Course Modules (15)
Module 1: Python for Data Science
Module 2: Probability & Statistics for Data Science
Module 3: Data Preprocessing & Data Visualization
Module 4: Machine Learning Techniques
Module 5: Deep Learning Foundations
Module 6: Deep Learning Models & Applications
Module 7: MLOps & Large Language Model Ops (LLMOps)
Module 8: AI Applications & Emerging Technologies
Module 9: Capstone: AI Solutions to Real-World Problems
Module 10: AI Tools / Libraries
Module 11: Natural Language Processing (NLP)
Module 12: Responsible & Ethical AI
Module 13: Time Series & Forecasting
Module 14: Reinforcement Learning
Module 15: Career Enablement & Certification Exam Prep
Show/Hide Module Details
- Python for Data Science: Numpy, Pandas, OOPs, Matplotlib
- Probability & Stats: Bayes, Distributions, Hypothesis testing
- Data Prep & Viz: Feature engineering, Seaborn, Plotly
- ML Techniques: Regression, SVM, Tree models, Ensembles
- Deep Learning: CNNs, RNNs, GANs, Transfer Learning
- MLOps & LLMOps: ML pipeline, Model monitoring, LLM APIs
- Applications: Healthcare, Finance, Retail, Autonomous Systems
- Capstone: Industry-level project + Viva with experts
- Tools: Scikit-learn, TensorFlow, Hugging Face, MLflow
- NLP: Transformers, Q&A, Named Entity Recognition
- Ethical AI: XAI, Bias, Fairness, Regulations
- Time Series: ARIMA, LSTM, Forecasting
- Reinforcement Learning: Q-learning, Deep Q Networks
- Career: Resume prep, Mock interviews, Exam prep