Artificial Intelligence and Machine Learning

Intro to Predictive Analytics and Evolution of ML

Familiarize yourself with data 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. 

Data Exploration and Pattern Detection via Partitioning

Examine how to find structure in data, including clusters, density, and patterns. Discover why clustering analysis is useful and learn the mathematical background for distance metrics and its importance in machine learning, DBSCAN, HDBSCAN, optics, and expectation-maximization.

Data Transformation and Dimensional Reduction

Acquire knowledge 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, and topic models, as well as unstructured data concepts with the fundamentals of natural language processing. Delve into high-dimensional data visualization with UMAP and t-SNE and non-paramedic data soothing with kernel density estimation.

Supervised and Semi-Supervised Learning

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

Prediction with Support Vectors and Decision Trees

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

Ensemble Learning

Understand voting classifiers, Bootstrap aggregation (bagging), boosting methods, and the random forest theoretical approach.

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

Recommendation Systems, Graph Networks, and Social Network Analysis

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