Mô tả

Unlock the potential of Generative AI with our comprehensive course, "Mastering Generative AI: LLMs, Prompt Engineering & More." This course is designed for both beginners and seasoned developers looking to deepen their understanding of the rapidly evolving field of artificial intelligence.


In this course, you will explore a wide range of essential topics, including:


· Python Programming: Learn the fundamentals of Python, the go-to language for AI development, and become proficient in data manipulation using libraries like Pandas and NumPy.

· Natural Language Processing (NLP): Dive into the world of NLP, mastering techniques for text processing, feature extraction, and leveraging powerful libraries like NLTK and SpaCy.

· Deep Learning and Transformers: Understand the architecture of Transformer models, which are at the heart of many state-of-the-art AI applications. Discover the principles of deep learning and how to implement neural networks using TensorFlow and PyTorch.

· Large Language Models (LLMs): Gain insights into LLMs, their training, and fine-tuning processes. Learn how to effectively use these models in various applications, from chatbots to content generation.

· Retrieval-Augmented Generation (RAG): Explore the innovative concept of RAG, which combines retrieval techniques with generative models to enhance AI performance.

· Prompt Engineering: Master the art of crafting effective prompts to improve the interaction with LLMs and optimize the output for specific tasks.

· Vector Databases: Discover how to implement and utilize vector databases for storing and retrieving high-dimensional data, a crucial skill in managing AI-generated content.

The course culminates in a Capstone Project, where you will apply everything you've learned to solve a real-world problem using Generative AI techniques.

By the end of this course, you will have a solid foundation in Generative AI and the skills to implement complex AI solutions. Whether you're looking to enhance your career, transition into AI development, or simply explore this fascinating field, this course is your gateway to mastering Generative AI.


Enroll now and take the first step toward becoming an expert in Generative AI!!

Bạn sẽ học được gì

Build a solid foundation in Python programming to effectively implement AI concepts and applications.

Understand the complete pipeline of Natural Language Processing, from data preprocessing to model deployment.

Learn how transformer models revolutionize NLP tasks, and how to leverage them for various applications.

Explore the essentials of Large Language Models (LLMs) and their applications in generative tasks.

Gain hands-on experience with Retrieval-Augmented Generation (RAG) and Langchain for building advanced AI applications.

Develop skills in crafting effective prompts to optimize model performance and achieve desired outputs.

Learn how to utilize vector databases for efficient storage and retrieval of embeddings in AI projects.

Yêu cầu

  • No pre-requisites

Nội dung khoá học

22 sections

Introduction to the Course

2 lectures
Introduction
02:03
Course Resources
00:16

Python begins!!

4 lectures
Welcome to the module on Python
01:31
Introduction
04:57
Datatypes & Operators
21:49
Variables & Keywords
19:54

Python Data Structures

4 lectures
Lists
27:51
Tuples
24:57
Sets
12:45
Dictionary
17:39

Python File Handling, Loops & Functions

4 lectures
Loops & Iterations
19:16
Functions in Python
23:31
Map, Reduce & Filter Functions
30:15
File Handling
19:19

Control Structures & OOPs

2 lectures
Control Structures
09:39
Object Oriented Programming Concepts
20:42

Python for Data Science & Analysis

2 lectures
Pandas
34:13
Numpy
28:33

Python for Data Visualization

3 lectures
Data Visualization
06:12
Matplotlib
26:37
Seaborn
19:10

Introduction to NLP (Pre-Requisite)

5 lectures
Welcome to the module on NLP
01:33
Introduction
06:57
Introduction (Part II)
06:08
NLP Key Challenges
09:02
Linguistics
07:08

NLP Basics (Pre-Requisite)

14 lectures
Case Folding
04:53
Special Character Removal
14:17
Handling Contractions
10:01
Tokenization
05:55
Stop Words Removal
07:09
nGrams
08:34
Vectorization
04:47
Word Embeddings
02:37
Bag of Words (Theory)
09:48
Bag of Words (Practicals)
19:00
TF-IDF (Theory)
12:18
TF-IDF (Practicals)
21:19
Part of Speech Tagging & NER
12:42
Named Entity Recognition (Practicals)
12:46

Word Embeddings (Pre-Requisite)

10 lectures
Word2Vec Intro
06:18
Word2Vec Part 2
03:24
Pretrained Word2Vec
07:34
Word2Vec INTUITION
08:50
Word2Vec 50Features
10:01
Word2Vec CBOW
19:10
Word2Vec SkipGrams
11:23
GloVe
18:04
fastText
11:58
Cosine Similarity
18:22

NLP Neural Networks (Pre-Requisites)

