Build your data science and machine learning skills using advanced mathematics and statistics About This Book * Implement complex mathematical and statistical concepts for solving data science problems using Python libraries *Explore essential mathematics behind the algorithmic methods to power machine learning and data science pipeline. *Learn and apply mathematics and statistics to build popular Machine learning algorithms Who This Book Is For Data Scientist Aspirants, Data Analysts, Data Engineers, Statisticians, and machine learning researchers who want to get well-versed with all mathematical foundation to implement machine learning from scratch will find this book useful. Basics of Python coding is sufficient for this book. What You Will Learn * Get an essential understanding of Set algebra, discrete math, and numbers *Learn how to use Python packages like SciPy and PuLP to solve simple optimization problems *Understand descriptive statistics and probability for data analysis, Inferential statistics, Bayesian statistics *Learn how to integrate linear algebra techniques and objects into machine learning algorithms *Understand important concepts of p-values, statistical power, and experimental/research design *Learn computational complexity for developing algorithms for solving Big Data problems *Solve linear equations using matrix inverse and Gauss-Jordan elimination techniques In Detail 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.