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• Introduction to Machine Learning• Models & Metrics• Classifi cation - Logistic Regression - Linear Discriminant• Analysis - Confusion Matrix - ROC• Optimisation - Gradient Descent - Non-linear Programming• Decision Trees - Random Forest Bagging - Support Vector Machines• Boosting - Ensemble Methods - CatBoost, AdaBoost, XG Boost, LightGBM• Recommender Systems - Collaborative Filtering, Factorisation• Neural Networks - Hyper Parameter Tuning - Regularisation - CNN• Natural Language Processing (RNNs)• Unsupervised Learning - Clustering Methods• Reinforcement Learning

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