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Master Python, Machine Learning, Deep Learning, Data Analytics, Generative AI, Agentic AI, LLMs, LangChain, RAG, AI Agents, TensorFlow, NLP, OpenCV & Real-Time AI Projects with 10+ Years Industry Experts and become a Job-Ready AI Engineer.

✔ Python Programming

✔ Machine Learning & Deep Learning

✔ Generative AI & LLMs

✔ LangChain & RAG

✔ Live AI Projects

✔ Placement Assistance

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Data Science & AI Online Real-Time Training

Digital Webglow offers industry-oriented Data Science & AI Training in Hyderabad designed for students, freshers, software professionals, data analysts, and anyone looking to build a successful career in Artificial Intelligence and Data Science.

Learn directly from 10+ Years Industry Experts through live coding sessions, real-world AI projects, interview preparation, certification guidance, resume building, and placement assistance.

Our curriculum covers the latest technologies including Python Programming, Data Analytics, Statistics, Machine Learning, Deep Learning, NLP, Computer Vision, TensorFlow, Pandas, NumPy, Scikit-Learn, Generative AI, Prompt Engineering, LLMs, LangChain, RAG, AI Agents, Agentic AI, OpenAI APIs, Hugging Face, Vector Databases, AI Automation, and Enterprise AI Project Development.

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Why Choose Us?

Data Science & AI Training Highlights

Become a job-ready AI Engineer through hands-on learning, live AI projects, Machine Learning, Deep Learning, Generative AI, LLMs, and placement-focused training.

Python Programming

Learn Python from beginner to advanced including OOP concepts, file handling, libraries, scripting, and automation.

Machine Learning

Master supervised learning, unsupervised learning, regression, classification, clustering, and model evaluation.

Deep Learning

Build Neural Networks, CNN, RNN, TensorFlow, Keras, and AI prediction models.

Generative AI & LLMs

Learn ChatGPT, OpenAI APIs, Gemini, Claude, Prompt Engineering, AI Automation, and Large Language Models.

LangChain & AI Agents

Build intelligent AI Agents using LangChain, LangGraph, RAG, Vector Databases, MCP, and Agentic AI.

10+ Years Expert Trainers

Learn from experienced AI Engineers, Data Scientists, ML Engineers, and industry professionals.

Certification Guidance

Get complete guidance for Python, Machine Learning, Data Science, AI, and cloud certification exams.

Mock Interviews

Practice coding, AI scenarios, ML interview questions, HR interviews, and resume discussions.

Placement Assistance

Resume preparation, LinkedIn optimization, portfolio building, interview support, and job referrals.

Job-Oriented Curriculum

Full Stack Data Science with Artificial Intelligence Course Syllabus

Learn Data Science, Machine Learning, Deep Learning, Generative AI, Agentic AI, Big Data, MLOps, and enterprise AI application development through live projects and practical implementation.

Module 1 : Data Science Fundamentals

  • Introduction to Data Science
  • Data Science Life Cycle
  • Data Acquisition & Data Sources
  • Data Types & Categorization
  • Data Quality & Transformation
  • Real-world Data Science Use Cases

Module 2 : Python Programming

  • Python Fundamentals
  • Variables & Data Types
  • Functions & Lambda
  • Collections
  • File Handling
  • Jupyter Notebook & Google Colab

Module 3 : NumPy

  • NumPy Arrays
  • Array Manipulation
  • Mathematical Operations
  • Linear Algebra
  • Reshaping & Indexing
  • Random Numbers

Module 4 : Pandas

  • Series & DataFrames
  • CSV, Excel & JSON
  • Data Cleaning
  • Filtering & Indexing
  • Grouping & Aggregation
  • Merging & Joining

Module 5 : Data Visualization

  • Matplotlib
  • Line Charts
  • Bar Charts
  • Pie Charts
  • Scatter Plots
  • Histograms

Module 6 : Statistics

  • Descriptive Statistics
  • Inferential Statistics
  • Hypothesis Testing
  • Correlation
  • Sampling Techniques
  • Confidence Intervals

Module 7 : Machine Learning

  • Supervised Learning
  • Unsupervised Learning
  • Regression
  • Classification
  • Clustering
  • Recommendation Systems

Module 8 : Time Series Forecasting

  • ARIMA
  • Facebook Prophet
  • Stationarity Testing
  • Forecast Evaluation
  • Trend Analysis

Module 9 : MLOps

  • CI/CD for ML
  • MLflow
  • Model Monitoring
  • Model Lifecycle Management

Module 10 : Big Data with PySpark

  • Apache Spark
  • PySpark
  • Spark SQL
  • Data Processing
  • Real-Time Big Data Projects

Module 11 : NLP & Text Mining

  • Natural Language Processing (NLP)
  • Text Cleaning & Preprocessing
  • Tokenization & N-Grams
  • Stemming & Lemmatization
  • POS Tagging & Named Entity Recognition
  • Syntax Trees & Text Paraphrasing

Module 12 : Deep Learning & TensorFlow

  • Deep Learning Fundamentals
  • Neural Networks & Perceptrons
  • Activation Functions
  • TensorFlow 2.x
  • Model Training & Optimization
  • Digit Classification Project

Module 13 : Computer Vision (CNN)

  • Convolution Neural Networks
  • Image Classification
  • Pooling & Feature Extraction
  • OpenCV Integration
  • Face Detection
  • Model Saving & Deployment

Module 14 : Autoencoders & RBM

  • Boltzmann Machines
  • Restricted Boltzmann Machines
  • Autoencoders
  • Feature Extraction
  • Deep Representation Learning

