ARTIFICIAL INTELLIGENCE & MACHINE LEARNING WITH PYTHON COURSE

PROGRAMMING & LIBRARIES

• Python 3.x
• NumPy, Pandas, Matplotlib, Seaborn
• Scikit-Learn, TensorFlow/PyTorch
• NLTK, OpenCV

DEVELOPMENT TOOLS

• Jupyter Notebook
• VS Code
• Git/GitHub
• Google Colab (for GPU Access)

DEPLOYMENT

• Flask/FastAPI
• Heroku/Streamlit

Goal: Solid python basics & environment setup

• Week 1-2: Python syntax, variables, data types, basic operators
• Week 3: Control structures (loops, conditionals), functions
• Week 4: Data structures (lists, dictionaries, tuples, sets)

PROJECTS:

• Simple Calculator
• Number guessing game
• To-do list application

Goal: Master data manipulation & visualization

• Week 1-2: NumPy arrays, Pandas DataFrames, Data cleaning
• Week 3: Matplotlib & Seaborn for data visualization
• Week 4: Data preprocessing, handling missing values, basic EDA

PROJECTS:

• COVID data analysis
• Stock price visualization
• Customer data cleaning

Goal: Understand core ML concepts & implementations

• Week 1: Linear regression, logistic regression
• Week 2: K-nearest neighbors, decision trees
• Week 3: Model evaluation metrics, train-test split, cross-validation
• Week 4: Clustering (K-means), introduction to Scikit-learn

PROJECTS:

• House price prediction
• Customer segmentation
• Spam classifier

Goal: Advanced ML & neural network basics

• Week 1: Ensemble methods (Random forest, Gradient boosting)
• Week 2: Support vector machines, Hyperparameter tuning
• Week 3: Neural networks fundamentals, activation functions
• Week 4: Introduction to TensorFlow/PyTorch, basic neural networks

PROJECTS:

• Credit risk assessment
• Image classification with simple NN

Goal: Master neural networks & computer vision

• Week 1: Convolutional Neural Networks (CNNs)
• Week 2: Advanced CNN architectures (ResNet, VGG)
• Week 3: Transfer learning, data augmentation
• Week 4: Object detection basics, CNN project implementation

PROJECTS:

• Dog breed classifier
• Facial expression recognition
• Medical image analysis

Goal: NLP, Deployment, & comprehensive project

• Week 1: Natural language processing (tokenization, embeddings)
• Week 2: RNNs, LSTMs for sequence data
• Week 3: Model deployment basics (Flask/FastAPI)
• Week 4: End-to-end capstone project

PROJECTS:

• Sentiment analysis
• Chatbot development
• Full-stack AI web application

• Session 1: 2 Hours - Theory & concepts
• Session 2: 1 Hour - Hands-on coding exercises
• Session 3: 1 Hour - Project work & Q/A