← Generative AI & Large Language Models

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The complete curriculum

01 Introduction 2 lessons ▶

1. 1.1Generative AI Introduction
2. 1.2Generative AI End to End Flowchart Roadmap

02 Deep Dive In python 28 lessons ▶

1. 2.1Python Getting Started
2. 2.2Python Introduction
3. 2.3Variables and print & input functions
4. 2.4Data Types
5. 2.5Operators in python
6. 2.6Data Structures and List
7. 2.7List Mutability and List Slicing
8. 2.8List Methods and List Comprehension
9. 2.9Tuples in Python
10. 2.10Dictionary in Data Structure
11. 2.11Sets in Data Structures
12. 2.12Conditional Statements
13. 2.13Looping Statements_for loop
14. 2.14looping Statements While loop and loop control statements
15. 2.15Functions Overview
16. 2.16Functions Definition intro
17. 2.17Functions examples
18. 2.18Functions Recursive and Variable Arguments
19. 2.19Functions Questions
20. 2.20Lambda Functions
21. 2.21Map Functions
22. 2.22Filter Function
23. 2.23File Handling Introduction
24. 2.24File Handling Questions
25. 2.25OS Library
26. 2.26Modules and Packages
27. 2.27Important Libraries
28. 2.28Exception Handling

03 Object Oriented Programming 8 lessons ▶

1. 3.1Object Oriented Programming Introduction
2. 3.2Object Oriented Programming Concepts
3. 3.3Inheritance in OOPs
4. 3.4Polymorphism in OOPs
5. 3.5Encapsulation in OOPs
6. 3.6Abstraction in OOPs
7. 3.7Magic Methods
8. 3.8Operator Overloading

04 Streamlit in Python 1 lesson ▶

1. 4.1Streamlit in Python

05 Numpy 4 lessons ▶

1. 5.1Numpy Overview
2. 5.2Numpy Important Formulas
3. 5.3Mathematical Operations in Numpy
4. 5.4Numpy Statistical Functions and Other Important Concepts

06 Pandas 9 lessons ▶

1. 6.1Pandas Introduction
2. 6.2Getting Started with Series
3. 6.3Mathematical Functions in Series and indexing in Series
4. 6.4Getting Started with Real World Datasets in Series
5. 6.5DataFrame Introduction
6. 6.6Python Functionalities in Series and Plotting Graphs
7. 6.7Creating dataframe and Attributes and Functions in DataFrame
8. 6.8Important Functions and Fetching Specific Records and Columns From DataFrame
9. 6.9Performing Analysis Through Filtering Data & Adding New Columns and Discussing Other Important Functions

07 Matplotlib 8 lessons ▶

1. 7.1Matplotlib Overview and Installation
2. 7.2Getting Started with Practically working with Matplotlib
3. 7.3Deep Dive into Line Plot
4. 7.4Scatter Plot
5. 7.5Getting Started with Bar Plot
6. 7.6Horizontal,Multiple and Stacked Bars
7. 7.7Histogram
8. 7.8Pie Chart and Changing Styles

08 Seaborn 7 lessons ▶

1. 8.1Seaborn Overview
2. 8.2Relational Plot Introduction
3. 8.3Lineplot Other Examples and Scatter and Facet Plot
4. 8.4Distribution Plots as histplot,kdeplot,rugplot
5. 8.5Bivariate Histogram & KDE plots and Matrix Plots
6. 8.6Categorical Plots
7. 8.7Regression Plots and Multi Plots

