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Best Gen AI & Agentic AI Training Institute in Hyderabad

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Gen AI & Agentic AI SSSIT

5.0 Created by potrace 1.15, written by Peter Selinger 2001-2017

5.0 Created by potrace 1.15, written by Peter Selinger 2001-2017

4.6 Created by potrace 1.15, written by Peter Selinger 2001-2017

Best Gen AI & Agentic AI Training Institute in Hyderabad, Kukatpally & KPHB

SSSIT Computer Education is rated as one of the Best Gen AI & Agentic AI Training Institutes in KPHB, Kukatpally and Hyderabad by trained students. Here Trainers are highly qualified & experienced in delivering Training and Development delivers the content as per industry expectation from a Gen AI & Agentic AI Expert. The Gen AI & Agentic AI Training class consists of more project oriented scenarios with the Industry Aligned Curriculum

What is Gen AI & Agentic AI?

You will be exposed to the following Gen AI & Agentic AI training content

  • Python Programming
    • Introduction to Data Science
    • Python Basics
    • Python Data Types and Utilities
    • Control Structures and Loops
    • Strings
    • Exception Handling
    • Collection Framework
    • List
    • Set
    • Tuple
    • Dictionary and Dictionary Comprehension
    • Functions
    • Inbuilt vs User Defined Functions
    • Function Arguments and Types
    • Global Variable vs Local Variable
    • Packages
    • Lambda Functions
    • Map and Reduce
    • Object Oriented Programming (OOP)
    • Class and Object
    • Methods
    • Decorators
    • Polymorphism
    • File Handling
    • Docstrings
    • Modularization
    • Pickling and Unpickling
    • NumPy
    • Pandas
    • Matplotlib
    • Seaborn
    • SciPy
    • Statsmodels

  • Introduction
    • Types of Data
    • Measures of Central Tendency
    • Descriptive Statistics and Measures of Symmetry
    • Measurement of Spread
    • Levels of Data Measurement
    • Variable
    • Types of Data
    • Measures of Central Tendency
    • Descriptive statistic Measures of symmetry
    • Measurement of Spread
    • Measurement of Spread
    • Levels of Data Measurement
    • Variable

  • SQL Introduction
    • Keys
    • Constraints
    • CRUD Operations
    • SQL Languages
    • SQL Commands
    • Operators
    • Wild Cards
    • Aggregate Functions
    • SQL Joins

  • ML Introduction
    • What is Machine Learning?
    • Types of Machine Learning Methods
    • Classification problem in general
    • Validation Techniques: CV, train accuracy, test accuracy
    • Different types of metrics for Classification
    • Feature Transformations
    • Overfitting and Underfitting
    • Error Measures
  • Data Processing
    • Creating Training and Test Sets, Data Scaling and Normalization
    • Feature Engineering: Adding New Features as Required, Modifying Data
    • Data Cleaning: Treating Missing Values and Outliers
    • Data Wrangling: Encoding, Feature Transformations, Feature Scaling
    • Feature Selection: Filter Methods, Wrapper Methods, Embedded Methods
  • Regression
    • Introduction to Regression
    • Mathematics Involved in Regression
    • Regression Algorithms
    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Regression
    • Lasso Regression
    • Ridge Regression
    • Elastic Net Regression
  • Evaluation Metrics for Regression
    • Mean Absolute Error (MAE)
    • Mean Squared Error (MSE)
    • Root Mean Squared Error (RMSE)
  • Classification
    • Introduction
    • K-Nearest Neighbors
    • Logistic Regression
    • Support Vector Machines (Linear SVM)
    • Linear Classification
    • Kernel-Based Classification
    • Non-Linear Examples
    • 2 Features Form Straight Line and 3 Features Form Plane
    • Hyperplane and Support Vectors
    • Controlled Support Vector Machines
    • Support Vector Regression
    • Kernel SVM (Non-Linear SVM)
    • Naive Bayes
    • Decision Trees
    • Random Forest / Bagging
    • AdaBoost
    • Gradient Boost
    • XGBoost
    • Evaluation Metrics for Classification
  • Clustering
    • Introduction
    • K-Means Clustering
    • Hierarchical Clustering
    • Agglomerative clustering
    • DBSCAN Clustering
    • Association Rules
  • Recommendation Engines
  • ML Pipeline
  • ML Model Deployment in Flask

