Machine Learning for Beginners

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Machine Learning for Beginners: Learn to Build Machine Learning Systems Using Python by Harsh Bhasin

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Data is being collected by websites, mobile applications, dispensations (on various pretexts), and even by devices. This data must be analyzed to become useful. The patterns extracted by this data can be used for targeted marketing, for national security, for propagating believes and myths, and for many other tasks. Machine Learning helps us in explaining the data by a simple model. It is currently being used in various disciplines ranging from Biology to Finance and hence has become one of the most important subjects.

There is an immediate need for a book that not only explains the basics but also includes implementations. The analysis of the models using various datasets needs to be explained, to find out which model can be used to explain a given data. Despite the presence of excellent books on the subject, none of the existing books covers all the above points.

This book covers major topics in Machine Learning. It begins with data cleansing and presents a brief overview of visualization. The first chapter of this book talks about introduction to Machine Learning, training and testing, cross-validation, and feature selection. The second chapter presents the algorithms and implementation of the most common feature selection techniques like Fisher Discriminant ratio and mutual information.

The third chapter introduces readers to Linear Regression and Gradient Descent. The later would be used by many algorithms that would be discussed later in the book. Some of the important classification techniques like K-nearest neighbors, logistic regression, Naïve Bayesian, and Linear Discriminant Analysis have been discussed and implemented in the next chapter. The next two chapters focus on Neural Networks and their implementation. The chapters systematically explain the biological background, the limitations of the perceptron, and the backpropagation model. The Support Vector Machines and Kernel methods have been discussed in the next chapter. This is followed by a brief overview and implementation of Decision Trees and Random Forests.

Various feature extraction techniques have been discussed in the book. These include Fourier Transform, STFT, and Local Binary patterns. The book also discusses Principle Component Analysis and its implementation.

The concept of Unsupervised Learning methods like K-means and Spectral clustering have been discussed and implemented in the last chapter.

The implementations have been given in Python, therefore cheat sheets of NumPy, Pandas, and Matplotlib have been included in the appendix.



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