09 Why Data Science Knowledge For Being An AI Engineer 1 lesson ▶

1. 9.1Why Data Science Knowledge For Being an AI Engineer

10 Machine Learning 40 lessons ▶

1. 10.1What is Artificial Intelligence and Machine Learning
2. 10.2AI vs ML vs NLP vs DL vs GenAI
3. 10.3Types of machine Learning
4. 10.4Tensors in Machine Learning
5. 10.5Machine Learning Development Life Cycle
6. 10.6Building an Demo Machine Learning Model 1
7. 10.7Building an Demo Machine Learning Model 2
8. 10.8Building an Demo Machine Learning Model 3
9. 10.9Framing The Business Problem To ML Problem
10. 10.10Data Gathering using csv
11. 10.11Json,SQL Data Gathering & Role in Agentic AI & AI App Development
12. 10.12Data Gathering using APIs for AI Apps,Agents,RAGs Development
13. 10.13Data Gathering Using API and Publishing on Kaggle(Mini Project)
14. 10.14Data Cleaning in ML,AI Data Management,Fine Tuning AI Models & NLP
15. 10.15Automated data Cleaning and importance while building Langchain Applications
16. 10.16Automated Data-Cleaning
17. 10.17Exploratory Data Analysis Steps
18. 10.18Exploratory Data Analysis( Feature Engineering)
19. 10.19Feature Engineering
20. 10.20Feature Encoding
21. 10.21One Hot Encoder
22. 10.22Column Transformer
23. 10.23Feature Scaling - Standardization & Normalization
24. 10.24ML PipeLine
25. 10.25Machine Learning Algorithm- Linear Regression
26. 10.26Logistic Regression
27. 10.27Naive Bayes
28. 10.28K Nearest Neighbors (KNN)
29. 10.29Metrics in Regression - RMSE, MAE, MSE
30. 10.30Overfitting, Underfitting , Variance & Bias
31. 10.31Classification Metrics Accuracy , Precision , Recall
32. 10.32Support Vector Machine
33. 10.33Decision Tree Algorithm
34. 10.34Train vs Validation vs Test Data & Cross Validation
35. 10.35Bagging vs Boosting & Random Forest Bagging Algorithm
36. 10.36Unsupervised Machine Learning
37. 10.37K Means Clustering
38. 10.38Heirarichal Clustering
39. 10.39DBSCAN Clustering
40. 10.40Anomaly Detection

11 Natural Language Processing 20 lessons ▶

1. 11.1Natural Language Processing Introduction
2. 11.2NLP Pipeline Overview
3. 11.3NLP Daily Tasks , Approach for it And Challenges in it
4. 11.4NLP Pipeline Initial Steps
5. 11.5Feature Engineering and Modelling For Rule Based vs Generative AI Chatbots with Deployment in Further Pipeline
6. 11.6Recommendation System Project Overview
7. 11.7Getting Started with Project
8. 11.8Preprocessing in Recommendation System
9. 11.9NLP Vectorization and Building the Recommendation System
10. 11.10Launching Recommendation System as a Website
11. 11.11Connecting to GitHub and Deployment in Render
12. 11.12Basic Text Cleaning in NLP
13. 11.13Advance Text Cleaning in NLP
14. 11.14Basic Text Preprocessing in NLP
15. 11.15Text Vectorization Overview
16. 11.16One Hot Encoding and Bag of Words in Text Vectorization
17. 11.17N - Grams and TFIDF Text Vectorization
18. 11.18Word2Vec Vector Embedding Overview
19. 11.19Types of Word2Vec Techniques and Neural Networks Overview
20. 11.20Training our own Word2Vec Model on our personal Data

12 Deep Learning 4 lessons ▶

1. 12.1Introduction To Deep Learning
2. 12.2Working of Deep Learning and ML vs DL
3. 12.3Perceptron in Deep Learning
4. 12.4Multi Layered Perceptron

13 Fundamentals for Training Deep Learning Model 10 lessons ▶

1. 13.1Requirements for Building Deep Learning Model
2. 13.2What is Loss Function and Training of GPT like networks
3. 13.3Regression Loss and Binary Cross Entropy
4. 13.4GPT Model Training Loss and Multi classification loss
5. 13.5Backpropogation in Neural Network and use in Training Chatgpt
6. 13.6Backpropogation Step by Step process
7. 13.7What is Gradient Descent
8. 13.8Types of Gradient descent and batch size in Neural Network
9. 13.9What is Vanishing Gradient Problem in Neural Network
10. 13.10Vanishing Gradient Reason and How to Identify and Handle

14 Improving Performance of Model 11 lessons ▶

1. 14.1How to Improve Performance of Model
2. 14.2Early Stopping and Data Scaling
3. 14.3Dropout Layers in Neural Network
4. 14.4Regularization in Neural Network
5. 14.5Batch Normalization in Neural Network
6. 14.6Batch Normalizaion Indepth and How used in Gen AI Model
7. 14.7What is Activation Function
8. 14.8Types of Activation Function in DL and Gen AI Models
9. 14.9Optimizers in Neural Network
10. 14.10Adagrad Optimizer
11. 14.11RMSProp and Adam Optimizer and Optimizers used for GenAI Models

15 ANN Project Implementation 13 lessons ▶

1. 15.1Getting Started with Building ANN Classification Model
2. 15.2Data Preprocessing and Cleaning For ANN Model
3. 15.3Feature Encoding,Scaling and Train-Test split
4. 15.4ANN Implementation in Model Building
5. 15.5Prediction using ANN Model
6. 15.6Using Encoder and Model File for Prediction
7. 15.7Creating Streamlit UI for the Application
8. 15.8Deployment of Application in Cloud
9. 15.9Regression Problem using ANN
10. 15.10Hyperparameter Tuning using KerasTuner
11. 15.11Hyperparameter Tuning for Nodes Number in Hidden Layers
12. 15.12Deciding Optimal Number of Hidden Layers with Tuning
13. 15.13Hyperparameter Tuning For deciding Optimal Hidden Layers,Nodes,Optimizer

