• Machine Learning in Nut shell, Supervised Learning, Unsupervised Learning, ML applications in the real world. Introduction to Feature engineering and Data Pre-processing: Data Preparation, Feature creation, Data cleaning & transformation, Data Validation & Modelling, Feature selection Techniques, Dimensionality reduction, Recommendation Systems and anomaly detection, PCA
• Decision Trees, Oblique trees, Random forest, Bayesian analysis and Naïve bayes classifier, Support vector Machines, KNN, Gradient boosting, Ensemble methods, Bagging & Boosting, Association rules learning, Apriori and FP growth algorithms, Linear and Nonlinear classification, Regression Techniques, Clustering, K-means, Overview of Factor Analysis, ARIMA, ML in real time, Algorithm performance metrics, ROC, AOC, Confusion matrix, F1score, MSE, MAE, DBSCAN Clustering in ML, Anomaly Detection, Recommender System
• Usage of ML algorithms, Algorithm performance metrics (confusion matrix sensitivity, Specificity, ROC, AOC, F1score, Precision, Recall, MSE, MAE) • Credit Card Fraud Analysis, Intrusion Detection system