How_to_learn_modern_Machine_Learning

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How_to_learn_modern_Machine_Learning

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A guide to the adventurer

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# How to learn modern Machine Learning
**A guide to the adventurer** 
First of all basic machine learning can be simple to understand, but to understand it deeply and see were the rabbit hole goes you have to do some serious kind of study. The plan to enlightenment has multiple parallel path’s, but books, internet info, youtube video channels, free online courses, constitute the main pillars.

## Books
I’m suggesting that you read the books from cover to cover and not only for reference.

### Most important books
The first book that you should read is a cheap book but a very good one. It summarizes in a small amount of pages what is the bulk of machine learning. 
 
**The Hundred-Page Machine Learning Book** 
by Andriy Burkov 
Pag. 160

**Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition** 
by Aurélien Géron 
Pag. 856 
Note: If you can only have one book read this one.

**Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition** 
by Sebastian Raschka 
Pag. 748

**Python Data Science Handbook: Essential Tools for Working with Data** 
by Jake VanderPlas 
Pag. 548

**Deep Learning with Python** 
by Francois Chollet 
Pag. 384 
Note: From the author of Keras.

**Deep Learning with PyTorch** 
by Eli Stevens, Luca Antiga 
Pag. 450

**Data Science and Machine Learning: Mathematical and Statistical Methods** 
by Dirk Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman 
Pag. 532

### Good second line books
**An Introduction to Statistical Learning: with Applications in R** 
by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani 
Pag. 426 
[Free book online](http://faculty.marshall.usc.edu/gareth-james/ISL/) 
Note: It will also make you understand a little bit of R. And it's a really good book to introduce you to the mathematical rigor of machine learning.

**Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition** 
by Wes McKinney 
Pag. 550 
Note: From the author of Pandas.

**Machine Learning: An Algorithmic Perspective, 2nd Edition** 
by Stephen Marsland 
Pag. 457 
Note: You will understand how to program all those algorithms from scratch.

**Data Science from Scratch: First Principles with Python 2nd Edition** 
by Joel Grus 
Pag. 406 
Note: You will understand how to program all those algorithms from scratch.

**Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning** 
by Chris Albon 
Pag. 366 
Note: A cookbook book of recipes.

**Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD** 
by Sylvain Gugger and Jeremy Howard 
Pag. 350 
Note: From the author of FastAI Course and Framework.

**TinyML: Machine Learning with TensorFlow on Arduino and Ultra-Low Power Micro-Controllers** 
by Pete Warden, Daniel Situnayake 
Pag. 520 
Note: Machine Learning on Microcontrollers and embedded software.

**Grokking Deep Learning** 
by y Andrew Trask 
Pag. 336 
Note: Helps you understand Deep Learning from the basics in a simple way.

**Deep Learning** 
by Ian Goodfellow, Yoshua Bengio, Aaron Courville 
Pag. 800 
Note: It has a good introduction to Linear Algebra, Probability, Calculus and Optimization. You will need it to understand the text because it is of a high mathematical nature. Made by some of the inventors of deep learning. 
[Free book online](http://www.deeplearningbook.org)

**Dive into Deep Learning** 
by Aston Zhang, Zack Lipton, Mu Li, Alex Smola 
Pag. 902 
[Free book online](https://d2l.ai/)

**The Elements of Statistical Learning: Data Mining, Inference, and Prediction, second Edition** 
by Trevor Hastie, Robert Tibshirani, Jerome Friedman 
Pag. 745 
[Free book online](https://web.stanford.edu/~hastie/ElemStatLearn/)

**Practical Statistics for Data Scientists: 50 Essential Concepts** 
by Peter Bruce, Andrew Bruce 
Pag. 318

**Pattern Recognition and Machine Learning** 
by Christopher M. Bishop 
Pag. 738 
[Free book online](https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/)

### Reinforcement Learning
**Reinforcement Learning: An Introduction second Edition** 
by Richard S. Sutton, Andrew G. Barto 
Pag. 552 
[Free book online](http://incompleteideas.net/book/the-book.html)

**Deep Reinforcement Learning Hands-On - Second Edition: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimisation, web automation and more** 
by Maxim Lapan 
Pag. 777

### Global view on Machine Learning field from several fronts
**The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World** 
by Pedro Domingos 
Pag. 352

