Book Image

Python Machine Learning By Example - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
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Predicting Stock Prices with Artificial Neural Networks

Continuing the same project of stock price prediction from the last chapter, in this chapter I will introduce and explain neural network models in depth. We will start by building the simplest neural network and go deeper by adding more layers to it. We will cover neural network building blocks and other important concepts, including activation functions, feedforward, and backpropagation. We will also implement neural networks from scratch with scikit-learn and TensorFlow. We will pay attention to how to learn with neural networks efficiently without overfitting, utilizing dropout and early stopping techniques. Finally, we will train a neural network to predict stock prices and see whether it can beat what we achieved with the three regression algorithms in the previous chapter.

We will cover the following topics in this chapter:

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