INTRODUCTION TO AI AND PYTHON BASICS

Introduction To Artificial Intelligence

• Overview of Artificial Intelligence, history, and applications
• Types of AI, basic concepts (agents, environments, problem-solving)

Python Fundamentals For AI

• Introduction to Python programming language, syntax, and data structures
• Control structures (loops, conditionals), functions, and file handling in Python

DATA HANDLING AND VISUALIZATION FOR AI

Data Handling With Numpy and Pandas

• Introduction to NumPy for numerical computing and arrays
• Data manipulation and analysis with Pandas DataFrames

Data Visualization with Matplotlib and Seaborn

• Introduction to data visualization with Matplotlib
• Advanced visualization techniques using Seaborn for exploratory data analysis

MACHINE LEARNING BASICS

Introduction To Machine Learning

• Week 9: Overview of supervised and unsupervised learning
• Week 10: Basics of regression and classification algorithms

Model Evaluation and Validation

• Evaluation metrics for regression and classification models
• Techniques for model validation, cross-validation, and overfitting

ADVANCED MACHINE LEARNING TECHNIQUES

Supervised Learning Algorithms

• Decision trees and ensemble methods (Random Forest, Gradient Boosting)
• Support Vector Machines (SVM) and kernel methods

Unsupervised Learning Algorithms

• Clustering algorithms (K-means, hierarchical clustering)
• Dimensionality reduction techniques (PCA, t-SNE)

DEEP LEARNING FUNDAMENTALS

Introduction To Neural Networks

• Basics of artificial neural networks (ANN)
• Training neural networks, backpropagation algorithm

Deep Learning With Tensorflow

• Introduction to deep learning frameworks (TensorFlow)
• Building and training deep neural networks for image classification

ADVANCED TOPICS IN AI AND CAPSTONE PROJECT

Natural Language Processing (NLP)

• Week 21: Introduction to NLP, text preprocessing techniques
• Week 22: Sentiment analysis, text classification, and named entity recognition

Weeks 23-24: Reinforcement Learning

• Week 23: Introduction to reinforcement learning concepts and terminology
• Week 24: Q-learning algorithm, Markov decision processes (MDPs)

Weeks 25-26: Capstone Project

• Weeks 25: Students work on a real-world AI or ML project applying the concepts learned throughout the course
• Week 26: Presentation of capstone projects, peer review, and feedback session.