Introduction to Data Science - Popular Data Science Packages in Python - Advanced Functions - Data Manipulation and Analysis with Pandas - Data Visualization with Matplotlib - Random Variables & Statistical Inferences - Statistical Distributions & Hypothesis Testing - Exploratory Data Analysis

Real-world use cases of Machine Learning - Introduction to SciKit-Learn - Machine learning Life Cycle - Implement a multi-variable regression problem with the scikit-learn library

Understanding cost function and gradient descent - Overfitting and Underfitting - K-Nearest Neighbours: - Classification and Regression - Linear Regression - Least Squares – Ridge - Lasso - Polynominal Regression - Logistic Regression: - SVM and Hyperparameter tuning - Implementing SVM using scikit- learn Lab Practice: House Price Prediction Write code to predict house prices based on several parameters available in the Housing and Urban Development of TN dataset using least squares linear regression.

Reason to evaluate models - Model Evaluation and selection methods - Precision-Recall - ROC Curves - Confusion Matrices - Regression Evaluation - Optimizing Classifiers for Different Evaluation Metrics Lab Practice: Movie Recommendation Engine Build a movie recommendation engine by applying collaborative filtering and topic modelling techniques. The dataset which contains 20 million viewer ratings of 27,000 movies.

Naive Bayes Classifiers, Decision Tree, Training and Visualizing a Decision Tree, Entropy and The CART Training Algorithm, Random Forests, Implement Random Forest with a real-world use case and understand the basics of random forest, Boosting - AdaBoost and Gradient Boosting, Capstone Project Lab Practice: K-Means Clustering Methods Create market segments using the India Census dataset and by applying the k-means clustering method.