16 Pretraining and Transfer Learning in Gen AI Models 4 lessons ▶

1. 16.1Pre Trained Models
2. 16.2Pre Training For Gen AI Apps in Industry
3. 16.3Transfer Learning in Neural Network
4. 16.4How Transfer Learning is Performed in Industry

17 Simple RNN Indepth Intuition 10 lessons ▶

1. 17.1Why RNNs are Needed
2. 17.2Problem with ANN for solving Sequential Data Problem
3. 17.3What is Recurrent Neural Network
4. 17.4Forward Propogation in RNN with Architecture
5. 17.5Types of RNN - Many To One
6. 17.6Other Types of RNN for Gen AI Applications
7. 17.7Backpropogation in RNN
8. 17.8Backpropogating Through Time
9. 17.9Problems with RNN
10. 17.10Problem of Vanishing Gradient of Long Term in RNNs

18 Deep Learning Project with Simple RNN 8 lessons ▶

1. 18.1Getting Started with RNN Project
2. 18.2Traditional Embedding Generation Techniques
3. 18.3Embedding Layer for capturing Context Info
4. 18.4RNN Project Initial Steps
5. 18.5Training Simple RNN without Embedding Layer
6. 18.6Training with Embedding Layer in Simple RNN
7. 18.7Creating Streamlit UI for the App
8. 18.8Pushing Larger File in Github and Deployment

19 LSTM and GRU Indepth Intuition 5 lessons ▶

1. 19.1From RNN to LSTM
2. 19.2Different Memory Types in LSTM
3. 19.3Gates in LSTM
4. 19.4GRU Introduction
5. 19.5Working of GRU

20 LSTM Next Word Prediction Project (Large Language Model) 6 lessons ▶

1. 20.1Overview of Project Task
2. 20.2Getting Started with Project
3. 20.3Tokenization and Text Prep for Modelling
4. 20.4Creating Model for Next Word Prediction
5. 20.5Streamlit UI For App
6. 20.6Predicting Through Model and Pushing Code in Github with Deployment

21 BIDirectional RNN Indepth Intuition 2 lessons ▶

1. 21.1Introduction to BiDirectional RNN
2. 21.2Working of BiDirectional RNN

22 Transformers and ChatGPT Evolution 14 lessons ▶

1. 22.1How LLMs work Introduction
2. 22.2TikTokenizer and Tokenization in Transformers
3. 22.3OpenAI and HuggingFace for Embeddings in Transformer
4. 22.4Positional Encoding and Self Attention in Transformer
5. 22.5Multi Head Attention and Encoder Other Layers
6. 22.6Decoder Working in Transformer and HuggingFace Model For Predictions
7. 22.7History of LLMs
8. 22.8Encoder Decoder For Seq to Seq Data
9. 22.9Attention Mechanism
10. 22.10Transformers in the Lifecycle
11. 22.11Transfer Learning with Transformers
12. 22.12Transfer Learning with Language Modelling Task
13. 22.13LLMs from OpenAI and Google
14. 22.14ChatGPT from LLMs

23 Introduction To Gen AI and LLM Models 6 lessons ▶

1. 23.1AI vs ML vs DL vs GenAI
2. 23.2How Transformers were Introduced for OpenAI Chatgpt Model
3. 23.3OpenAI Chatgpt Model Training Initial Steps
4. 23.4OpenAI Chatgpt Training Final RLHF Step
5. 23.5Evolution of Large Language Models
6. 23.6ALL LLM Models Analysis

24 Introduction To Langchain For Generative AI 2 lessons ▶

1. 24.1Getting Started with Langchain
2. 24.2Why Langchain and Its Benefits

25 Getting Started with Langchain and OpenAI 2 lessons ▶

1. 25.1Langchain Website Overview with Langsmith , Langraph and Langserve
2. 25.2Getting Started with OpenAI LLM

