Ready to add Machine Learning to your skill stack?As the second title in the Machine Learning From Scratch series, this book teaches you how to code machine learning models in Python.

By working on different projects with repeatable steps, you will have the blueprints and the effective strategies to code and design prediction models using your own data.

Who is this book for?The book is designed for beginners with basic background knowledge of machine learning, including common algorithms such as logistic regression and decision trees. For a gentle explanation of machine learning theory minus the code, we suggest reading the first book in this series Machine Learning for Absolute Beginners (Third Edition), which is written for a more general audience.

In this step-by-step guide you will learn: - How to code a machine learning prediction model using a range of algorithms including logistic regression, gradient boosting, and decision trees.
- How to install a development environment and use the programming language Python to code 10 different models.
- How to write your model in the least amount of code possible with the help of Pandas, Scikit-learn, Matplotlib, and Seaborn.
- How to visualize relationships in your dataset including Heatmaps and Pairplots with just a few lines of code.

Ready to add Machine Learning to your skill stack?As the second title in the Machine Learning From Scratch series, this book teaches you how to code machine learning models in Python.

By working on different projects with repeatable steps, you will have the blueprints and the effective strategies to code and design prediction models using your own data.

Machine Learning with Python: A Practical Beginners' Guide