Course Content

• Python overview (syntax, data structures, and data handling libraries)
• Introductory Probability and Statistics (distributions, the Law of Large Numbers, tail bounds)
• Data Science Overview
• Machine Learning introduction (Supervised, Unsupervised, Semi-supervised, Reinforcement)
• Supervised Learning
• Classification vs. Regression
• Linear Regression, Logistic Regression, KNN, Trees methods, SVMs
• Regularized Models (Ridge and Lasso Regression)
• Ensemble Learning (Bagging, Boosting, Random Forest, Gradient Boosting)
• Un-Supervised Learning (Dimensionality Reduction, Clustering (KMeans, Hierarchical and Fuzzy)
• Best Practices
• Feature Selection
• Bias-Variance Trade-off, Overfitting vs. Underfitting
• Handling Imbalanced Dataset
• Hyperparameter Tuning (Cross Validation)
• Model Assessment
• Docker for ML Model Deployment
• Introduction to Deep Learning and Neural Networks
• Capstone project (Project objective, Data wrangling, feature selection, AI Model selection and training, validation, and deployment (HMI and documentation)