Supervised Machine Learning with Python

▶What You Will Learn
- Crack how a machine learns a concept and generalize its understanding to new data
- Uncover the fundamental differences between parametric and non-parametric models
- Implement and grok several well-known supervised learning algorithms from scratch
- Work with models in domains such as ecommerce and marketing
- Expand your expertise and use various algorithms such as regression, decision trees, and clustering
- Build your own models capable of making predictions
- Delve into the most popular approaches in deep learning such as transfer learning and neural networks

▶Key Features
- Delve into supervised learning and grasp how a machine learns from data
- Implement popular machine learning algorithms from scratch, developing a deep understanding along the way
- Explore some of the most popular scientific and mathematical libraries in the Python language

▶Who This Book Is For
This book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming―and some fundamental knowledge of supervised learning―are expected.

▶What this book covers
- Chapter 1, First Step toward Supervised Learning, covers the basics of supervised machine learning to get you prepared to start tackling problems on your own. The chapter comprises four important sections. First, we will get our Anaconda environment set up and make sure that we are able to run the examples. Over the next couple of sections following that, we will cover a bit more of the theory behind machine learning, before we start implementing algorithms in the final section, where we'll get our Anaconda environment set up.

- Chapter 2, Implementing Parametric Models, dives into the guts of several popular supervised learning algorithms within the parametric modeling family. We'll start this section by formally introducing parametric models, then we'll focus on two very popular parametric models in particular: linear and logistic regression. We'll spend some time understanding the inner workings and then jump into Python and actually code them from scratch.

- Chapter 3, Working with Non-Parametric Models, explores the non-parametric model family. We will start by covering the bias-variance trade-off, and explain how parametric and nonparametric models differ at a fundamental level. We will then get into decision trees and clustering methods. Finally, we'll address some of the pros and cons of non-parametric models.

- Chapter 4, Advanced Topics in Supervised ML, splits its time between two topics: recommender systems and neural networks. We'll start with collaborative filtering and then talk about integrating content-based similarities into your collaborative filtering systems. Finally, we'll get into neural networks and transfer learning.