COURSE | DURATION (Short Duration) | DURATION ( Included with Semester ) | |
|---|---|---|---|
Data Science (AI&ML) | 70(hours) | 194(hours) | |
Applied Artificial Intelligence (For Medical Students) | 64(hours) | 160(hours) | |
Prompt Engineering (Unlock capabilities of LLM) | 65(hours) | 165(hours) | |
Digital Marketing Using AI | 60(hours) | 140(hours) |
*Depending upon the enquiry Duration and on campus programs can be scheduled.
COURSE | DURATION (One Day Workshop) | DURATION (Two Days Workshop ) | |
|---|---|---|---|
Data Science (AI&ML) | 10AM to 4PM | 10AM TO 4PM | |
Applied Artificial Intelligence (For Medical Students) | 10AM TO 4PM | 10AM TO 4PM | |
Prompt Engineering (Unlock capabilities of LLM) | 10AM TO 4PM | 10AM TO 4PM | |
Digital Marketing Using AI | 10AM TO 4PM | 10AM TO 4PM |
COURSE | DURATION (BATCH 1) | DURATION (Batch 2 ) | |
|---|---|---|---|
Data Science (AI&ML) | 10AM to 12PM | 2PM TO 4PM | |
Applied Artificial Intelligence (For Medical Students) | 10AM to 12PM | 2PM TO 4PM | |
Prompt Engineering (Unlock capabilities of LLM) | 10AM to 12PM | 2PM TO 4PM | |
Digital Marketing Using AI | 10AM to 12PM | 2PM TO 4PM |
*Depending upon the enquiry Duration and on campus programs can be scheduled.
1.Generative AI Foundations
2.Introduction to Large language models
3.Introduction to Prompt Engineering
4.Advanced prompt Engineering Techniques
5.AI Conversational Models-ChatGPT, Claude, bard, etc.
6.Agents and GenAI
7.Enterprise GenAI
8.AI Security
9.GenAI Start-ups
10.Practical Examples
11.Tools & Response accusamus ullam voluptatibus commodi numquam, error, est. Ea, consequatur.
Program outline:
| Program Component | Duration |
|---|---|
| Technical Course Content | 44 Hrs. |
| Capstone Project Content | 15 Hrs. |
| Modular Activities | 5 Hrs. |
| Total Duration | 64 Hrs. |
1. Machine Language Introduction
2. Supervised learning
3. Unsupervised learning
4. Decision trees
5. K-nearest neighbors Algorithm
6. BIAS &Variance under Fitting and over Fitting
7. Random Forest
8. SVM
9. Feature Engineering
10. Feature Encoding
11. Clustering
12. K-means Clustering
13. Principal Analyzing Component
1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
2. WHAT’S NEXT FOR AI
3. GENERATIVE AI
4. REAL WORLD APPLICATION
5. EVOLUTION OF GENERATIVE AI
6. GENERATIVE PRE-TRAINED TRANSFORMS
7. DEEP LEARNING
8. NATURAL LANGUAGE PROCESS
9. NEURAL NETWORKS
HOW BIG DATA PRODUCE AI APPLICATIONS Component
1 Python for Data Science
1.Implementing of LLM Models
2 Mathematics
3.Data Analytics
4. Machine Learning & Artificial Intelligence
5. Specialization
6. Latest Technology in IT
7. Job Opportunities in Data Science
Artificial Intelligence and Generative AI
1.Implementing of LLM Models
2. Disease Predictions from symptoms
3.Fake Data Analysis
4.Website AI Chatbot (using RAG Models)
5. AI Data Analyst
6. Analyst PRODUCE AI APPLICATIONS Component
1. Understanding digital marketing
2.The power of AI Marketing
3.Automation marketing process with AI
4. Data Analytics and insights
5. Future trends and innovation
6. The Role of chat bots and Virtual Assistance in business automation and
customer management
7. Adv. Designing and Editing tools
8. Live Demo session on social media adv. Posting (FACEBOOK &INSTAGRAM)
9. Fulltime and part time earning opportunities with real time examples