Artificial Intelligence and Machine Learning

Module 1: Introduction to AI & ML

• What is Artificial Intelligence?

• Machine Learning vs Deep Learning vs Data Science

• Real-world applications of AI/ML

Module 2: Python for AI/ML

• Python basics, libraries (NumPy, Pandas, Matplotlib)

• Data types, loops, functions, and OOP concepts

• Data visualization with Seaborn & Matplotlib

Module 3: Basics of Statistics & Probability

• Mean, median, mode, standard deviation

• Probability distributions

• Hypothesis testing & correlation

Module 4: Working with Jupyter Notebooks

• Setting up and using Jupyter

• Writing and running Python code interactively

• Markdown and visualization in Jupyter

Module 5: Data Preprocessing & Cleaning

• Handling missing values, outliers

• Feature engineering and scaling techniques

• Data encoding (label, one-hot)

Module 6: Supervised Learning Algorithms

• Linear Regression, Logistic Regression

• Decision Trees, Random Forest

• K-Nearest Neighbors, Naive Bayes

• Model evaluation (confusion matrix, accuracy, precision, recall, F1 score)

Module 7: Unsupervised Learning Algorithms

• K-Means Clustering

• Hierarchical Clustering

• Dimensionality Reduction (PCA, t-SNE)

Module 8: Introduction to Deep Learning

• Neural Networks basics

• Activation functions, backpropagation

• Using TensorFlow/Keras for simple models

Module 9: Model Deployment Basics

• Saving and loading models

• Creating APIs with Flask or FastAPI

• Deployment to cloud (Heroku, AWS, Streamlit)

Module 10: Capstone Project

• End-to-end ML project using real-world dataset

• Data analysis, modeling, evaluation, and deployment

• Final presentation and review