Master Study AI

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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