Hands-On Mathematics for Data Scientists
This hands-on guide will help you sharpen the skillsets by understanding the required math for implementing machine learning models.
The book will start with giving you an overview of fundamental mathematical concepts such as set algebra and discrete math, various algebraic functions, plotting and visualization techniques, and more. You will cover essential topics such as calculus and key optimization techniques as applicable to machine learning. It will help you learn various statistical methods such as descriptive statistics and probability for data analysis, Inferential statistics, Bayesian statistics and more using examples. Further, the book focuses on the basic properties of vectors and matrices. It also touches on the advanced topic of principal component analysis, as an important component of machine learning pipeline. Lastly, you will be able to apply these learned topics to various popular machine learning algorithms such as linear and logistic regression, decision trees, support vector machine, and even cover advanced topics such as deep neural networks.
By the end of the book, you will build a strong foundation of mathematical skills, statistical knowledge, and data computation abilities to pursue a successful career as a highly efficient and impactful data scientist.
|Course Length||11 hours 20 minutes|
|Date Of Publication||24 Jan 2020|