Additional information
by Michael Krauss (Author)
This book is for anyone who would like to learn how to develop machine-learning systems. We will cover the most important concepts about machine learning algorithms, in both a theoretical and a practical way, and we'll implement many machine-learning algorithms using the scikit-learn library in the python programming language. In the first chapter, you'll learn the most important concepts of machine learning, and, in the next chapter, you'll work mainly with the classification. In the last chapter you'll learn how to train your model. I assume that you've knowledge of the basics of programming.
What you'll learn:
- What is machine learning
- What is supervised, unsupervised, and reinforcement learning
- How to use the numpy and pandas library
- How to use matplotlib to plot charts
- What is the scikit-learn library?
- What do the fit() and transform() methods do
- How to pre-process our data
- How to use pipelines and column transformers to streamline our code
- How to evaluate our models
Machine learning is the way of the future - and breaking into this highly lucrative and ever-evolving field is a great way for your career, or business, to prosper. Inside this guide, you'll find simple, easy-to-follow explanations of the fundamental concepts behind machine learning, from the mathematical and statistical concepts to the programming behind them.
