Generative Ai Training in Hyderabad

Basics of Python Programming  

• Installation and Setup:
• Installing Python and setting up a development environment (IDEs like PyCharm, VSCode, Jupyter Notebooks)
• Syntax and Basic Constructs:
• Variables and data types (integers, floats, strings, booleans)
• Basic input and output
• Comments and documentation

 Control Structures 

• Conditional Statements:
• if, elif, else Loops:
• for, while
• Loop control statements (break, continue, pass)

Functions

• Defining Functions:
• Parameters and return values Scope and Lifetime:      
•  Local and global variables 
•  Lambda Functions:
• Anonymous functions

Core Data Structures

• Lists:
• Creating, accessing, modifying, and iterating over lists
• List comprehensions Tuples: 
• Creating and using tuples
• Unpacking tuples Sets: 
• Creating and using sets
• Set operations (union, intersection, difference) Dictionaries: 
• Creating and using dictionaries
• Dictionary methods and comprehensions

 Advanced Data Structures  

● Collections Module:

defaultdict, Counter, OrderedDict, deque

File Operations 

• Reading and Writing Files:
• Opening, reading, writing, and closing files Working with different file modes (r, w, a, rb, wb) 
•  Working with CSV and JSON: 
• Reading from and writing to CSV and JSON files using csv and json modules

OOP Basics 

• Classes and Objects:
• Defining classes and creating objects
• Instance variables and methods Class Variables and Methods: 
• Using class variables and class methods Inheritance: 
• Single and multiple inheritance
• Polymorphism and Encapsulation:
• Method overriding
• Private variables and name mangling

Modular Programming 

• Creating and Importing Modules:
• Defining and using modules Packages: 
• Organizing code into packages
• Importing from packages

Error Handling 

• Exception Types:
• Common exceptions (ValueError, TypeError, etc.,)
• Try, Except Blocks: 
• Using try, except, else, and finally
• Custom Exceptions:
• Creating and raising custom exceptions

 Debugging

• Debugging Techniques:
• Using print statements and logging
• Using debuggers (pdb)

Decorators and Context Managers

● Decorators: 

• Function decorators
• Class decorators Context Managers: 
• Using with statement
• Creating custom context managers with __enter__ and __exit__

 Iterators and Generators 

● Iterators: 

• Creating iterators using __iter__ and __next__ Generators: 
• Creating generators with yield
• Generator expressions

Scientific Computing

● NumPy: 

• Arrays and matrix operations Pandas: 
• DataFrames for data manipulation
• Reading and writing data (CSV, Excel)

 Data Visualization

● Matplotlib: 

• Plotting graphs and charts Seaborn: 
• Statistical data visualization

Unit Testing

• Unittest Framework:
• Writing and running tests Test fixtures and test suites 
•  Pytest: 
• Advanced testing with pytest

GenAI and It’s Industry Applications

• Introduction to Generative AI
• AI vs ML vs DL vs NLP vs Generative AI
• Generative AI principles
• What is the role of ML in Gen-AI
• Different ML techniques (Supervised, Unsupervised, Semisupervised & Reinforcement Learning)
• Applications in various domains
• Ethical considerations

 NLP & Deep Learning

• NLP essentials
• Basic NLP tasks
• Different text classification approaches
• Frequency based – Bag of words,TF-IDF, N-gram.
• Distribution Models – CBOW, Skipgram(Traditional approaches)and word2vec, Glove.
• Ensemble Methods (Random Forest, Gradient Boosting, AdaBoost) & Traditional Machine Learning Models – Naïve Bayes, Support Vector Machine (SVM), Decision Trees, Logistic Regression.
• Deep learning techniques – CNNs, RNNs, LSTMs, GRU and Transformers.

 Generative AI Models

• Autoencodes
• VAE’s and applications
• GAN’s and it’s applications
• Different types of GAN’s and applications

 Language Models & Transformer Models

• Different types of Language models
• Applications of Language models
• Transformers and its architecture
• BERT, RoBERTa, GPT variations
• Applications of transformer models

Prompt Engineering

• What is Prompt Engineering
• What are the different principles of Prompt Engineering
• Types of Different Prompt Engineering Techniques
• How to Craft effective prompts to the LLMs
• Priming Prompt
• Prompt Decomposition

 Large Language Models

• Generative AI lifecycle
• What is RLHF
• LLM pre-training and scaling
• Different Fine-Tuning techniques

 LLM’s Embeddings

• What are word embeddings
• What is the use of word embeddings, where we can use it?
• Word Embeddings – Word2Vec, GloVe and FastText
• Contextual Embeddings – ELMo , BERT and GPT
• Sentence Embeddings – Doc2Vec, Infersent, Universal Sentence Encoder

• Subword Embeddings – BPE(Byte Pair Encoding), Sentence Piece
• Usecase of Embeddings.

 Different Chunk Metrics

• What is Chunking
• What is the use of chunking the document
• What are the traditional effective chunking techniques
• What are the problems and limitations with traditional chunking techniques?
• How to overcome the limitations of Traditional chunking ● Advanced Chunking Techniques:
  1. Character Splitting
  2. Recursive Character Splitting
  3. Document based Chunking
  4. Semantic Chunking
  5. Agentic Chunking

 RAG and Advanced RA with Langchain

• What is RAG
• What are the main components of RAG
• High level architecture of RAG
• How to Build RAG using external data sources
• Advanced RAG

Langchain for LLMs

• What is Langchain
• What are the core concepts of Langchain
• Components of Langchain
• How to use Langchain agents

 Vector Databases

• LlamaIndex
• What are Vector Databases
• Why do we prefer Vector Databases over Traditional Databases
• Different Types of Vector Databases: OpenSource and Close Source
• OpenSource: Chroma DB, Weaviate,Faiss,Qdrant
• Close-Source Vector Databases:Pinecone,ArangoDB,Cloud-Based Solutions

Finetuning LLMs

• Supervised Finetuning
• Repurposing-Feature Extraction
• Advanced techniques in Supervised Finetuning -PEFT -LoRA, QLoRA

 LLMs Evaluation

Text based LLMs

Automatic Evaluation: BULE Score, ROUGE Score, METEOR, BERT Score.

Human Evaluation: Coherence, Factuality, Originality, Engagement

• Image based LLMs

Automatic Evaluation: Pixel-level metrics, FID (Frechet Inception

Distance), IS (Inception Score), Perceptual Quality Metrics, Diversity Metrics.

Human Evaluation: Photorealism, Style, Creativity, Cohesiveness  Audio generation LLMs:

Automatic Evaluation: FAD (Frechet Audio Distance), IS (Inception

Score), Perceptual Quality Metrics – PAQM, PAQM – SNR (Signal-to-

Noise Ratio), PAQM – PESQ (Perceptual Evaluation of SpeechQuality)

Human Evaluation:Perceptual Quality – PQ, PQ- Naturalness, PQ- Fidelity, PQ- Musicality, Task Specific Evaluation.

• Video Generation LLMs:

Automatic Evaluation: FVD (Frechet Video Distance), Inception

Score(IS), Perceptual Quality Metrics, Motion Based Metrics – Optical Flow Error, Content-Specific Metrics. 

Human Evaluation: Visual Quality, Temporal Coherence, Content

Fidelit.

LLMops

• Model Deployment and Management
• Scalability and Performance Optimization
• Security and Privacy
• Monitoring and Logging
• Cost Optimization
• Model Interpretability and Explainability.

 LLM’s on Cloud

• Amazon Bedrock, Azure OpenAI

 Different AI Tools

• ChatGPT, Gemini, Copilot