The program comprehensively addresses the key areas of machine learning and AI in the context of mechanical engineering, enabling professionals and entry-level candidates to apply advanced techniques to real-world challenges in manufacturing and related fields.

Week 1: Python & Data Science Fundamentals

• Python Programming Basics: Data structures, control flow, functions
• NumPy for Numerical Computing: Arrays, vectorization, matrix operations
• Pandas for Data Analysis: DataFrames, GroupBy, data wrangling, merging
• Data Visualization with Matplotlib & Seaborn: Plots, exploratory data analysis (EDA)

Week 2: Mathematical Foundations for Machine Learning

• Linear Algebra: Vectors, matrices, transformations
• Eigenvalues and Eigenvectors: Dimensionality reduction
• Multivariable Calculus: Gradients, partial derivatives, optimization
• Probability & Statistics: Probability distributions, central limit theorem (CLT), hypothesis testing

Week 3: Regression & Supervised Learning

• Simple & Multiple Linear Regression: Model building, multicollinearity
• Model Evaluation Metrics: R², RMSE, and other performance measures
• Logistic Regression: Classification and decision boundaries
• Predictive Maintenance Case Study: Regression models on sensor data

Week 4: Advanced Machine Learning Techniques

• Decision Trees & Random Forests: Non-linear models, feature importance
• Support Vector Machines (SVM): Hyperplanes, kernel tricks
• Clustering Algorithms: k-Means, Hierarchical Clustering
• Dimensionality Reduction: Principal Component Analysis (PCA)
• Fault Detection in Manufacturing: Case study using anomaly detection techniques

 Week 5: Industry Applications & Capstone

• Problem Definition + Data Collection
• Exploratory Data Analysis (EDA)
• Model Building & Training
• Model Evaluation & Optimization
• Presentation (Insights, Visualizations, Demo)

Capstone project:

• Predictive Maintenance for Rotating Machinery
• HVAC System Optimization using Machine Learning
• Anomaly Detection in CNC Machines
• Material Property Prediction for Mechanical Components
• Fault Detection in Manufacturing Systems using Anomaly Detection