  • Artificial Intelligence
    • Introduction to Neural Network
    • Biological and Artificial Neuron
    • Introduction to Perceptron
    • Perceptron Learning Rule and Drawbacks
    • Multilayer Perceptron and Loss Function
  • Artificial Neural Networks (ANN)
    • What is a Neuron
    • ANN Architecture
    • Neural Network Activation Functions
    • Step Function
    • Linear Function
    • Sigmoid Function
    • Tanh Function
    • ReLU Function
    • Backpropagation vs Forward Pass
    • Gradient Descent
    • Fine-Tuning Neural Network Hyperparameters
    • Number of Hidden Layers and Hidden Neurons
    • Optimizer
    • Loss Functions
    • Finding Optimal Hidden Layers and Hidden Neurons in ANN
    • Forward and Backward Propagation, Epoch
    • Training MLP: Backpropagation
    • Cost Function
    • Introduction to PyTorch
    • Regularization
    • Optimizers
    • Hyperparameters and Tuning
  • TensorFlow Framework
    • Introduction to TensorFlow
    • TensorFlow Basic Syntax
    • TensorFlow Graphs
    • Variables and Placeholders
    • TensorFlow Playground
  • Computer Vision
    • Human Vision vs Computer Vision
    • CNN Architecture
    • Convolution, Max Pooling, Flatten Layer, Fully Connected Layer
    • Striding and Padding
    • Max Pooling
    • Data Augmentation
    • Introduction to OpenCV and YOLOv3 Algorithm
  • Image Processing with OpenCV
    • Image Basics with OpenCV
    • Opening Image Files with OpenCV
    • Drawing on Images with OpenCV
    • Face Detection with OpenCV
  • Video Processing with OpenCV
    • Introduction to Video Basics and Object Detection
    • Object Detection with OpenCV
  • RNN (Recurrent Neural Network)
    • Introduction to RNN
    • Backpropagation Through Time
    • Simple RNN Backward Propagation
    • Input and Output Sequences
    • RNN vs ANN
    • Vanishing and Exploding Gradient Problem
    • End-to-End Deep Learning Projects with Simple RNN
    • Different Types of RNN: LSTM, GRU
  • LSTM
    • Why LSTM
    • LSTM Architecture
    • Forget Gate in LSTM
    • Input Gate and Candidate Memory in LSTM
    • Output Gate in LSTM
    • Training Process in LSTM
    • Variants of LSTM
    • GRU RNN In-Depth Intuition
    • LSTM and GRU End-to-End Deep Learning Project

  • Text Processing
    • Introduction, What is a Token, Tokenization
    • Stop Words in spaCy Library
    • Stemming
    • Lemmatization
    • Lemmatization through NLTK
    • Lemmatization using spaCy
    • Word Frequency Analysis
    • Counter
    • Part of Speech and POS Tagging
    • POS using spaCy and NLTK
    • Dependency Parsing
    • Named Entity Recognition (NER)
    • NER with NLTK
    • NER with spaCy
    • Text Cleaning
    • Texts, Tokens
    • Basic Text Classification based on Bag of Words
  • Document Vectorization
    • Bag of Words
    • TF-IDF Vectorizer
    • Topic Modelling using LDA
    • Sentiment Analysis
    • Email Classification
    • Text Clustering

  • Open AI
    • Introduction to Open AI
    • Generative AI
    • ChatGPT (3.5)
    • LLM (Large Language Model)
    • Classification Tasks with Generative AI
    • Content Generation and Summarization with Generative AI
    • Information Retrieval and Synthesis Workflow with Gen AI
  • Time Series and Forecasting
    • Time Series Forecasting using Deep Learning
    • Seasonal-Trend Decomposition using LOESS (STL) Models
    • Bayesian Time Series Analysis
  • Sequence to Sequence Architecture
    • Encoder and Decoder
    • In-Depth Intuition of Encoder and Decoder
    • Sequence to Sequence Architecture
    • Problems with Encoder and Decoder
  • Attention Mechanism
    • Seq2Seq Networks
    • Attention Mechanism Architecture
  • Transformers
    • What and Why to Use Transformers
    • Understanding the Basic Architecture of Encoder
    • Self-Attention Layer Working
    • Multi-Head Attention
    • Feed Forward Neural Network with Multi-Head Attention
    • Positional Encoding
    • Layer Normalization
    • Layer Normalization Examples
    • Complete Encoder Transformer Architecture
    • Decoder Plan of Action
    • Decoder Masked Multi-Head Attention
    • Encoder and Decoder Multi-Head Attention
    • Decoder Final Linear and Softmax Layer
  • Hugging Face Platform and API
    • Introduction to Hugging Face
    • Hands-on with Transformers, HF Pipeline, Datasets, and LLMs
    • Data Processing, Tokenization, and Feature Extraction with Hugging Face
    • Fine-Tuning using Pretrained Models
    • Hugging Face API Key Generation
    • Project: Text Summarization with Hugging Face
    • Project: Text-to-Image Generation with LLMs using Hugging Face
    • Project: Text-to-Speech Generation with LLMs using Hugging Face
    • Hugging Face Platform and its API

