Essential Math for Data Science : take control of your data with fundamental linear algebra, probability, and statistics

Table of Contents: 1. Basic Math and Calculus Review 2. Probability 3. Descriptive and Inferential Statistics 4. Linear Algebra 5. Linear Regression 6. Logistic Regression and Classification 7. Neural Networks 8. Career Advice and the Path Forward

Master the mathematics required for data science, machine learning, and statistics success. In this book, author explains how calculus, probability, linear algebra, and statistics apply to linear regression, logistic regression, and neural network techniques. Along the way, you will also gain practical insights into the current state of data science and how to maximise your career using these insights. Explore calculus, linear algebra, statistics, and machine learning using Python code and libraries such as SymPy, NumPy, and scikit-learn. Comprehend techniques such as linear regression, logistic regression, and neural networks with minimal mathematical notation and jargon in plain English. Interpret p-values and statistical significance based on descriptive statistics and hypothesis testing on a dataset. Perform vector and matrix manipulations and matrix decomposition. Apply incremental knowledge of calculus, probability, statistics, and linear algebra to regression models that include neural networks. Avoid common pitfalls, assumptions, and biases while enhancing your skill set to distinguish yourself on the job market.