26 Important Components and Modules in Langchain 21 lessons ▶

1. 26.1Langchain Important Components - Models
2. 26.2Langchain Components- Prompts
3. 26.3Langchain Components- Chains and Indexes
4. 26.4Langchain Components- Memory and Agents
5. 26.5Introduction To Document Loaders
6. 26.6Text Loaders in Document Loaders
7. 26.7PDF Loaders in Document Loaders
8. 26.8DirectoryLoader in Document Loaders
9. 26.9Text Splitters in Langchain
10. 26.10WebBaseLoader and CSVLoader in DocumentLoaders
11. 26.11Length based Text Splitter in Text Splitter
12. 26.12Text Structure Based Text Splitter
13. 26.13Document Structure and Semantic Meaning based Text Splitting
14. 26.14Introduction To Embedding Models
15. 26.15OpenAI and HuggingFace Embedding Models
16. 26.16Document SImilarity Seach Application with OpenAI Embeddings
17. 26.17Ollama Embeddings Model
18. 26.18Getting Started with Vector Stores
19. 26.19Vector Stores vs Vector Database and ChromaDB Introduction
20. 26.20FAISS Vector Store
21. 26.21Working with ChromaDB

27 Getting Started with OpenAI and OLLAMA and Creating RAG 7 lessons ▶

1. 27.1Getting Started with Retrievers
2. 27.2Wikipedia Retriever & Vector Store Retriever
3. 27.3Maximal Marginal Relevance Retriever
4. 27.4Multi Query & Contextual Compression Retriever
5. 27.5What is RAG (Shifting From Fine Tuning To RAG)
6. 27.6YouTube Content Chatbot(RAG Application)
7. 27.7Simple ChatBot App with Ollama Model and Tracking using Langsmith

28 Building Basic LLM Application using LCEL 12 lessons ▶

1. 28.1What is Prompting and Prompt Templates
2. 28.2Messages and ChatPromptTemplate
3. 28.3Structured Output From LLM and Pydantic
4. 28.4Creating Schema Response using Pydantic
5. 28.5Output Parsers in Langchain and StrOutputParser
6. 28.6Json and Pydantic Output Parser
7. 28.7Chains in Langchain
8. 28.8Parallel and Conditional Chains
9. 28.9What are Runnables and LCEL
10. 28.10Working With Groq API
11. 28.11Creating APIs for App using FastAPI
12. 28.12Serving Groq App using FastAPI

29 Building Chatbots with Conversation History using Langchain 4 lessons ▶

1. 29.1Building ChatBots with Message History
2. 29.2Messages Placeholder
3. 29.3Trimming Chat History
4. 29.4Q & A RAG Chatbot with Message History

30 Conversational Q & A ChatBot with Message History 1 lesson ▶

1. 30.1Q & A RAG Chatbot with Message History

31 End to End Q and A ChatBot Gen AI App 3 lessons ▶

1. 31.1Setup of Project with Environment and Keys
2. 31.2Creating The OpenAI Application
3. 31.3Creating Ollama Open Source Model

32 RAG Document Q&A with GROQ API and LLAMA3 2 lessons ▶

1. 32.1Getting Started with ChatGroq RAG Application
2. 32.2Creating the End to End Groq Application

33 Conversational Q & A Chat with PDF ChatBot with Message History 2 lessons ▶

1. 33.1Getting Started with Application
2. 33.2Creating The Application

34 AI Agents and Search Engine with Langchain Tools 7 lessons ▶

1. 34.1Tools in AI Agents and Built In Tools
2. 34.2Creating Custom Tools and Toolkit
3. 34.3Tool Binding For Tool Calling
4. 34.4Tool Calling and Tool Execution
5. 34.5What is an AI Agent
6. 34.6Creating an AI Agent
7. 34.7Working of an React AI Agent

35 Chat with SQL DB with Langchain SQL Toolkit and Agenttype 4 lessons ▶

1. 35.1Storing Data in Sqlite Database
2. 35.2Connecting To SQL Database and Overview of Agentic App
3. 35.3Creating Langchain Database Engine
4. 35.4Connecting SQL Agent with Database

36 Text Summarization with Langchain 3 lessons ▶

1. 36.1Text Summarization Using Langchain Prompt Template
2. 36.2Stuff Summarization Chain
3. 36.3Map Reduce and Refine Summarization Chain

37 Text To Math Problem Solver 2 lessons ▶

1. 37.1Getting Started with Text to Math App
2. 37.2Creating Agent and Session State For App

38 Huggingface and Langchain Integration 2 lessons ▶

1. 38.1Getting Started with HuggingFace Models and Working with it
2. 38.2Working with Chat prompt template with Huggingface

39 PDF Query RAG with Langchain and AstraDB 2 lessons ▶

1. 39.1Getting Started with Astra DB using DataStax
2. 39.2Creating RAG App with Astra DB

40 Working with CodeLlama with Ollama 2 lessons ▶

1. 40.1Getting Started with Working with Codellama
2. 40.2Creating the CodeLlama Application

41 Deployment of Gen AI Apps 2 lessons ▶

1. 41.1Deployment in Streamlit Cloud
2. 41.2Deployment in Huggingface Space

Tools you'll use

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