  • Getting Started with Gen AI
    • Introduction to Gen AI and LLMs
    • Pretraining and Fine Tuning
    • Real-Time Support Chatbot Use Case
    • RAG, RLHF, LangChain, Few Shot Learning
    • Llama Model
  • Gen AI Practicals
    • Tasks (Sentiment Analysis)
    • Text Summarization, Translation, Question-Answer Tasks
    • Token Classification, Fill Mask, Text Generation
    • Feature Extraction, Zero Shot Classification
    • Selecting the Right Model for the Task, Preprocessing
    • Model Inference, Working of Softmax
    • More about Tokenizer: AutoTokenizer, AutoModel
    • Introduction to Hugging Face, Downloading a Model from Hugging Face, Execute NLP
  • Transformers Internals and Attention Blocks
    • Model Selection
    • Preprocessing: Tokenizer
    • Postprocessing: Softmax
    • Attention Block and Multilayer Perceptron: Decoding Attention Pattern
    • Attention Pattern: Vectors and Matrices
    • Embedding Matrix and Unembedding Matrix
    • Query Matrix and Query Vector, Key Vector and Key Matrix
    • Attention Mechanism: Embedding Contextual Knowledge
    • Multi Layer Perceptron: Feed Forward Layer
    • How MLP Works
  • Getting Started with RAG
    • Introduction to RAG
    • RAG Practicals (Using Google Colab)
    • RAG Practicals Using Databricks CE and Local
    • Accessing Gated Models
    • RAG Internals Explained
    • RAG Pipeline: Text to Embeddings
    • RAG Pipeline: Store Embeddings and Retrieve Answers with LLM
  • RAG End-to-End Production Pipeline
    • End-to-End Production Grade Pipeline
    • Generating Embeddings
    • Building Retriever and RAG LangChain Creation
    • Registering LangChain and Creating Serving Endpoint
  • LangChain Essentials
    • Understanding LangChain Framework
    • Inferring Large Models on Cloud
    • Working with OpenAI
    • Message Structure: System, Human, AI Message
    • Use Case: Few Shot Learning
    • Prompt Template
    • Task and Chain: Runnable Lambda, Runnable Sequence
    • Runnable Parallel
  • RAG Application using LangChain
    • Advanced RAG Application Use Case with LangChain
    • Document Loaders
    • Chunking Strategies and Embedding Model Selection
    • Retriever Configs, Search Types
    • Conversational RAG Solution
    • RAG Challenges

  • Agentic AI
    • Agentic Behavior in AI
    • Introduction to LangGraph
    • LangGraph Use Case: Natural Language to PySpark DataFrame
    • LangGraph Use Case: PySpark DataFrame to Spark SQL
    • Binding Tools to LLMs Using LangGraph
  • Agentic AI Tools
    • Registering Tools with LLMs in LangGraph
    • Tool Binding with ReAct Architecture
    • State Persistence in LangGraph Using Checkpoints
    • Structured Outputs with Pydantic Models
    • Reducers to Retain Full Conversation History
    • Handling Long Conversations with Message Filtering
    • Dynamic Summarization in LangGraph Agents
    • Persisting LangGraph State with SQLite Checkpointers
  • CRAG and Agentic AI Projects
    • Corrective RAG (CRAG)
    • Building a LangGraph-Based CRAG Application
    • Agentic AI Project: SQL Querying Agent with Tools
    • Designing a Multi-Role LLM System with Agents
  • LLM Fine-Tuning
    • Introduction to LLM Fine-Tuning
    • RAG vs Fine-Tuning
    • Types of Fine-Tuning
    • Full Fine-Tuning and Parameter-Efficient Fine-Tuning (PEFT)
    • LoRA and QLoRA (Reducing Memory and Compute Requirements)
    • Practical Fine-Tuning Demo
    • Hyperparameters

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