Classification and Prediction:
Basics of Machine Learning,
Supervised and Unsupervised Learning,
Predicting numerical outcomes using Linear Regression,
Linear Regression
- Introduction of Linear Regression
- What is Linear Regression?
- Simple Linear Regression
- Why Do We Need a Best Fit Line?
- Improving the Line (Optimization)
- Multiple Linear Regression
- Applications of Linear Regression
Regression Performance Metrics
- Understanding Training data, Testing data, Actual & Predicted Values
- Regression metrics
- Mean Absolute Error (MAE)
- What is Mean Squared Error (MSE)?
- Root Mean Squared Error (RMSE)
- Mean Absolute Percentage Error (MAPE)
- Comparison of Regression Performance Metrics (MAE, MSE,RMSE,MAPE)
- R-Squared (R²) and Adjusted R-Squared
Classification using Decision Trees
- Introduction of Decision Trees
- Splitting Criteria in Decision Trees
- ID3 (Iterative Dichotomiser 3)
- Pure and Impure Split using Entropy (withExample)
- Decision Tree using ID3(InformationGain)(Example)
- Numerical example of decision tree using ID3
- Where is ID3 Used? (Practical Applications)
- Limitations of ID3
- CART (Classification and Regression Trees)
- What is CART? (Definition)
- How CART Builds a Tree (Step-by-Step)
- What is Gini Index?
- Pure and Impure node in CART
- Characteristics of CART
- Comparison of Gini Index with Entropy(graph)
- ID3 vs CART
- Numerical examples to solve gini index
- Numerical example of CART
- Where CART is Used (Practical Angle)
- Applications of Decision Tree
K-Means Clustering
- Introduction to Cluster
- K-Means Clustering
- Centroid
- Distance Measures in K-Means
- Key Objectives of K-Means Clustering
- How to Apply K-Means Clustering Algorithm
- Stopping Criteria for K-means
- Objective Function of K-Means
- How to Choose the Value of K?
- Illustration of K-Means Clustering Using a Sample Dataset
- Applications of K-Means Clustering
Basic evaluation of classification models using accuracy
Confusion matrix