16 lectures
LSTM Part 1
19:25
LSTM Part 2 (Architecture)
16:19
LSTM Part 3 (Deep Dive Architecture)
06:53
LSTM Part 4 (Pointwise Operations)
06:19
LSTM Part 5 forgetGate
12:45
LSTM Part 6 inputGate
18:29
LSTM Part 7 outputGate
09:38
LSTM Part 8 Practicals
33:39
LSTM Part 9 Practicals
17:20
LSTM Part 10 Practicals
13:22
GRU Part 1
05:16
GRU Part 2
25:48
GRU Part 3 (resetGate)
08:39
GRU Part 4 (updateGate)
09:47
GRU Part 5 Practicals
12:07
Bi Directional RNN
27:14

Deep Learning (Pre-Requisite)

34 lectures
Welcome to the module on DL
01:51
Introduction to DL
03:02
Understanding Deep Learning
05:58
What is a Neuron
08:48
Activation Functions
05:12
Step Function
06:48
Linear Function
13:32
Sigmoid Function
07:06
TanH Function
03:35
ReLU Function
10:18
Backpropagation vs Forward Pass
15:42
Gradient Descent
07:32
ANN Intuition
04:33
ANN (Practicals)
15:25
ANN (Hyper Parameter Optimization)
12:37
CNN - Steps in CNN
12:19
CNN - What is CNN
08:07
CNN - Architecture Explained
17:21
CNN - Image Augmentation
14:40
CNN - Batch size vs iterations vs epochs
09:19
CNN - Code Implementation of CNN
21:21
CNN - Model Summary & Parameters
08:14
CNN - Hands on Project (XRAY Image Classification)
17:53
RNN - Basics
06:52
RNN - Types of RNN
05:18
RNN - Vanishing Gradient vs Exploding Gradient Problem
12:53
RNN - LSTMs
05:55
RNN - LSTMs (Practicals)
11:13
Pre Trained Models (Theory)
22:37
Pre Trained Models (Practicals)
23:23
VGG16
09:47
MobileNet
05:40
Transfer Learning
07:30
Final Project + Streamlit
15:13

Transformers (Pre-Requisite)

7 lectures
Welcome to the journey of Gen AI
01:49
Transformers Types
19:43
What are Transformers
33:08
Self Attention is all you need!!
27:25
Encoder Architecture
09:10
Contextual Embeddings
05:39
Decoder Architecture
05:29

Encoder Only Architecture (Pre-Requisite)

9 lectures
Introduction to BERT
13:33
BERT Configurations
03:46
BERT Fine Tuning
03:48
BERT Pre Training (Masked LM)
08:37
Input Embeddings BERT
09:13
ARLM vs AELM
06:23
RoBERTa
09:26
DistilBERT
13:48
AlBERT
14:20

Decoder Only Architecture

5 lectures
Introduction to GPT
05:44
GPT Architecture
05:21
GPT (Masked Multi Head Attention)
11:49
GPT Blocks
07:21
GPT Training
09:37

LLM Basics - Tokens, Context Window, Prompt, Prompt Tuning etc.

6 lectures
What are Tokens
01:53
Context Window
10:58
What is a Prompt?
10:50
Prompt Engineering
18:25
Prompt Tuning
06:58
Prompt Structures
13:25

Retrieval Augmented Generation

7 lectures
Introduction to RAGs
01:36
RAGs - What and Why
19:03
RAGs - Use Cases
16:45
RAGs - Research Paper
05:43
RAGs Architectures + VectorDB Architecture
19:07
RAGs - Detailed Architecture
12:44
RAGs - Research Paper based Chatbot using Open AI (Practicals)
33:56

LangChain

1 lectures
LangChain
10:31

Prompt Engineering

4 lectures
Introduction to Prompt Engineering
11:05
Types of Prompting
13:51
Few Shot Limitations
07:18
Chain of Thoughts Prompting
06:54

Vector Databases vs Vector Index

5 lectures
Vector Databases
13:00
Vector Index vs Vector Database
08:04
How Vector db works
10:43
Vector Database (Practicals)
30:41
LSH
14:29

Deployment of Gen AI Applications

7 lectures
Introduction
07:27
Introduction to Flask
12:41
Flask Basic App
12:37
Model Building (Breast Cancer)
15:55
Flask App (Breast Cancer)
22:23
Role of Cloud in Deployment (AWS)
09:52
AWS Deployment
28:51

Gen AI Projects

3 lectures
Research RAG Chatbot
33:56
Chat Scholar Chatbot + Essay Grading System
46:28
AI Powered Insurance Claims Processing
47:27

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