### Biological inspired algorithms
**Clever Algorithms: Nature-Inspired Programming Recipes** 
By Jason Brownlee PhD 
Pag. 438 
[Free book online](http://cleveralgorithms.com/nature-inspired/index.html)

### Programming
**Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming** 
by Eric Matthes 
Pag 544

### Mathematics
**Summary - Mathematics for Machine Learning** 
by Garrett Thomas 
Pag. 47 
[Free book online](https://gwthomas.github.io/docs/math4ml.pdf)

**Mathematics for Machine Learning** 
by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong 
Pag. 398 
[Free book online](https://mml-book.github.io/)

**Engineering Mathematics** 
by Prof Anthony Croft, Dr Robert Davison 
Pag. 1024

**Higher Engineering Mathematics, 8th edition** 
by John Bird 
Pag. 924

**Linear Algebra: Step by Step** 
by by Kuldeep Singh 
Pag. 528

**Introduction to Linear Algebra, 5th Edition** 
by Gilbert Strang 
Pag. 584 
Note: There is a excellent MITOpenCourse from this professor that follows this book.

**All of Statistics: A Concise Course in Statistical Inference** 
by Larry A. Wasserman 
Pag. 538

**Information Theory, Inference and Learning Algorithms** 
by David J. C. MacKay 
Pag. 640 
[Free book online](https://www.inference.org.uk/itprnn/book.html)

**Algorithms for Optimization** 
by Mykel J. Kochenderfer, Tim A. Wheeler 
Pag. 520

**Convex Optimization** 
by Stephen Boyd, Lieven Vandenberghe 
Pag. 727 
[Free book online](https://web.stanford.edu/~boyd/cvxbook/)

## Sites
* [Python Anaconda Distribution](https://www.anaconda.com/distribution)
* [Visual Studio Code](https://code.visualstudio.com)
* [SciPy Numerical Python, NumPy, Jupyter lab and notebooks, Pandas, MatPlotLib ](https://scipy.org)
* [scikit-learn Machine Learning in Python](https://scikit-learn.org/stable)
* [Tensor Flow 2.0 Deep Learning](https://www.tensorflow.org)
* [PyTorch Deep Learning](https://pytorch.org)
* [XGBoost One of the best Tree algorithms](https://xgboost.readthedocs.io/en/latest)
* [Kaggle competitions](https://www.kaggle.com)
* [FastAI Courses and Lib](https://www.fast.ai)
* [Khan Academy](https://www.khanacademy.org)
* [Land on Vector Spaces: Practical Linear Algebra with Python](https://github.com/engineersCode/EngComp4_landlinear)
* [Introduction to Numerical Computing with NumPy](https://github.com/enthought/Numpy-Tutorial-SciPyConf-2019/blob/master/slides.pdf)
* [Tools - Math - Linear Algebra](https://github.com/ageron/handson-ml2/blob/master/math_linear_algebra.ipynb)
* [Tools – NumPy](https://github.com/ageron/handson-ml2/blob/master/tools_numpy.ipynb)
* [Tools – pandas](https://github.com/ageron/handson-ml2/blob/master/tools_pandas.ipynb)
* [Tools – matplotlib](https://github.com/ageron/handson-ml2/blob/master/tools_matplotlib.ipynb)
* [Eloquent Arduino - Microcontrollers Machine Learning](https://eloquentarduino.github.io)
* [GitHub MicroML - from Eloquent Arduino - SVMs](https://github.com/eloquentarduino/micromlgen)

## Youtube Channels
* [Lex Fridman](https://www.youtube.com/user/lexfridman/featured)
* [Siraj Raval](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/featured)
* [Python Programmer](https://www.youtube.com/user/consumerchampion/featured)
* [TensorFlow](https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ/featured)
* [Two Minute Papers](https://www.youtube.com/user/keeroyz/featured)
* [3Blue1Brown](https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw/featured)
* [Microsoft Research](https://www.youtube.com/user/MicrosoftResearch/featured)
* [Derek Banas Programming Tutorials](https://www.youtube.com/user/derekbanas/featured)
* [Jeremy Howard](https://www.youtube.com/user/howardjeremyp/featured)
* [The Artificial Intelligence Channel](https://www.youtube.com/user/Maaaarth/featured)
* [mathematicalmonk](https://www.youtube.com/user/mathematicalmonk/featured)
* [Getting Started with JupyterLab 2019](https://www.youtube.com/watch?v=RFabWieskak)

## Have fun!
Best regards, 
Joao Nuno Carvalho

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