Part 2:Mathematical Tools

In this part, you will learn some of the most tried and tested mathematical tools and algorithms. On the one hand, there are algorithms for data dimensionality reduction, optimization of machine learning models, and data classification, which are explored through Python code. On the other hand, there are algorithms that model the relationships between objects (data points) and estimate the current and future states of variables (unknown and immeasurable ones) of a dynamic system. There are also other algorithms that predict the next future state probabilistically from knowledge of the present state of a process, explained with simple examples and Python code.

This part has the following chapters:

• Chapter 3, Principal Component Analysis
• Chapter 4, Gradient Descent
• Chapter 5, Support Vector Machine
• Chapter 6, Graph Theory
• Chapter 7, Kalman Filter
• Chapter 8, Markov Chain

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