Python Machine Learning & AI
The Python, Machine Learning & AI Course is designed to provide learners with a solid foundation in programming, mathematics, and modern artificial intelligence techniques. Starting with Python basics, the course builds progressively into data handling, visualization, and statistical concepts essential for machine learning. Learners explore supervised and unsupervised algorithms, deep learning with neural networks, computer vision, and natural language processing.
Syllabus
Module 1: Python Programming Foundations
- Core concepts: variables, datatypes, conditionals, loops
- Functions & Object-Oriented Programming (classes, inheritance)
- File handling & exception management
- Working with NumPy & Pandas
- Data visualization using Matplotlib & Seaborn
- Environment setup: Jupyter Notebook, VS Code, virtual environments
Hands-on Projects: Calculator app, weather data visualization, data cleaning mini-project
Module 2: Mathematics & Statistics for ML
- Descriptive statistics: mean, median, mode, variance
- Probability & distributions (Normal, Binomial, Poisson)
- Correlation & covariance (Pearson vs Spearman)
- Linear algebra essentials: vectors, matrices, dot product, matrix multiplication
- Hypothesis testing: t-test, chi-square, p-value
- Basics of gradients & optimization
- Practice Labs: Probability simulations, hypothesis testing on datasets
Module 3: Supervised Machine Learning
- ML workflow: data preparation → model → evaluation
- Preprocessing: handling missing values, encoding, scaling
- Algorithms: Linear & Logistic Regression, Decision Trees, Random Forest, SVM, KNN
- Model evaluation: accuracy, confusion matrix, precision, recall, F1, ROC-AUC
- Hyperparameter tuning: GridSearch, RandomSearch
- Extra: Gradient Boosting (XGBoost, LightGBM)
- Project: Predict housing prices / customer churn
Module 4: Unsupervised Learning & Feature Engineering
- Clustering: K-Means, Hierarchical clustering
- Dimensionality reduction: PCA
- Feature selection & engineering techniques
- Handling imbalanced datasets (SMOTE, oversampling, undersampling)
- Extra: Association rule mining (Apriori, Market Basket Analysis)
Mini Project: Customer segmentation & clustering
Module 5: Introduction to Deep Learning
- Neural network basics (ANN)
- Activation functions, loss functions, optimizers
- Backpropagation explained visually
- Overfitting & regularization (dropout, batch norm)
- Frameworks: TensorFlow, Keras, PyTorch basics
- Extra: Transfer learning overview
- Projects: MNIST digit recognition, stock trend prediction
Module 6: Computer Vision with CNN
- CNN architecture: filters, pooling, layers
- Building CNN models with TensorFlow/Keras
- Object detection: YOLO, Faster R-CNN
- Data augmentation techniques
- Extra: Image segmentation (U-Net)
- Projects: Image classifier (cats vs dogs), object detection
Module 7: Natural Language Processing (NLP)
- Text preprocessing: tokenization, stopwords, stemming, lemmatization
- Feature extraction: Bag of Words, TF-IDF
- Word embeddings: Word2Vec, GloVe
- Sentiment analysis with ML models
- Sequence models: RNN, LSTM basics
- Extra: Intro to Transformers for NLP
- Projects: Sentiment analysis on Twitter data, spam email classification
Module 8: Generative AI Foundations
- What is generative AI?
- Types of generative models: GPT, diffusion models, VAEs, GANs
- Real-world applications: chatbots, content creation, automation
- Ethical considerations & bias in AI
- Mini Project: AI-generated text or images
Module 9: Transformer Architecture (Visual & Intuitive)
- Why transformers replaced RNNs
- Key concepts: embeddings, self-attention, multi-head attention, positional encoding
- Encoder vs Decoder structures
- GPT-style decoder-only transformers for text generation
- Extra: Comparing BERT, GPT, LLaMA
- Hands-on: Build a simple transformer for text classification
Module 10: Deployment & Capstone Projects
- Model deployment basics: Streamlit, Flask
- Hosting models: HuggingFace, AWS, Azure ML
- Git & GitHub essentials (version control, branching)
- CI/CD pipelines for ML models
- Career prep: resume building, interview readiness
- Final Projects:
- Supervised/Unsupervised ML project
- Build & deploy a chatbot using LLM
- Deploy CV/NLP model with Streamlit
