Book Image

Python Machine Learning By Example

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example

By: Yuxi (Hayden) Liu

Overview of this book

Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.
Table of Contents (9 chapters)

Getting Started with Python and Machine Learning

We kick off our Python and machine learning journey with the basic, yet important concepts of machine learning. We will start with what machine learning is about, why we need it, and its evolution over the last few decades. We will then discuss typical machine learning tasks and explore several essential techniques of working with data and working with models. It is a great starting point of the subject and we will learn it in a fun way. Trust me. At the end, we will also set up the software and tools needed in this book.

We will get into details for the topics mentioned:

  • What is machine learning and why do we need it?
  • A very high level overview of machine learning
  • Generalizing with data
  • Overfitting and the bias variance trade off
    • Cross validation
    • Regularization
  • Dimensions and features
  • Preprocessing, exploration, and feature engineering
    • Missing Values
    • Label encoding
    • One hot encoding
    • Scaling
    • Polynomial features
    • Power transformations
    • Binning
  • Combining models
    • Bagging
    • Boosting
    • Stacking
    • Blending
    • Voting and averaging
  • Installing software and setting up
  • Troubleshooting and asking for help