This course revisits Python programming fundamentals, including computational logic, object-oriented design, file/error handling, and AI-powered code-generation tools. It features an environment setup with VS Code and Google Colab demos. Learners explore programming principles, debugging AI-generated code, code automation, refactoring, and ethical/legal considerations of AI-generated code, reinforced with practical exercises

Covering data science essentials, this course introduces key Python libraries like NumPy, Pandas, SciPy, Statsmodels, Scikit-Learn, and visualization tools (Matplotlib, Seaborn, Plotly). Modules involve data wrangling, cleaning, transformation, feature engineering (scaling, encoding), and statistical analysis. Practical operations include plotting, time-series handling, categorical/text data processing, aggregation, join/merge, and knowledge checks.

This provides a foundation in machine learning types and Python ML tools. It progresses through supervised learning techniques (classification/regression), algorithms (Linear/Logistic Regression, Naive Bayes, KNN, Decision Trees, Random Forests, SVM), regression models, evaluation metrics (MSE, RMSE, MAE, R²), regularization (Lasso, Ridge), hyperparameter tuning (Grid/Random Search), and model validation strategies

A deep dive into deep learning and its applications, this specialization covers neural network architecture, computer vision using CNNs (including ResNet, filters, pooling, TensorBoard), sequence modeling for NLP, generative AI architectures, optimization techniques (SGD, Adam, dropout), interpretability, and a hyperparameter tuning project on MNIST. Emphasis on practical model training and performance enhancements

This foundational course explores Generative AI concepts, transformer and GAN/VAE architectures, and case studies on GPT applications. It drills into prompt engineering with practical ChatGPT demos and surveys of open-source tools, including Hugging Face Spaces, and tackles security, bias, ethics, compliance, and future trends like Edge, Quantum, and Explainable AI

Building on GenAI Literacy, this course advances techniques in generative modeling, focusing on deep generative networks and applications across business domains such as automation, marketing, software engineering, product development, analytics, and customer service. Learners engage in demonstrations on AI-powered video creation and transcription, UI design, product roadmapping, and data integrity

This course immerses learners in sensors, actuators, control theory, and AI integration in control systems. Topics include sensor technologies (optical, motion, pressure), sensor fusion, electric/hydraulic/pneumatic actuators, PID and MPC feedback loops, industrial control applications, and AI-based sensor data processing for anomaly detection and predictive maintenance. Advanced focus on ML, deep learning, and reinforcement learning for adaptive control in robotics and motion systems

The program concludes with a hands-on capstone project, providing an opportunity to apply the skills and concepts learned across modules. Guided by industry experts, you will tackle a real-world business problem, leveraging AI and Generative AI techniques to design and implement a complete end-to-end solution. This capstone challenges both your technical and strategic abilities while creating a portfolio-ready project that showcases your expertise and prepares you for professional opportunities.