Machine Learning & Model Building
artificial-intelligence-ai.
Course Modules:
Module 1: Introduction to Machine Learning
What is machine learning?
ML vs. AI vs. Deep Learning
Types of ML: supervised, unsupervised, reinforcement
Module 2: Data Preparation and Splitting
Handling missing data and duplicates
Train/test/validation splitting
Feature scaling and normalization
Module 3: Supervised Learning – Regression
Linear Regression and Polynomial Regression
Model assumptions and evaluation (R², RMSE)
Use cases: price prediction, forecasting
Module 4: Supervised Learning – Classification
k-NN, Logistic Regression, Decision Trees
Evaluating performance (confusion matrix, precision, recall, F1-score)
Use cases: spam detection, medical diagnosis
Module 5: Unsupervised Learning Basics
Clustering with k-means and hierarchical clustering
Dimensionality reduction (PCA)
Use cases: customer segmentation, pattern discovery
Module 6: Model Evaluation and Tuning
Cross-validation and bias-variance tradeoff
Grid search and hyperparameter tuning
Avoiding overfitting and underfitting
Module 7: Introduction to Ensemble Methods
Random Forest and Gradient Boosting
Voting classifiers and stacking
Benefits of ensemble models
Module 8: Capstone Project
Choose from:
Building a customer churn classifier
Predicting housing prices
Clustering products for market segmentation
Submit final notebook, performance report, and model explanation
Tools & Technologies Used:
Python
Scikit-learn
Pandas, NumPy, Matplotlib
Jupyter Notebook / Google Colab
Target Audience:
Beginners in AI or machine learning
Data enthusiasts and developers
Students preparing for careers in data science or ML
Professionals applying ML to real-world tasks
Global Learning Benefits:
Understand how machine learning works from end to end
Build, test, and tune ML models using industry-standard tools
Solve real-world problems with structured data
Prepare for advanced AI, deep learning, and specialization tracks
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