IT0033 : Machine Learning Basics - Understanding Supervised, Unsupervised & Reinforcement Learning

This hands-on, highly-interactive course includes real-life case studies and exercises where participants will be engaged in a series of interactive small groups and class workshops.

This course is designed to provide participants with a detailed and up-to-date overview of Machine Learning Basics - Understanding Supervised, Unsupervised and Reinforcement Learning. It covers the importance of machine learning and its applications in various industries; the types of machine learning covering supervised learning, unsupervised learning and reinforcement learning; the data preprocessing for machine learning and regression and classification in supervised learning; the structure of decision trees, entropy and information gain in decision trees, overfitting and pruning techniques as well as the advantages of random forests over decision trees; the support vector machines (SVM) and neural networks in supervised learning, model evaluation and validation techniques; and the hyperparameter tuning in supervised learning.

Further, the course will also discuss the differences between supervised and unsupervised learning including its advantages and disadvantages; the types of clustering covering hierarchical, partitioning and density-based; the similarity measures comprising of euclidean, manhattan and cosine; the dimensionality reduction techniques covering principal component analysis (PCA), t-SNE for non-linear dimensionality reduction; and the practical applications of dimensionality reduction.

During this interactive course, participants will learn the basics of reinforcement learning, markov decision processes (MDP), Q-learning algorithm and deep reinforcement learning (DRL); the challenges and limitations of reinforcement learning, the concept and benefits of transfer learning; pretrained models in supervised learning and transfer learning in reinforcement learning; and the generative models, explainability and interpretability in ML models and deploying machine learning models.