Module 15 : Generative Adversarial Networks

  • GAN Architecture
  • Generator & Discriminator
  • Image Generation
  • Recent GAN Models
  • Hands-on GAN Projects

Module 16 : Emotion & Gender Detection

  • Emotion Recognition
  • Gender Prediction
  • OpenCV Projects
  • Haar Cascade
  • Real-Time Detection

Module 17 : RNN & GRU

  • Recurrent Neural Networks
  • GRU Architecture
  • Sequence Learning
  • Backpropagation
  • Time-Series Applications

Module 18 : LSTM Networks

  • LSTM Architecture
  • Forget, Input & Output Gates
  • Sequence Prediction
  • Sequence Classification
  • CNN-LSTM Models

Module 19 : OpenCV

  • Computer Vision Basics
  • Image Processing
  • Object Detection
  • Face Recognition
  • Real-Time Projects

Module 20 : Generative AI

  • LLMs & Transformers
  • GPT, BERT & T5
  • ChatGPT Architecture
  • Open Source LLMs
  • Responsible AI

Module 21 : Prompt Engineering

  • Zero-shot & Few-shot Prompting
  • Prompt Design
  • Instruction Prompting
  • Prompt Evaluation
  • NLP Task Prompting

Module 22 : Advanced Prompt Engineering

  • Chain of Thought (CoT)
  • Tree of Thought (ToT)
  • Self Consistency
  • Prompt Injection
  • Auto Prompting

Module 23 : Working with LLM APIs

  • OpenAI API
  • Gemini API
  • Anthropic Claude
  • Llama & Mistral
  • Function Calling & Tools

Module 24 : LangChain & LlamaIndex

  • LangChain
  • Agents & Chains
  • Memory
  • LlamaIndex
  • Document Indexing

Module 25 : Retrieval Augmented Generation (RAG)

  • RAG Architecture
  • Embeddings
  • Retrievers
  • Hybrid Search
  • Advanced RAG Pipelines

Module 26 : Vector Databases

  • Embeddings
  • ChromaDB
  • Pinecone
  • Weaviate
  • FAISS & Milvus

Module 27 : End-to-End GenAI Applications

  • Chatbots
  • Copilots
  • FastAPI
  • LangServe
  • Cloud Deployment

Module 28 : Enterprise GenAI Use Cases

  • Customer Support AI
  • Legal AI
  • Healthcare AI
  • Finance AI
  • Evaluation Metrics

Module 29 : Multimodal AI

  • GPT-4 Vision
  • Gemini
  • Vision Language Models
  • Speech-to-Text
  • AI Agents (CrewAI, AutoGPT)

Module 30 : LLMOps & Fine-Tuning

  • LLMOps
  • Hugging Face
  • LoRA & QLoRA
  • PEFT
  • Fine-Tuning Large Language Models

Module 31 : Agentic AI Fundamentals

  • Introduction to Agentic AI
  • AI Agents vs Agentic AI
  • Generative AI vs Traditional AI
  • Autonomous AI Agents
  • Human-in-the-Loop Systems
  • Single & Multi-Agent Systems
  • Agentic AI Frameworks
  • Responsible & Ethical AI

Module 32 : Agentic AI Architecture

  • Agent Architectures
  • Design Patterns
  • Building Blocks
  • Planning & Reasoning
  • Memory Management
  • Workflow Orchestration
  • Best Practices

Module 33 : LangChain & LCEL

  • Document Loaders
  • Text Splitting
  • Embeddings
  • Vector Database Integration
  • LCEL (LangChain Expression Language)
  • Chains & Runnables
  • LangServe Deployment

Module 34 : LangGraph AI Agents

  • LangGraph Fundamentals
  • State & Memory
  • Reducers & Schemas
  • Human-in-the-Loop
  • Long-Term Memory
  • Agent Deployment

Module 35 : Agentic RAG

  • Agentic RAG Architecture
  • Adaptive RAG
  • Traditional vs Agentic RAG
  • LlamaIndex RAG
  • Cohere RAG
  • Enterprise Applications

Module 36 : AI Agents with Phidata

  • Phidata Framework
  • AI Agents & Models
  • Knowledge Bases
  • Embeddings
  • Vector Databases
  • AI Workflows

Module 37 : Multi-Agent Systems

  • Multi-Agent Architecture
  • LangGraph Workflows
  • CrewAI Framework
  • Collaborative AI Agents
  • Task Delegation
  • Enterprise Automation

Module 38 : AutoGen Framework

  • Microsoft AutoGen
  • Conversational Agents
  • Human-in-the-Loop
  • Tool Calling
  • Code Execution
  • Agent Deployment

Module 39 : AgentOps & Observability

  • AgentOps
  • LangSmith
  • Langfuse
  • Tracing & Monitoring
  • Prompt Management
  • Workflow Evaluation

Module 40 : No-Code AI Agents

  • No-Code AI Platforms
  • Low-Code AI Development
  • Drag & Drop Workflows
  • Business Automation
  • AI Integrations
  • Deployment Best Practices

Module 41 : OpenClaw Framework

  • OpenClaw Introduction
  • Multi-Agent Systems
  • Role-Based Agents
  • Task Delegation
  • Agent Collaboration
  • Enterprise AI Scaling
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🚀 Start Your Data Science & AI Career Today

Join our Job-Oriented Full Stack Data Science with Artificial Intelligence Training Program and gain hands-on experience in Python, Machine Learning, Deep Learning, NLP, Computer Vision, Generative AI, Prompt Engineering, LangChain, LangGraph, LLMs, RAG, Vector Databases, AI Agents, Agentic AI, OpenAI APIs, TensorFlow, MLOps, LLMOps, real-time industry projects, certification guidance, interview preparation, and dedicated placement assistance.

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