Course Overview
| Module | Duration (Instruction al Hours) | Theory | Lab | Self-Study | Tool/Software |
|---|---|---|---|---|---|
| Managing Large DataSets with MongoDB | 32 | 16 | 16 | 12 | MongoDB 6.x |
| Application Based Programming in Python | 36 | 18 | 18 | 10 | Python NLP Tools and Libraries JupyterNotebook, Google Collab TensorFlow |
| Data Science using R Programming | 36 | 18 | 18 | 8 | " |
| AI Primer | 16 | 16 | 16 | " | |
| AI Applications of NLP | 40 | 20 | 20 | 8 | " |
| AI and ML with Python | 40 | 20 | 20 | 12 | " |
| Applied Machine Learning using Python | 40 | 20 | 20 | 12 | " |
| Deep Learning using Neural Networks | 60 | 30 | 30 | 12 | " |
| Capstone Project-Recommendation Engine and Customer Churn Prediction | 40 | 2 | 38 | " | |
| Total Hours | 340 | 160 | 180 | 90 |
EXIT PROFILE
AI Developer
CERTIFICATE NAME
Smart Professional- Artificial Intelligence and Machine Learning
LEARNING OUTCOMES
- Understand the basics of statistical analysis, descriptive statistics, predictive analytics, probability, and Bayes theorem
- Gain an understanding of AI
- Gain knowledge in NLP and learn the use of AI in NLP
- Use important building blocks of AI ML with Python, make data modelling decisions, interpret output of the algorithms, and validate results
- Master ML concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands on modeling to develop AI algorithms
- Master deep learning concepts and TensorFlow open source framework, implement deep learning algorithms, and build ANN
- Develop a real world Capstone project on recommendation engine and perform customer churn prediction