Introduction to Machine Learning with Python: A Guide for Data Scientists

Category: Computer Science
Author: Sarah Guido

About This Book

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.


With this book, you’ll learn:

  • Fundamental concepts and applications of machine learning
  • Advantages and shortcomings of widely used machine learning algorithms
  • How to represent data processed by machine learning, including which data aspects to focus on
  • Advanced methods for model evaluation and parameter tuning
  • The concept of pipelines for chaining models and encapsulating your workflow
  • Methods for working with text data, including text-specific processing techniques
  • Suggestions for improving your machine learning and data science skills


by anonymous   2017-11-20
Recommended: [_Intro to ML with Python_]( by Andreas C. Mùˆller & Sarah Guido. (Andreas is a core sklearn developer.) The section on PCA gives a very clear walkthrough.
by mindcrime   2017-10-31
What? No. Why in the world do people even ask this kind of question. To a first approximation, the answer to "is it too late to get started with ..." question is always "no".

If no, what are the great resources for starters?

The videos / slides / assignments from here:

This book:

This book:

These books:

This book:

These subreddits:

These journals:

This site:

Any tips before I get this journey going?

Depending on your maths background, you may need to refresh some math skills, or learn some new ones. The basic maths you need includes calculus (including multi-variable calc / partial derivatives), probability / statistics, and linear algebra. For a much deeper discussion of this topic, see this recent HN thread:

Luckily there are tons of free resources available online for learning various maths topics. Khan Academy isn't a bad place to start if you need that. There are also tons of good videos on Youtube from Gilbert Strang, Professor Leonard, 3blue1brown, etc.

Also, check out Doing Kaggle contests can be a good way to get your feet wet.

And the various Wikipedia pages on AI/ML topics can be pretty useful as well.

by anonymous   2017-09-11
You can start with [Introduction to ML with Python]( if you want to see overview of some basic things in ML and try them in practice .