In this introduction chapter, you’ll learn what machine learning is all about. You’ll touch on everything from, the difference between supervised and unsupervised learning, to what transfer learning is. You’ll even go over the ethics of machine learning, and how bias can affect models.

In this chapter, you’ll build your first iOS app by adding a CoreML model to detect whether a snack is healthy or unhealthy. You’ll focus on how machine learning can be used to solve classification problems such as trying to identify what an object might be.

In this chapter, you’ll start to build your first machine learning model using Create ML. You’ll be introduced to the dataset used to create the model, along with how Create ML uses transfer learning to get amazing classification results. Moreover, you’ll learn what it means to evaluate the performance of your model.

In this chapter, you’ll get a quick primer on Python. You’ll learn how to setup your Python environment using Conda, and how to install external libraries. You’ll learn how to run and use Jupyter notebooks to iterate quickly with Python.

In this chapter, you’ll learn to use Turi Create to build a classification model. You’ll use the snacks dataset to create your model. You’ll learn how to analyze your model’s performance, and how to go under the hood with Turi Create in order to improve your model.

In this chapter, you’ll learn to how to take control of your model’s training with Keras. You’ll design your first neural network, and how to pass your dataset into Keras for training.

In this chapter, you’ll learn why a simple neural network might not be enough when it comes to solving problems with images using machine learning. You’ll learn about how using a convolutional neural network provides a better approach to solving classification problems.

In this chapter, you’ll learn about advanced model architectures used for solving image classification. You’ll learn how you can use Keras to do transfer learning, and how applying advanced techniques such as dropout and regularization can improve your model’s performance.

In this chapter, you’ll learn how to identify an object’s location in an image. You’ll learn how to build a simple localization model that predicts a single bounding box.

In this final chapter, you’ll learn about some advanced localization models. You’ll learn about one-shot detectors like YOLO and SSD and how they can be used to identify multiple objects in an image. You’ll also learn about how machine learning can be used for segmentation to separate an object from its background.

In this chapter, you’ll learn how working with sequences differs from working with discrete data like individual images. You’ll learn how to collect iPhone sensor data, as well as what it takes to build a good training dataset.

In this chapter, you’ll learn about neural networks designed to work with sequences. You’ll also learn how to use Turi Create to train an activity classification model using data from the previous chapter.

In this chapter, you’ll learn how to pass real-time sequential data captured from a device’s motion sensors into your Core ML model. You’ll learn some tricks to help keep your apps responsive and accurate while processing sequences of streaming data.

In this chapter, you’ll learn how to use Apple’s Natural Language framework to handle several useful text-related tasks. You’ll explore this API in the context of a movie review app that supports multiple languages.

In this chapter, you’ll learn about sequence-to-sequence models and how you can use them to do things like language translation. You’ll build a model with Keras that attempts to translate Spanish-language movie reviews into English.

This chapter introduces additional techniques you can use to improve the performance of your sequence-to-sequence models.