Understanding intelligence in machines

A brief history of AI and key milestones

AI tools and technologies used in modern applications

Basics of Python programming for AI applications

Key libraries: NumPy, Pandas, Matplotlib, Scikit-Learn

Data visualization techniques for AI and ML

What is Machine Learning? Differences between AI and ML

Supervised vs. Unsupervised learning with real-world examples

Work ShopTraining a model: data preprocessing, feature selection, and evaluation

Clustering methods: K-Means, DBSCAN, and Hierarchical Clustering

Dimensionality reduction: PCA, t-SNE, and feature selection

Practical applications in market segmentation and anomaly detection

Introduction to neural networks and activation functions

Forward and backward propagation in deep learning

Understanding CNNs (for images) and RNNs (for sequential data)

Text preprocessing: tokenization, stemming, and lemmatization

Practical applications: chatbots, spam filters, and language translation

Understanding responsible AI: fairness and transparency

Case studies of AI bias and real-world ethical concerns

Strategies for building unbiased and fair AI systems

AI applications in healthcare: diagnostics, drug discovery, and patient care

AI in finance: fraud detection, algorithmic trading, and credit scoring

AI in entertainment: recommendation systems, content generation, and gaming

Data collection, cleaning, and preparation for AI models

Model building, evaluation, and deployment in real-world applications

Mini project

Major projects

Coding exercises to reinforce AI concepts and techniques