COURSE OUTLINE

Week 1: Foundations & Classical Machine Learning

Day 1: Introduction to AI and Machine Learning

• The AI Landscape: Distinguishing between Narrow AI, General AI, and the role of Machine Learning within the ecosystem.
• Learning Paradigms: Comparative study of Supervised, Unsupervised, and Reinforcement Learning.
• Python Environment Architecture: Configuring Virtual Environments, Jupyter Notebooks, and essential AI libraries.
• Data Handling Basics: Introduction to data structures and loading external datasets (CSV, JSON, SQL).

Day 2: Data Science Essentials with Python

• Numerical & Tabular Processing: Mastering NumPy for vectorization and Pandas for data frame manipulation.
• The Preprocessing Pipeline: Handling missing values, encoding categorical variables, and feature scaling.
• Feature Engineering: Deriving new insights through transformation and selection techniques.
• Exploratory Data Analysis (EDA): Visualizing data distributions and correlations using Matplotlib and Seaborn.

Day 3: Supervised Learning & Evaluation

• Regression Analysis: Implementing Linear and Polynomial models to predict continuous outcomes.
• Classification Strategies: Deep dive into Logistic Regression, Decision Trees, and Random Forests.
• The Metrics of Success: Understanding Confusion Matrices, Accuracy, Precision-Recall, and F1-Score.
• Hands-on Labs: Developing a predictive model to solve a binary classification business challenge.

Day 4: Unsupervised Learning & Complexity Reduction

• Clustering Techniques: Grouping data with K-Means and Hierarchical clustering.
• Dimensionality Reduction: Using PCA (Principal Component Analysis) to simplify datasets without losing variance.
• Association Rule Learning: Discovering relationships in large-scale transactional data.
• Practical Exercises: Segmenting customer data into actionable behavioral groups.

Week 2: Deep Learning & Production Integration

Day 5: Deep Learning Fundamentals

• Biological to Artificial: The structure of a Neural Network and the role of the Perceptron.
• Activation Functions: Implementing ReLU, Sigmoid, and Tanh to introduce non-linearity.
• Backpropagation & Optimization: How models learn through Gradient Descent and error correction.
• TensorFlow & Keras Intro: Building your first "Sequential" model in a high-level API environment.

Day 6: Advanced Neural Architectures

• Computer Vision with CNNs: Convolutional layers, pooling, and spatial hierarchy for image recognition.
• Sequence Modeling with RNNs: Understanding "Memory" in models using LSTMs and GRUs for time-series or text.
• Transfer Learning: Utilizing pre-trained models (VGG, ResNet) to accelerate specialized development.
• Model Optimization: Preventing overfitting with Dropout, Regularization, and Early Stopping.

Day 7: AI Project Development & Pipelines

• End-to-End ML Pipelines: Orchestrating the flow from raw data ingestion to prediction.
• Model Deployment Concepts: Introduction to Model Serving, APIs (FastAPI/Flask), and Containerization (Docker).
• Performance Tuning: Hyperparameter optimization and cross-validation for maximum reliability.
• Practical Implementation: Building a robust validation strategy for production-grade AI.

Day 8: Capstone Project & Evaluation

• AI Solution Implementation: Final development time for a self-selected or assigned technical problem.
• Project Presentations: Demonstrating the technical architecture and results to peers.
• Assessments & Feedback: Comprehensive review of the course objectives and individual performance evaluations.
• Roadmap Ahead: Certification paths and emerging trends in Deep Learning.