- 3. Feature Engineering
- 4. 📊 Regression
- 5. 🧑💻 Gradient Descent
- 6. 👮 Regularization
- 7. 📘 Logistic Regression
- 8. 🌴 Decision Tree
- 9.🌋 Voting Ensemble Learning
- 10. 🛍️ Bagging Ensemble Learning
| Topic | What You'll Learn | Notebook | Lecture |
|---|---|---|---|
| What is Feature Engineering | – | – | 🔥 |
| Column Transformer | How to transform columns | 👨💻 | 🔥 |
| Sklearn without Pipeline | Why avoiding pipelines can cause problems | 👨💻 | 🔥 |
| Sklearn with Pipeline | How to implement sklearn pipelines effectively | 👨💻 | 🔥 |
| Topic | What You'll Learn | Notebook | Lecture |
|---|---|---|---|
| Ordinal Encoding | Ordinal categorical data preprocessing using OrdinalEncoder() |
👨💻 | 🔥 |
| One Hot Encoding | Nominal categorical data preprocessing using OneHotEncoder() |
👨💻 | 🔥 |
| Function Transformer | Log, reciprocal transformation using FunctionTransformer() |
👨💻 | 🔥 |
| Power Transformer | Square, square root transformation using PowerTransformer() |
👨💻 | 🔥 |
| Binarization | Preprocessing with Binarizer() |
👨💻 | 🔥 |
| Binning | Preprocessing with KBinsDiscretizer() |
👨💻 | 🔥 |
| Handling Mixed Variables | Processing datasets with both numerical & categorical features | 👨💻 | 🔥 |
| Handling Date & Time | How to work with time and date columns | 👨💻 | 🔥 |
| Topic | What You'll Learn | Notebook | Lecture |
|---|---|---|---|
| Standardization | Preprocessing using StandardScaler() |
👨💻 | 🔥 |
| Normalization | Preprocessing using MinMaxScaler() |
👨💻 | 🔥 |
| Topic | What You'll Learn | Notebook | Lecture |
|---|---|---|---|
| Complete Case Analysis | Remove NaN values |
👨💻 | 🔥 |
| Arbitrary Value Imputation (Numerical) | Impute with arbitrary value using SimpleImputer() |
👨💻 | 🔥 |
| Mean/Median Imputation (Numerical) | Impute with mean/median using SimpleImputer() |
👨💻 | 🔥 |
| Missing Category Imputation (Categorical) | Fill missing with a label using SimpleImputer() |
👨💻 | 🔥 |
| Frequent Value Imputation (Categorical) | Replace missing with most frequent value | 👨💻 | 🔥 |
| Missing Indicator | Add binary flag for missing values (MissingIndicator()) |
👨💻 | 🔥 |
| Auto Imputer Parameter Tuning | Use GridSearchCV() to optimize imputer settings |
👨💻 | 🔥 |
| Random Sample Imputation | Fill missing values with random samples | 👨💻 | 🔥 |
| KNN Imputer | Use K-Nearest Neighbors to fill missing values | 👨💻 | 🔥 |
| Iterative Imputer | MICE-style multivariate imputation | 👨💻 | 🔥 |
| Topic | What You'll Learn | Notebook | Lecture |
|---|---|---|---|
| What is Outliers | Introduction to outliers and their impact | 👨💻 | 🔥 |
| Outlier Removal using Z-Score | Removing outliers using Z-Score | 👨💻 | 🔥 |
| Outlier Removal using IQR | Removing outliers using Interquartile Range (IQR) | 👨💻 | 🔥 |
| Outlier Removal using Percentiles | Removing outliers using Percentiles | 👨💻 | 🔥 |
| Topic | What You'll Learn | Notebook | Lecture |
|---|---|---|---|
| Feature Construction and Splitting | Extract useful data and split features | 👨💻 | 🔥 |
| Topic | What You'll Learn | Notebook | Lecture |
|---|---|---|---|
| Curse of Dimensionality | Introduction to the "curse" of high dimensions | 👨💻 | 🔥 |
| PCA Geometric Intuition (PCA) | Geometric understanding of PCA (Principal Component Analysis) | 👨💻 | 🔥 |
| PCA Problem Formulation & Solution | Formulating and solving PCA problems | 👨💻 | 🔥 |
| PCA Step by Step Implementation | Implementing PCA step by step | 👨💻 | 🔥 |
| PCA + KNN (MNIST Dataset) | Apply PCA and KNN on the MNIST dataset | 👨💻 | 🔥 |
| Topic | What You'll Learn | Notebook | Lecture |
|---|---|---|---|
| Simple LR from Scratch | Code implementation from scratch | 👨💻 | 🔥 |
| Sklearn LR | Using LinearRegression() from sklearn |
👨💻 | 🔥 |
| Regression Metrics | Understanding R² score, MSE, RMSE | 👨💻 | 🔥 |
