Unraveling the Mathematics of Machine Learning and Deep Learning Algorithms: Demystifying Basic to Advanced Concepts & Mathematics of Machine Learning and Deep Learning

Artificial Intelligence (AI) has grown to be a very popular field in today’s world. It is the simulation of natural intelligence in machines that are programmed to learn and mimic the actions of humans. Machine learning is a branch of artificial intelligence based on the idea that computers can learn from data, identify patterns and make decisions with minimal human intervention and gradually improve in decision making over time with more data. Deep learning is a subset of machine learning and is one of the most popular fields in artificial intelligence (AI). This book entails the machine learning and deep learning concepts, basic to advanced algorithms along with the explanation of mathematics behind each algorithm.

Key Features

· Fundamental concept of machine learning and deep learning

· Explanation of important terminology used in machine learning and deep learning

· In-depth explanation of machine learning and deep learning algorithms along with mathematics behind them

· Coverage of machine learning algorithms such as linear regression, logistic regression, SVM, decision tree, random forest, KNN, naïve Bayes, market basket analysis, clustering (K-Means, K-Medoids, K-Modes, and K-Prototypes), and Boosting (AdaBoost, GBM, and XGBoost)

· Coverage of deep learning algorithms such as ANN, RNN, LSTM, GRU, and CNN