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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Introducing sequential learning

The machine learning problems we have solved so far in this book have been time-independent. For example, ad click-through doesn't depend on the user's historical ad clicks under our previous approach; in face classification, the model only takes in the current face image, not previous ones. However, there are many cases in life that depend on time. For example, in financial fraud detection, we can't just look at the present transaction; we should also consider previous transactions so that we can model based on their discrepancy. Another example is part-of-speech (PoS) tagging, where we assign a PoS (verb, noun, adverb, and so on) to a word. Instead of solely focusing on the given word, we must look at some previous words, and sometimes the next words too.

In time-dependent cases like those just mentioned, the current output is dependent on not only the current input, but also the previous inputs; note that the length of the ...