by reashikaa verma, parallel dots
“what we want is a machine that can learn from experience“
You are reading: Free machine learning books
alan turing
There is no doubt that machine learning has become one of the most popular topics today. According to a study, machine learning engineer was voted one of the best jobs in the US. uu. in 2019.
In light of this trend, we’ve compiled a list of some of the best (and free) machine learning books that will prove useful to anyone aspiring to build a career in the field.
enjoy!
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1. islr
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best introductory book on machine learning theory. even paid books are rarely better. a good introduction to math, and also has practice material in r. I cannot praise this book highly enough.
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2. neural networks and deep learning
This free online book is one of the best and fastest introductions to deep learning out there. reading it takes only a few days and gives you all the basics about deep learning.
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3. pattern recognition and machine learning
is one of the most famous theoretical machine learning books, so you don’t need to write much introduction.
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4. deep learning book
The bible of deep learning, this book is an introduction to deep learning algorithms and methods that is useful for beginners and professionals alike.
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5. understanding machine learning: from theory to algorithms
very good treatise on machine learning theory.
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6. Seven Steps to Success: Machine Learning in Practice
Non-technical product managers and non-machine learning software engineers entering the field shouldn’t miss this tutorial. very well written (a bit old and doesn’t cover deep learning, but works for all practical purposes).
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7. machine learning rules: best practices for machine learning engineering
Wondering how Google thinks about its machine learning products? this is a very good machine learning product management tutorial.
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8. a brief introduction to machine learning for engineers
monologue covering almost all machine learning techniques. easier math to understand (for people who are afraid of difficult math notations).
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9. brief introduction to machine learning without deep learning
monologue covering almost all machine learning techniques. easier math to understand (for people who are afraid of difficult math notations).
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10. machine learning introductory notes
machine learning guide for absolute beginners.
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11. fundamentals of machine learning
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a detailed treatise on mathematical concepts of machine learning.
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12. an introduction to function and variable selection
Feature engineering and variable selection are probably the most important human contributions to traditional machine learning algorithms. (not so important in deep learning methods, but not everything is solved by deep learning). This tutorial provides an introduction to different feature engineering methods.
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13. automl book – frank hutter, lars kotthoff, joaquin vanschoren
traditional machine learning in recent days has really come down to running automatic models (h2o, auto sklearn or tpot, our favorite on paralleldots) once you’re done with feature engineering. (in fact, there are also some methods to do non-domain-specific automatic feature engineering). this book covers the methods used in automl.
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14. deep learning with pytorch
a free book that helps you learn deep learning using pytorch. pytorch is our favorite side-by-side deep learning library and we recommend it for everyone doing applied deep learning research/development.
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15. immerse yourself in deep learning
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another detailed book on deep learning using amazon’s mxnet library to teach deep learning.
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16. keras github notebooks
francois chollet is the leader of the keras library. his book “deep learning in python” written to teach deep learning in keras is highly rated. the book is not freely available, but all the code from it is available on github in the form of notebooks (forming a book with deep learning examples) and is a good resource. I read it when I was learning keras a few years ago, a very good resource.
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17. model-based machine learning
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an excellent resource on Bayesian machine learning. uses microsoft’s infer.net library to teach, so you may need to install ironpython to read/implement the examples in the book.
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18. Bayesian models for machine learning
another book detailing various Bayesian methods in machine learning.
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19. eisenstein’s nlp notes
natural language processing is the most popular use of machine learning. These gatetech course notes give a very good overview of how machine learning is used to interpret human language.
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20. reinforcement learning – sutton and barto
the bible of reinforcement learning. this is a must read for anyone interested in the field of reinforcement learning.
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21. Gaussian processes for machine learning
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teaches the use of Bayesian optimization and Gaussian processes for machine learning. with variational inference based libraries like edward/gpytorch/botorch etc, this method is making a comeback.
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22. machine learning interviews chip machine learning systems design flee
Do you want to interview for a machine learning job? These questions can help you discover strategy while answering machine learning system problems.
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23. algorithmic aspects of machine learning
This book deals with the parts of machine learning that deal with computational algorithms and numerical methods for solving factorization models, dictionary learning, and Gaussian models.
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24. causality for machine learning
With causality making inroads into data science fields, machine learning is not without discussion either. while no detailed material is available on this, here is a short tutorial that attempts to explain the key concepts of causality for machine learning.
Did you find the blog useful? read our other blog to learn all about the best books to help you excel as a data scientist.
originals. republished with permission.
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