Artificial Intelligence and Machine Learning

Familiarize yourself with predictive analytics, machine learning applications, and positioning in data-driven decision-making within the business world. Learn about types of decisions and tools like regression, classification, recommendation, and retrieval. Discover how to connect data, domain knowledge, business problems, and analytics, and learn to answer business questions with data visualization, nuances, and modalities. Explore neural networks, deep learning, and reinforcement learning concepts. 

Examine data exploration and pattern detection via partitioning, focusing on unsupervised big data algorithms. Discover why clustering analysis is useful and learn the mathematical background for distance metrics and machine learning, DBSCAN, HDBSCAN, optics, and the expectation-maximization algorithm.

Learn about of feature selection, extraction, and transformation, singular value decomposition (SVD), independent component analysis, and truncated SVD. Continue with data transformation, projection, and dimension reduction to better understand SVD. Examine feature embeddings, text transformations, topic models, unstructured data concepts, and the fundamentals of natural language processing. Delve into high-dimensional data visualization through UMAP and t-SNE and non-paramedic data soothing with kernel density estimation.

Explore supervised and semi-supervised learning. Examine concepts like basic notation, training, model development, the theory of loss function, and learning as an optimization algorithm. Learn about gradient descent-bayesian networks, ridge and Lasso, and descriptive classifiers like the k-nearest neighbor and the Naïve Bayes algorithm. Examine binary class learning, logistic regression, and Hinge, Jacobian, Hessian, and logarithmic loss.

Explore the use of discriminative classifiers vs. descriptive classifiers, kernel trick and support vector machines, and tree-based prediction algorithms. Learn how to extract classification and regression rules from decision trees.

Focus on voting classifiers, bootstrap aggregation (bagging), boosting methods, and the random forest theoretical approach.

Learn the essentials of model interpretation and regularization in machine learning, performance evaluation metrics, cross-validation, imbalanced data handling, anomaly detection, and generalization error (overfitting vs. underfitting). Examine autoencoders with an introduction to neural networks.

Explore collaborative filtering, the apriori algorithm, recommendation systems, and homogeneous vs. heterogeneous networks. Discover graph theory via social network analysis.