Artificial Intelligence and Machine Learning Fundamentals

This is a comprehensive course outline for Artificial Intelligence and Machine Learning that covers various important topics in the field. The course starts with an introduction to AI and ML, including their definitions, history, applications, and ethical and social implications. The second part of the course focuses on Supervised Learning and includes regression analysis, specifically simple linear regression and multiple linear regression, as well as various classification algorithms such as K-Nearest Neighbor (KNN), Decision Trees, Support Vector Machines (SVM), and Naive Bayes. Evaluation metrics for Supervised Learning such as accuracy, precision, recall, and F1 Score are also covered. The third part of the course covers Unsupervised Learning and includes clustering algorithms such as K-Means Clustering and Hierarchical Clustering, as well as dimensionality reduction techniques such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD). The fourth part of the course focuses on Deep Learning and covers artificial neural networks, including feedforward neural networks, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), as well as optimization techniques such as Stochastic Gradient Descent (SGD) and Backpropagation. The fifth part of the course covers Reinforcement Learning, including Markov Decision Processes (MDP), Q-Learning, Monte Carlo Methods, and Temporal-Difference Learning. The sixth part of the course focuses on Natural Language Processing and covers various text preprocessing techniques such as tokenization, stopword removal, stemming and lemmatization, as well as N-Grams, sentiment analysis, and Named Entity Recognition (NER). The conclusion of the course provides a recap of key concepts, the future of AI and ML, career opportunities in AI and ML, and final thoughts and recommendations for further study. This course outline provides a solid foundation for anyone interested in learning about Artificial Intelligence and Machine Learning.