https://github.com/bluelord/intro_to_ml_python

Introduction to Machine Learning with Python (notes & practice codes).
https://github.com/bluelord/intro_to_ml_python

book machine-learning notes practice

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Introduction to Machine Learning with Python (notes & practice codes).

• Host: GitHub
• URL: https://github.com/bluelord/intro_to_ml_python
• Owner: Bluelord
• License: mit
• Created: 2020-09-21T07:12:20.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-04-30T15:19:18.000Z (over 4 years ago)
• Last Synced: 2024-12-30T22:29:04.944Z (12 months ago)
• Topics: book, machine-learning, notes, practice
• Language: Jupyter Notebook
• Homepage:
• Size: 1.57 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Intro_ML_Python_Notes

This repository provides the notebooks which holds the notes along with the practic codes form the book "Introduction to Machine

Learning with Python" by [Andreas Mueller](http://amueller.io) and [Sarah Guido](https://twitter.com/sarah_guido).

You can find details about the book on the [O'Reilly website](http://shop.oreilly.com/product/0636920030515.do).

"This book is a fantastic, super-practical resourse for anyone who wants to start using machine learning in pyhton- I just wish that it had existed when I started using scikit-learn!"         -**Hanna Wallach**

This book has a additional library [mglearn](https://github.com/amueller/mglearn) as helper functions to create figures and

datasets. If you get ``ImportError: No module named mglearn`` you can try to install mglearn into your python environment. All datasets are included in the repository.

## Introduction to Machine Learning with Python

![](./Images/IntroML_cover.jpg)

This book is for Machine learnig practitioners looking to implement solution to the real-world ML problems, this book doesn't require any previous knowledge of ML, but it is understood that reader of the book has prior knowledge of linear algebra and probabilities though this book doesn't go indeapth analysis of algoritms.

### Navigation of this book:

- **Chapter 1:** Introduces the fundamental concepts of machine learning and its applications.

- **Chapters 2 & 3:** Describes the actual machine learning algorithms that are most widely used in practice,

and discuss their advantages and shortcomings.

- **Chapter 4:** Discusses the importance of how we represent data that is processed by machine learning.

- **Chapter 5:** Covers advanced methods for model evaluation and parameter tuning.

- **Chapter 6:** Explains the concept of pipelines for chaining models and encapsulating the workflow.

- **Chapter 7:** Shows how to apply the methods described in earlier chapters to text data, and introduces some text-specific processing techniques.

- **Chapter 8:** Offers a high-level overview, and includes references to more advanced topics.