| Day No | Lessons | Topics | Time (22.5 Hours) |
|---|---|---|---|
| 1 | Introduction to Machine Learning and Overview of pandas | What is Machine Learning Machine Learning Classification Types of Algorithms Importing and Manipulation of Data |
2.5hr |
| 2 | Sklearn Package, and Linear Regression using Machine Learning | Linear Regression with One variable Evaluation Metrics in Regression Models Train/Test splitting of data & Cross Validation Linear Regression with Multiple Variables |
2.5hr |
| 3 | Polynomial Regression | Under fitting, Overfitting, Best fit Polynomial Features Non-Linear Regression with One variable Non-Linear Regression with Multiple Variable |
2.5hr |
| 4 | Classification models - 1 | Introduction to categorical types of data Types of classification K-Nearest Neighbors Classifier Evaluation Metrics for classification Models |
2.5hr |
| 5 | Classification models - 1 | Logistic regression Support Vector Machines |
2.5hr |
| 6 | Classification models - 2 | Introduction to Decision Tree Terminology related to Decision Trees Types of Decision Trees Decision Trees Classifier |
2.5hr |
| 7 | Classification models - 2 | Decision Tree Regressor Random Forest Algorithm |
2.5hr |
| 8 | Unsupervised Learning and Clustering | Introduction to Unsupervised Learning Types of Unsupervised Learning Introduction to clustering Types of Clustering methods KMeans Clustering Applications |
2.5hr |
| 9 | Dimensionality Reduction | Dimensionality reduction Principal Component Analysis (PCA) |
2.5hr |