Artificial Intelligence and Machine Learning Fundamentals
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Course Overview
Course Modules Module 1: Introduction to AI & Machine Learning • AI History & Evolution • Types of Machine Learning • Supervised vs Unsupervised Learning • ML Workflow & Pipeline • Real-world AI Applications • Tools & Frameworks Overview Module 2: Python for Machine Learning • NumPy & Pandas • Data Manipulation • Matplotlib & Seaborn • Scikit-learn Basics • Data Preprocessing • Feature Engineering Module 3: Supervised Learning Algorithms • Linear Regression • Logistic Regression • Decision Trees • Random Forests • Support Vector Machines • Model Evaluation Module 4: Unsupervised Learning • K-Means Clustering • Hierarchical Clustering • Principal Component Analysis • Dimensionality Reduction • Anomaly Detection • Association Rules Module 5: Deep Learning Foundations • Neural Networks Basics • Activation Functions • Backpropagation • TensorFlow & Keras • Convolutional Neural Networks • Transfer Learning Module 6: Capstone Project • Problem Definition • Data Collection & Cleaning • Model Selection • Training & Evaluation • Deployment Basics • Presentation Capstone Project Build an end-to-end ML model solving a real-world classification or regression problem.
Learning Outcomes
Understand core AI and ML concepts and algorithms.
Build and evaluate ML models using Python.
Apply supervised and unsupervised learning techniques.
Implement deep learning solutions with TensorFlow.
Prerequisites
No mandatory prerequisites required.
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