6 months (26 weeks) with 4 hours of study per week + 6 Months Mentorship
I. Python Fundamentals (4 weeks)
- Introduction to Python programming
- Data types, variables, and operators
- Control structures (if/else, loops)
- Functions, modules, and libraries
- Exception handling
II. Numpy, Pandas, scikit (2 weeks)
- Numerical computation with Numpy
- Data manipulation and analysis with Pandas
- Machine learning with scikit
III. Data Visualization (2 weeks)
- Introduction to data visualization with Matplotlib
- Plotting various types of graphs and charts
IV. Machine Learning with Python (10 weeks)
- Overview of machine learning
- Supervised learning (regression, classification)
- Unsupervised learning (clustering, dimensionality reduction)
- Model evaluation and selection
- Regularization and optimization techniques
V. Deep Learning (6 weeks)
- Introduction to artificial neural networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Generative Adversarial Networks (GANs)
- Applications of deep learning in computer vision and natural language processing
VI. Project Work (2 weeks)
- Independent or group project work to apply learned concepts to real-world problems
- LLM, GPT, Open AI based projects and solution development.
6-Month Mentorship Phase
Month 7-12: Project Work and Advanced Studies
Month 7-8: Project Ideation and Planning
Mentors help students to select projects based on interest and complexity
Introduction to project management tools and version control systems (e.g., Git)
Month 9-10: Project Development
Regular meetings with mentors for guidance
Peer coding sessions for collaborative learning and problem-solving
Month 11: Project Finalization and Presentation
Polishing projects, debugging, and final testing
Preparing presentations and documentation
Month 12: Career Preparation and Advanced Studies
Resume building, interview preparation, and job searching strategies
Guidance on further studies, research areas, and specialization fields