This course is for Aspiring Data Scientists. Students need to bring their personal laptop. Python Introduction, Machine Learning, Classification, Linear Regression, Deep Learning. In classification, will cover Ensemble methods- XGBOOST, CATBOOST, LGB. Also include end to end Model life cycle, start from data extraction, Cleaning, EDA, Model building, Optimization and deployment. Will cover Cross Validation, Random search CV, Under/Over sampling, SHAP value, CONFUSION MATRIX, Precision and Recall, ROC, AUC. In course, we also will cover Mathmatics behind Algorithms and Statistical Test. Supervised Vs Unsupervised Learning. In Classification algorithms, will cover Logistic Regression, KNN, Bayes Theorem, Decision Tree, Random forest. In ANN, will cover Gradient Descent, Backpropagation, Softmax, Relu, CNN. For variable reduction will cover Principles component analysis (PCA). In Python, will cover functions, Dictionaries, Dataframe,Looping, conditional statement, string manipulation, Numpy for MATRIX, Pandas for dataframe operations, Scikitlearn library, variables and data types, operators, List, inputs and outputs, Lambda function, Data filtration, Read and write from different sources like csv and excel. Python connectivity with SQL through ODBC.