistical problems. You should at least have some basic knowledge of Python and probability before going through this book.
Grab it for free here.
