Essential Course 3,156 students 4.9/5

Machine Learning Mastery

Learn supervised and unsupervised learning algorithms. Master regression, classification, clustering, and model evaluation. Build real-world ML projects with Scikit-learn.

8
Modules
45+
Hours
120+
Examples
8
Assignments
Python Scikit-learn Pandas NumPy Classification Regression
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Course Outline

1ML Fundamentals

0/3
1.1 Introduction to Machine Learning
What is Machine Learning and its applications
Supervised vs Unsupervised vs Reinforcement Learning
Training, validation, and test sets
Overfitting and underfitting concepts
1.2 ML Workflow & Best Practices
Data preparation and feature engineering
Model selection and hyperparameter tuning
Cross-validation and evaluation metrics
MLOps principles and production deployment
1.3 Scikit-learn Fundamentals
Scikit-learn API and data structures
Pipeline creation for reproducibility
Model persistence and serialization
Assignment: ML Fundamentals Quiz

2Regression Algorithms

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2.1 Linear & Polynomial Regression
Simple and multiple linear regression
Least squares method and normal equation
Polynomial regression for non-linear data
Regression evaluation metrics (MSE, RMSE, R2)
2.2 Regularization Techniques
Ridge regression (L2 regularization)
Lasso regression (L1 regularization)
Elastic Net combining L1 and L2
Preventing overfitting with regularization
2.3 Advanced Regression Models
Support Vector Regression (SVR)
Decision Tree and Random Forest regression
Gradient Boosting for regression
Model comparison and selection
Assignment: Regression Model Building

3Classification Algorithms

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3.1 Logistic Regression & Basic Classifiers
Logistic regression for binary classification
Decision boundaries and sigmoid function
Naive Bayes and K-Nearest Neighbors
Multi-class classification strategies
3.2 Decision Trees & Ensemble Methods
Decision tree construction and pruning
Random Forests and feature importance
Bagging and Boosting ensemble techniques
XGBoost and LightGBM for advanced classification
3.3 SVM & Neural Networks
Support Vector Machines for classification
Kernel tricks and hyperparameter tuning
Multi-layer perceptrons with Scikit-learn
Deep learning introduction
Assignment: Classification Model Comparison

4Unsupervised Learning

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4.1 Clustering Algorithms
K-Means clustering and initialization
Elbow method and silhouette analysis
Hierarchical clustering and dendrograms
DBSCAN for density-based clustering
4.2 Dimensionality Reduction
Principal Component Analysis (PCA)
Explained variance and scree plots
t-SNE and UMAP for visualization
Feature selection vs feature extraction
4.3 Anomaly Detection
Isolation Forest for anomaly detection
Local Outlier Factor (LOF)
One-class SVM for novelty detection
Applications in fraud and intrusion detection
Assignment: Customer Segmentation

5Model Evaluation & Selection

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5.1 Classification Metrics & Evaluation
Confusion matrix and its interpretations
Accuracy, Precision, Recall, and F1-Score
ROC curve and AUC-ROC score
Precision-Recall curve and handling class imbalance
5.2 Hyperparameter Tuning
Grid Search and Random Search
Bayesian Optimization for tuning
Halving Grid Search for efficiency
Cross-validation strategies
5.3 Model Selection & Comparison
Learning curves and bias-variance analysis
Validation curves for hyperparameter analysis
Model comparison and statistical significance
Ensemble methods for model combination
Assignment: Model Evaluation Report

6Feature Engineering

0/2
6.1 Feature Creation & Transformation
Domain knowledge-based feature creation
Polynomial and interaction features
Time-based features from datetime columns
Binning and discretization strategies
6.2 Encoding & Feature Scaling
Categorical encoding: one-hot, label, ordinal
Target encoding and frequency encoding
Feature scaling: normalization and standardization
Handling missing values and outliers
Assignment: Feature Engineering Pipeline

7Advanced Topics & Deployment

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7.1 Model Interpretability & Explainability
Feature importance in tree-based models
Permutation importance and SHAP values
LIME for local interpretability
Partial dependence and ICE plots
7.2 Model Deployment & MLOps
Saving and loading models with joblib and pickle
Building REST APIs with Flask and FastAPI
Containerization with Docker
Monitoring, logging, and A/B testing in production
Assignment: Model Deployment Project

8Real-World Projects & Capstone

0/7
Project 1: House Price Prediction
Data cleaning and exploratory analysis
Feature engineering for real estate data
Building and comparing regression models
Project 2: Iris Classification
Multi-class classification problem
Algorithm comparison and evaluation
Visualization and interpretation
Project 3: Customer Segmentation
K-Means clustering implementation
Determining optimal cluster count
Segment profiling and business insights
Project 4: Credit Risk Classification
Imbalanced dataset handling
Classification with cost sensitivity
ROC-AUC and threshold optimization
Project 5: Breast Cancer Diagnosis
Medical data analysis and preprocessing
Feature selection and dimensionality reduction
Ensemble models for high accuracy
Project 6: Fraud Detection
Unsupervised anomaly detection methods
Isolation Forest and LOF application
Evaluation and business metrics
Final Capstone Project
What You'll Learn
  • Master supervised learning algorithms and models
  • Build regression and classification models with Scikit-learn
  • Apply unsupervised learning for clustering and dimensionality reduction
  • Evaluate models and tune hyperparameters effectively
  • Engineer features and preprocess data professionally
  • Deploy models and implement MLOps best practices
Quick Navigation
1. ML Fundamentals 2. Regression 3. Classification 4. Unsupervised 5. Evaluation 6. Feature Engineering 7. Advanced Topics 8. Real-World Projects
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