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Generative AI

Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications
For IT & Computer science Engineers

Course Type :

₹ 21788

Introduction to AI: History and Applications
Statistic Essentials and Pre-program Preparation
Python Programming Basics: Control Flow, Functions
Data Structures, Modules, and File I/O
Handling Exceptions and Using Standard Library

Supervised Machine Learning Algorithms
Image and Video Processing with OpenCV
Robotics and Raspberry Pi Integration
Text Mining with NLTK: Tokenization, Stemming, Lemmatization
Deep Learning and Neural Networks

Course Content

Topic 1 | Introduction to AI

Topic 2 | AI / ML Recap

Topic 3 | Introduction to Generative AI

Topic 4 | Applications of Generative AI

Topic 4 | Introduction to Generative AI

Topic 5 | LLMs — Tokens, Embeddings, Pre-training & RLHF

Topic 6 | Reasoning Models

Topic 7 | Prompt Engineering

Topic 8 | Advanced Prompting

Topic 8 | Supervised Learning Algorithms

Topic 9 | Working with APIs & Chat Completion

Topic 10 | Generative AI with Python

Topic 11 | Image & Multimedia Generation

Topic 12 | AI Agents

Topic 13 | Retrieval-Augmented Generation (RAG)

Topic 14 | LLM / RAG Evaluation

Topic 15 | Fine-Tuning & Model Customization

Topic 16 | Deployment & MLOps

Topic 17 | Real-World Projects

Topic 18 | Natural Language Processing

Course Description

Artificial intelligence (AI) is a research field that studies how to realize the intelligent human behaviors on a computer. The ultimate goal of AI is to make a computer that can learn, plan, and solve problems autonomously. AI is currently one of the hottest buzzwords in tech and with good reason. The last few years have seen several innovations and advancements that have previously been solely in the realm of science fiction slowly transform into reality.

Experts regard artificial intelligence as a factor of production, which has the potential to introduce new sources of growth and change the way work is done across industries. For instance, this PWC article predicts that AI could potentially contribute $15.7 trillion to the global economy by 2035. China and the United States are primed to benefit the most from the coming AI boom, accounting for nearly 70% of the global impact.

Course objectives

All complex real-world problems like self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection are solved with Artificial Intelligence (AI). The main objective of this course is to give a comprehensive knowledge of the basic techniques for creating intelligent computer systems and a perception of how AI is applied to problems by introducing AI’s key problems, and the state-of-the-art models and algorithms used to undertake these problems.

Roles in industry

What IT jobs will be hot in the age of AI? Take a sneak peek at roles likely to be in demand

Intelligence Designer

Data Curator

Data Evangelist

Machine Learning Data Scientist

Robotics Process Analyst

Digital Knowledge Manager

AI Interaction Designer

Cognitive Copywriter

Course Highlights

Artificial Intelligence

Statistics Essentials

Pre-Program Preparation

Python

Machine learning

OPEN-CV

Natural Language Processing

Deep Learning

TensorFlow

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Generative AI

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Generative AI

Course Content

Session 1 | Introduction To Ai (2 lectures)

Session 2 | Ai / Ml Recap (2 lectures)

Session 3 | Introduction To Generative Ai (2 lectures)

Session 4 | Applications Of Generative Ai (2 lectures)

Session 5 | Llms — Tokens, Embeddings, Pre-Training & Rlhf (2 lectures)

Session 6 | Reasoning Models (2 lectures)

Session 7 | Prompt Engineering (2 lectures)

Session 8 | Advanced Prompting (3 lectures)

Session 9 | Working With Apis & Chat Completion (2 lectures)

Session 10 | Generative Ai With Python (2 lectures)

Session 11 | Image & Multimedia Generation (2 lectures)

Session 12 | Ai Agents (2 lectures)

Session 13 | Retrieval-Augmented Generation (Rag) (2 lectures)

Session 14 | Llm / Rag Evaluation (2 lectures)

Session 15 | Fine-Tuning & Model Customization (2 lectures)

Session 16 | Deployment & Mlops (2 lectures)

Session 17 | Real-World Projects (3 lectures)

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