This course on Supervised and Unsupervised Learning by The Association of Data Scientists (ADaSci) is designed to advance the skills of data science and machine learning professionals. It covers the detailed coverage of supervised and unsupervised learning techniques, with their working principle, application to real-world problems and hands-on implementations.
This course is ideal for those preparing for the Chartered Data Scientist (CDSTM) and Certified Data Scientist – Associate Level exams, as well as those seeking to advance their knowledge in data science and machine learning. With experienced instructors and practical exercises, this course provides an excellent opportunity to expand your skills and advance your career in the field of data science.
Linear and Non-linear Models, Classification, Regression, K-Nearest Neighbours, Naïve Bayes, Clustering, K-Means Clustering, Hierarchical Clustering, Various learning errors, regularization, estimator bias-variance trade-off, active learning, Support vector machine (SVM) and kernels, Model selection and model selection criteria, Ensemble learning – bagging and boosting, Expectation-Maximization (EM) algorithm, Hidden Markov models, Bayesian networks, Probabilistic inference, Association Rule Learning, Reinforcement Learning, Time-Series Analysis, Cross-Validation.