Repository files navigation 100 Days of Machine Learning 3.1 🔧 Feature Transformation
Topic
What You'll Learn
Notebook
Lecture
What is Feature Engineering
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Column Transformer
How to transform columns
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Sklearn without Pipeline
Why avoiding pipelines can cause problems
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Sklearn with Pipeline
How to implement sklearn pipelines effectively
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3.1.2 🔧 Encoding Categorical and Numerical Data
Topic
What You'll Learn
Notebook
Lecture
Ordinal Encoding
Ordinal categorical data preprocessing using OrdinalEncoder()
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One Hot Encoding
Nominal categorical data preprocessing using OneHotEncoder()
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Function Transformer
Log, reciprocal transformation using FunctionTransformer()
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Power Transformer
Square, square root transformation using PowerTransformer()
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Binarization
Preprocessing with Binarizer()
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Binning
Preprocessing with KBinsDiscretizer()
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Handling Mixed Variables
Processing datasets with both numerical & categorical features
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Handling Date & Time
How to work with time and date columns
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Topic
What You'll Learn
Notebook
Lecture
Standardization
Preprocessing using StandardScaler()
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Normalization
Preprocessing using MinMaxScaler()
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3.1.4 🧩 Handling Missing Data
Topic
What You'll Learn
Notebook
Lecture
Complete Case Analysis
Remove NaN values
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Arbitrary Value Imputation (Numerical)
Impute with arbitrary value using SimpleImputer()
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Mean/Median Imputation (Numerical)
Impute with mean/median using SimpleImputer()
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Missing Category Imputation (Categorical)
Fill missing with a label using SimpleImputer()
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Frequent Value Imputation (Categorical)
Replace missing with most frequent value
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Missing Indicator
Add binary flag for missing values (MissingIndicator())
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Auto Imputer Parameter Tuning
Use GridSearchCV() to optimize imputer settings
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Random Sample Imputation
Fill missing values with random samples
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KNN Imputer
Use K-Nearest Neighbors to fill missing values
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Iterative Imputer
MICE-style multivariate imputation
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Topic
What You'll Learn
Notebook
Lecture
What is Outliers
Introduction to outliers and their impact
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Outlier Removal using Z-Score
Removing outliers using Z-Score
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Outlier Removal using IQR
Removing outliers using Interquartile Range (IQR)
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Outlier Removal using Percentiles
Removing outliers using Percentiles
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3.2 🏗️ Feature Construction
Topic
What You'll Learn
Notebook
Lecture
Feature Construction and Splitting
Extract useful data and split features
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3.3 🔍 Feature Extraction
Topic
What You'll Learn
Notebook
Lecture
Curse of Dimensionality
Introduction to the "curse" of high dimensions
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PCA Geometric Intuition (PCA)
Geometric understanding of PCA (Principal Component Analysis)
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PCA Problem Formulation & Solution
Formulating and solving PCA problems
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PCA Step by Step Implementation
Implementing PCA step by step
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PCA + KNN (MNIST Dataset)
Apply PCA and KNN on the MNIST dataset
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Topic
What You'll Learn
Notebook
Lecture
Simple LR from Scratch
Code implementation from scratch
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Sklearn LR
Using LinearRegression() from sklearn
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Regression Metrics
Understanding R² score, MSE, RMSE
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Geometric Intuition
Understanding the geometric intuition of MLR
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Multiple LR from Scratch
Code implementation from scratch
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Mathematical Formulation Sklearn LR
Using LinearRegression() from sklearn
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Polynomial LR
Preprocessing and using PolynomialFeatures()
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Topic
What You'll Learn
Notebook
Lecture
Gradient Descent
Basic Introduction to Gradient Descent
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Batch Simple GD
Implementing Simple Batch GD from Scratch
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Batch GD
Implementing Batch Gradient Descent from Scratch
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Stochastic GD
Implementing Stochastic Gradient Descent from Scratch
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Mini Batch GD
Implementing Mini-Batch Gradient Descent from Scratch
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Topic
What You'll Learn
Notebook
Lecture
Bias-Variance Trade-off
Understanding Underfitting & Overfitting
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Ridge Regression Geometric Intuition (Part 1)
Introduction to Regularized Linear Models
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Ridge Regression Mathematical Formulation (Part 2)
Scratch for slope (m) and intercept (b)
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Ridge Regression Mathematical Formulation (Part 2)
Full Scratch Implementation
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Ridge Regression (Part 3)
Gradient Descent Implementation
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5 Key Points about Ridge Regression
Q&A, Effects, and Insights
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Lasso Regression
Full Implementation
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Why Lasso Regression Creates Sparsity
Understanding Sparsity Effect
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ElasticNet Regression
Comparison and Effects
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Topic
What You'll Learn
Notebook
Lecture
LR 1 - Perceptron Trick
Why to use it, transformations, region concept
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LR 2 - Perceptron Trick Code
Math to algorithm conversion
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LR 3 - Sigmoid Function
How the sigmoid function helps to find the error line
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LR 4 - Math Behind Optimal Line
Maximum likelihood, binary cross-entropy, gradient descent
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Extra - Derivative of Sigmoid
Helps derive matrix form from loss function
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LR 5 - Logistic Regression (Gradient Descent)
Scratch implementation
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LR 6 - Multinomial Logistic Regression
Softmax regression
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LR 7 - Non-Linear Regression
Polynomial features
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LR 8 - Hyperparameter
Sklearn documentation and hyperparameter tuning
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P1 Classification Metrics
Accuracy, confusion matrix, Type I & II errors, binary vs. multi-class
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P2 Classification Metrics Binary
Precision, recall & F1 score (binary)
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P2 Classification Metrics Multi-Class
Precision, recall & F1 score (multi-class)
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Topic
What You'll Learn
Notebook
Lecture
D1 - Decision Tree Geometric Intuition
Entropy, Gini Impurity, Information Gain
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D2 - Hyperparameters
Overfitting and Underfitting
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D3 - Regression Trees
Numerical Points
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D4 - Awesome Decision Tree
dtreeviz Library👨💻 🔥
9.🌋 Voting Ensemble Learning
Topic
What You'll Learn
Notebook
Lecture
Intro to Ensemble Learning
Ensemble techniques in ML
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VE1 - Voting Ensemble
Code overview
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VE2 - Voting Classifier
Hard vs Soft voting
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VE3 - Voting Ensemble Regression
Ensemble for regression tasks
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10. 🛍️ Bagging Ensemble Learning
Topic
What You'll Learn
Notebook
Lecture
BE1 - Introduction
Basics of bagging
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BE2 - Bagging Classifiers
Bagging for classification
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BE3 - Bagging Regressor
Bagging for regression
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Rudra Prasad Bhuyan
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