What this book covers

Chapter 1, Getting Started with Machine Learning, teaches the main concepts of machine learning.

Chapter 2, Classification – Decision Tree Learning, builds our first machine learning application.

Chapter 3, K-Nearest Neighbors Classifier, continues exploring classification algorithms, and we learn about instance-based learning algorithms.

Chapter 4, K-Means Clustering, continues with instance-based algorithms, this time focusing on an unsupervised clustering task.

Chapter 5, Association Rule Learning, explores unsupervised learning more deeply. 

Chapter 6, Linear Regression and Gradient Descent, returns to supervised learning, but this time we switch our attention from non-parametric models, such as KNN and k-means, to parametric linear models.

 Chapter 7, Linear Classifier and Logistic Regression, continues by building different, more complex models on top of linear regression: polynomial regression, regularized regression, and logistic regression.

Chapter 8, Neural Networks, implements our first neural network.

Chapter 9, Convolutional Neural Networks, continues NNs, but this time we focus on convolutional NNs, which are especially popular in the computer vision domain.

Chapter 10, Natural Language Processing, explores the amazing world of human natural language. We're also going to use neural networks to build several chatbots with different personalities.

Chapter 11, Machine Learning Libraries, overviews existing iOS-compatible libraries for machine learning. 

Chapter 12, Optimizing Neural Networks for Mobile Devices, talks about deep neural network deployment on mobile platforms.

Chapter 13, Best Practices, discusses a machine learning app's life cycle, common problems in AI projects, and how to solve them.