Machine Learning - Introduction to Machine Learning - Supervised Learning - Regression - Classification - Decision Trees and Random Forest - Unsupervised Learning - Time Series Modeling - Ensemble Learning - Recommender Systems Introduction to Machine Learning - Explanation of machine Learning - Relationship between Artificial Intelligence, Machine Learning, and Data Science - Definition and Features of Machine Learning - Machine Learning Approaches Supervised Learning - Supervised Learning - Supervised Learning: Real Life Scenario - Understanding the Algorithm - Supervised Learning Flow - Types of Supervised Learning - Types of Classification Algorithms Regression - Types of Regression Algorithms - Regression Use Case - Accuracy Metrics - Cost Function - Evaluating Coefficients - Demo: Linear Regression - Challenges in Prediction - Types of Regression Algorithms: Part II - Example as Bigmart - Logistic Regression - Sigmoid Probability - Accuracy Matrix - Demo: Survival of Titanic Passengers Classification - Overview of Classification - Classification: A Supervised Learning Algorithm - Use Cases - Classification Algorithms - Performance Measures: Confusion Matrix - Performance Measures: Cost Matrix - Naive Bayes Classifier - Steps to Calculate Posterior Probability - Support Vector Machines: Linear Separability - Support Vector Machines: Classification Margin - Linear SVM: Mathematical Representation - Non linear SVMs - The Kernel Trick Decision Trees and Random Forest - Decision Tree: Classifier - Decision Tree: Examples - Decision Tree: Formation - Choosing the Classifier - Overfitting of Decision Trees - Random Forest Classifier Bagging and Bootstrapping - Example of Linear Regression Unsupervised Learning - Overview of Unsupervised Learning - Example and Applications of Unsupervised Learning - Clustering - Hierarchical Clustering - Hierarchical Clustering: Example - K-means Clustering - Optimal Number of Clusters - Examples of Cluster Based Incentivization Time Series Modeling - Overview of Time Series Modeling - Time Series Pattern Types - White Noise - Stationarity - Removal of Non Stationarity - Demo: Air Passengers I - Time Series Models - Steps in Time Series Forecasting - Demo: Air Passengers II Ensemble Learning - Overview of Ensemble Learning - Ensemble Learning Methods: Part I and Part II - Working of AdaBoost - AdaBoost Algorithm and Flowchart - Gradient Boosting - XGBoost - XGBoost Parameters: Part I and Part II - Demo: Pima Indians Diabetes - Model Selection - Common Splitting Strategies - Demo: Cross Validation Recommender Systems - Introduction to Recommender Systems - Purposes of Recommender Systems - Paradigms of Recommender Systems - Collaborative Filtering: Part I and II - Association Rule: Generation Apriori Algorithm - Apriori Algorithm Example: Part I and II - Apriori Algorithm: Rule Selection - Demo: User Movie Recommendation Model - Association Rule: Mining Market Basket Analysis - Association Rule: Mining