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Format: pdf
Size: 1.10 MB

Introducing: “Machine Learning Algorithms From Scratch“
This is the book that I wish I had when starting out.


It is designed for exactly the way developers like you learn.

The book works through how to write small functions to load data and prepare it for learning.

There are tutorials on how to evaluate predictions and evaluate the performance of machine learning models.


Then there’s a suite of tutorials on how to implement linear, nonlinear and even ensemble machine learning algorithms from scratch.

Each tutorial is written in Python. This is the growing and soon to be the dominant programming language for applied machine learning and data science.
Each tutorial uses the standard library. There is no NumPy, no SciPy, no Scikit-Learn and no other advanced libraries to hide the details.
Each tutorial is standalone. Everything you need to understand and run an algorithm is right there, no flipping back and forth through the book to piece it together yourself.
Each tutorial is super simple. Code does not use fancy Python tricks that are hard to read and even harder to understand. I’ve opted for simple loops and the simplest data structures to help even Python novices see exactly what is going on.
Each tutorial actually works. Each function is added one at a time with full explanation and spot testing. Nothing is sprung on you and all the code works with sample output for you to compare to.

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