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Machine Learning Algorithms

This is the code repository for Machine Learning Algorithms, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

About the Book

In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, XGBooster, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem.

Instructions and Navigation

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

The code will look like the following:

nn = NearestNeighbors(n_neighbors=10, radius=5.0, metric='hamming')

nn.fit(items)

There are no particular mathematical prerequisites; however, to fully understand all the algorithms, it's important to have a basic knowledge of linear algebra, probability theory, and calculus. Chapters 1 and 2 do not contain any code as they cover introductory theoretical concepts.

All practical examples are written in Python and use the scikit-learn machine learning framework, Natural Language Toolkit (NLTK), Crab, langdetect, Spark, gensim, and TensorFlow (deep learning framework). These are available for Linux, Mac OS X, and Windows, with Python 2.7 and 3.3+. When a particular framework is employed for a specific task, detailed instructions and references will be provided.

scikit-learn, NLTK, and TensorFlow can be installed by following the instructions provided on these websites: http://scikit-learn.org, http://www.nltk.org, and https://www.tensorflow.org.

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