| Geometric Intuition | Understanding the geometric intuition of MLR | 👨💻 | 🔥 |
| Multiple LR from Scratch | Code implementation from scratch | 👨💻 | 🔥 |
| Mathematical Formulation Sklearn LR | Using LinearRegression() from sklearn |
👨💻 | 🔥 |
| Polynomial LR | Preprocessing and using PolynomialFeatures() |
👨💻 | 🔥 |
| Topic | What You'll Learn | Notebook | Lecture |
|---|---|---|---|
| Gradient Descent | Basic Introduction to Gradient Descent | 👨💻 | 🔥 |
| Batch Simple GD | Implementing Simple Batch GD from Scratch | 👨💻 | 🔥 |
| Batch GD | Implementing Batch Gradient Descent from Scratch | 👨💻 | 🔥 |
| Stochastic GD | Implementing Stochastic Gradient Descent from Scratch | 👨💻 | 🔥 |
| Mini Batch GD | Implementing Mini-Batch Gradient Descent from Scratch | 👨💻 | 🔥 |
| Topic | What You'll Learn | Notebook | Lecture |
|---|---|---|---|
| Bias-Variance Trade-off | Understanding Underfitting & Overfitting | - | 🔥 |
| Ridge Regression Geometric Intuition (Part 1) | Introduction to Regularized Linear Models | 👨💻 | 🔥 |
| Ridge Regression Mathematical Formulation (Part 2) | Scratch for slope (m) and intercept (b) | 👨💻 | 🔥 |
| Ridge Regression Mathematical Formulation (Part 2) | Full Scratch Implementation | 👨💻 | 🔥 |
| Ridge Regression (Part 3) | Gradient Descent Implementation | 👨💻 | 🔥 |
| 5 Key Points about Ridge Regression | Q&A, Effects, and Insights | 👨💻 | 🔥 |
| Lasso Regression | Full Implementation | 👨💻 | 🔥 |
| Why Lasso Regression Creates Sparsity | Understanding Sparsity Effect | 👨💻 | 🔥 |
| ElasticNet Regression | Comparison and Effects | 👨💻 | 🔥 |
| Topic | What You'll Learn | Notebook | Lecture |
|---|---|---|---|
| LR 1 - Perceptron Trick | Why to use it, transformations, region concept | - | 🔥 |
| LR 2 - Perceptron Trick Code | Math to algorithm conversion | 👨💻 | 🔥 |
| LR 3 - Sigmoid Function | How the sigmoid function helps to find the error line | 👨💻 | 🔥 |
| LR 4 - Math Behind Optimal Line | Maximum likelihood, binary cross-entropy, gradient descent | - | 🔥 |
| Extra - Derivative of Sigmoid | Helps derive matrix form from loss function | - | 🔥 |
| LR 5 - Logistic Regression (Gradient Descent) | Scratch implementation | 👨💻 | 🔥 |
| LR 6 - Multinomial Logistic Regression | Softmax regression | 👨💻 | 🔥 |
| LR 7 - Non-Linear Regression | Polynomial features | 👨💻 | 🔥 |
| LR 8 - Hyperparameter | Sklearn documentation and hyperparameter tuning | - | 🔥 |
| P1 Classification Metrics | Accuracy, confusion matrix, Type I & II errors, binary vs. multi-class | 👨💻 | 🔥 |
| P2 Classification Metrics Binary | Precision, recall & F1 score (binary) | 👨💻 | 🔥 |
| P2 Classification Metrics Multi-Class | Precision, recall & F1 score (multi-class) | 👨💻 | 🔥 |
| Topic | What You'll Learn | Notebook | Lecture |
|---|---|---|---|
| D1 - Decision Tree Geometric Intuition | Entropy, Gini Impurity, Information Gain | - | 🔥 |
| D2 - Hyperparameters | Overfitting and Underfitting | 👨💻 | 🔥 |
| D3 - Regression Trees | Numerical Points | 👨💻 | 🔥 |
| D4 - Awesome Decision Tree | dtreeviz Library |
👨💻 | 🔥 |
| Topic | What You'll Learn | Notebook | Lecture |
|---|---|---|---|
| Intro to Ensemble Learning | Ensemble techniques in ML | - | 🔥 |
| VE1 - Voting Ensemble | Code overview | - | 🔥 |
| VE2 - Voting Classifier | Hard vs Soft voting | 👨💻 | 🔥 |
| VE3 - Voting Ensemble Regression | Ensemble for regression tasks | 👨💻 | 🔥 |
| Topic | What You'll Learn | Notebook | Lecture |
|---|---|---|---|
| BE1 - Introduction | Basics of bagging | 👨💻 | 🔥 |
| BE2 - Bagging Classifiers | Bagging for classification | 👨💻 | 🔥 |
| BE3 - Bagging Regressor | Bagging for regression | 👨💻 | 🔥 |
Rudra Prasad Bhuyan
- Profile https://rudra-g-23.github.io/
- GitHub: https://github.com/Rudra-G-23
- Kaggle: https://www.kaggle.com/code/rudraprasadbhuyan/
- LinkedIn: https://www.linkedin.com/in/rudra-prasad-bhuyan-44a388235
