Master AI & ML Class

Week 1: Fundamentals

• Python Programming Review: Data structures, functions, and key libraries (NumPy, Pandas).

• Mathematics for Data Science: Statistics, probability, and linear algebra essentials.

• Data Analysis Workflow: Understanding the end-to-end process.

• Development Setup: Configuring Python, Jupyter Notebooks, and essential tools.

Week 2: Data Preprocessing

• Data Collection: APIs, web scraping, and database queries.

• Data Cleaning: Handling missing values, duplicates, and outliers.

• Exploratory Data Analysis (EDA): Statistical summaries and pattern detection.

• Data Visualization: Introduction to Matplotlib and Seaborn.

Week 3: Machine Learning Foundations

• Supervised vs. Unsupervised Learning: Key differences and use cases.

• Model Training & Evaluation: Train-test split, cross-validation.

• Performance Metrics: Accuracy, precision, recall, ROC curves.

• Basic Algorithms: Linear/Logistic Regression, Decision Trees.

Week 4: Advanced ML & Power BI Introduction

• Ensemble Methods: Random Forests, Gradient Boosting (XGBoost).

• Feature Engineering: Creating meaningful input features.

• Power BI Basics: Connecting data sources, building dashboards.

• First Dashboard Project: Visualizing ML model outputs.

Week 5: Deep Learning Basics

• Neural Networks: Perceptrons, activation functions, backpropagation.

• Frameworks: TensorFlow/Keras or PyTorch introduction.

• Simple Neural Networks: Building and training a basic model.

• Transfer Learning: Leveraging pre-trained models (e.g., ResNet, BERT).

Week 6: Advanced Power BI & Data Storytelling

• DAX Formulas: Calculations and custom measures.

• Advanced Visualizations: Interactive charts, drill-downs.

• Data Modeling: Star schema, relationships, optimization.

• Dashboard Development: Creating business-ready reports.

Week 7: AI Applications

• Natural Language Processing (NLP): Text preprocessing, sentiment analysis.

• Computer Vision: Image classification, object detection.

• Time Series Analysis: Forecasting with ARIMA, LSTM.

• Recommender Systems: Collaborative vs. content-based filtering.

Week 8: Capstone Project & Professional Skills

• End-to-End Project: From data collection to model deployment.

• Deployment Basics: Flask/Django for ML APIs, Power BI publishing.

• Ethics in AI: Bias, fairness, and responsible AI practices.

• Presentation & Documentation: Showcasing work effectively.

Outcome:

By the end of this internship, students will have:
✔ Hands-on experience with Python, ML, and Power BI
✔ Completed a real-world capstone project
✔ Developed data storytelling and visualization skills
✔ Gained exposure to AI ethics and deployment