Aniruddha Bhattacharjee Generative AI Engineer
5 Reviews

As a Senior Data Scientist and Generative AI Trainer, I specialize in advanced techniques to extract actionable insights from large datasets, with a strong focus on cutting-edge **NLP (Natural Language Processing)**, **Generative AI**, and **Agentic AI**. My expertise spans creating predictive models, regression algorithms, and sophisticated statistical techniques to uncover key parameters from diverse data sources. I also excel in building data visualizations and dashboards using **Python** and **Tableau** to communicate insights effectively.

I am also a **technical reviewer** for the published book **"Applied Natural Language Processing with PyTorch 2.0"**, where I contributed my expertise to ensure the technical accuracy and depth of content related to NLP and deep learning.

In this program, you’ll be trained by an industry expert with practical experience in these areas, providing you with real-world examples and hands-on projects. I actively encourage my students to engage in **hackathons** and **Kaggle competitions** to apply their learning in a competitive, real-world context.

### **Key Learning Outcomes:**
1. **Interactive doubt-clearing sessions** for complex concepts in NLP and Generative AI.
2. **Building profiles for online hackathons and Kaggle** to enhance your competitive edge.
3. **Assistance in developing a strong GitHub profile** to showcase your NLP and AI projects.
4. **Interview preparation** with a focus on Data Science, NLP, and Generative AI roles.
5. **Quizzes, assessments, and challenges** to reinforce knowledge in Agentic AI and advanced NLP.
6. A hands-on project for **every topic covered**, including **Generative AI** and **NLP-based solutions**.
7. A **capstone project** focused on solving industry-specific problems using advanced NLP and AI models.
8. **Practical experience in deploying machine learning and Generative AI models to production**.
9. **Deep Learning with PyTorch**, including state-of-the-art **NLP models** like **BERT**, **GPT**, and Transformer architectures.
10. Building and fine-tuning **large language models (LLMs)** for sophisticated NLP tasks.
11. Advanced techniques in **Agentic AI** for autonomous decision-making and intelligent systems.
12. **Exploring Azure ML** for cloud-based NLP and AI model deployment.
13. Building **intelligent web apps with Streamlit** to showcase AI models.
14. Deploying AI models to the cloud using **Heroku** and **Docker**, incorporating **CI/CD pipelines**.
15. Mastering **Generative AI**, including the latest models for text, image, and multimodal generation.
16. Implementing **Question Answering (QA)** on structured data using **Langchain** and **LLMs**, creating intelligent, agentic systems.

### **Course Topics Include:**
1. **Mathematical foundations for NLP and AI**.
2. **Advanced Data Visualization techniques** with a focus on NLP and AI insights.
3. **Machine Learning Algorithms** (basic to advanced), including those for **NLP** and **Agentic AI**.
4. **Time Series Forecasting** and its applications in intelligent, self-adaptive systems.
5. **NLP (from basic to advanced)**, including semantic understanding, sentiment analysis, and entity recognition.
6. **Deep Learning** (from foundational to advanced), including neural architectures for NLP and generative tasks.
7. **Advanced Regression and anomaly detection**, integrated with **NLP models**.
8. **MLOps** for **NLP** and **Generative AI model lifecycle management**.
9. **Cloud-based Machine Learning and NLP Solutions** (Azure and other cloud platforms).
10. **Generative AI applications** in creative industries, automating content generation, and more.
11. **Advanced Time Series Forecasting**, leveraging **NLP** for trend analysis.
12. **Agentic AI**: Building autonomous systems capable of intelligent decision-making in dynamic environments.

### **Sample Projects:**
1. **Telecom Churn Prediction** using **NLP** and sentiment analysis on customer feedback.
2. **Credit Card Default Prediction** with **NLP-enhanced feature engineering**.
3. **Sentiment Analysis** on large text datasets using advanced **NLP models** (BERT, GPT).
4. Building a **Contextual Search Engine** with **NLP**, Elasticsearch, and **Generative AI**.
5. Developing **QA and Named Entity Recognition (NER) models** using advanced Transformers like **BERT** and **T5**.
6. **Image Recognition** with Deep Learning combined with **NLP** for multimodal understanding.
7. **Service Contract Analytics** using **NLP** for contract analysis and prediction.
8. **IoT Data Analysis and KPI Discovery**, using **NLP** to extract insights from unstructured data.
9. Building an **AI-driven Chatbot** with **NLP**, conversational agents, and reinforcement learning.
10. **Suspect Claim Analytics** using **Generative AI** to detect anomalies and fraudulent behavior.
11. Advanced **Generative AI** projects using **Large Language Models (LLMs)** for content creation.
12. Implementing **Retrieval-Augmented Generation (RAG)** for more accurate information retrieval.
13. **Multimodal AI** for combining text, image, and video data in decision-making processes.
14. **Agentic AI Project**: Building an autonomous recommendation system that adapts to user preferences and feedback in real-time, mimicking human-like decision-making capabilities.
15. **AI-powered Agentic System**: Develop a **self-learning agent** for optimizing inventory management in dynamic supply chain environments using **Reinforcement Learning**.

Subjects

  • Python Beginner-Expert

  • Machine Learning Beginner-Expert

  • Data Science Beginner-Expert

  • Deep Learning Beginner-Expert

  • Artificial Intelligence Beginner-Expert

  • Natural Language Processing Beginner-Expert

  • Data Science and Machine Learning Beginner-Expert

  • MLOps Beginner-Expert

  • Time Series Forecasting Beginner-Expert

  • Generative AI Beginner-Expert

  • Large Language Models LLMs Beginner-Expert

  • Agentic AI Beginner-Expert

Experience

  • Data Science Trainer (Aug, 2019–Present) at Self-employed, Kolkata
    Data Science Trainer
  • Senior Data Scientist (Dec, 2018–Present) at Industry (MNC)
    • Currently engaged in the creation of a Gen AI Assistant System to help telecom operations in the daily activity for both structured data and unstructured data using large language model to extract best possible answer for a question asked from multisource documents.
    • Fine Tuned code llama and llama-3 model for text to SQL generation task for some specific use cases along with various prompts.
    • Harnessing speech-to-text and text-to-text translation models for automatic speech recognition and seamless translation tasks.
    • I'm actively engaged in prompt engineering work, refining the system's ability to generate responses effectively.
    • Detect anomalies occurring in Microwave nodes resulting degraded user experience using Autoencoder.
    • Anticipating and preemptively identifying RRU faults.
    • Implementing traffic balancing strategies in the core network, utilizing Time Series Forecasting methods using Bi-LSTM across multiple network nodes which won FutureNet MENA Award 2024 in AIOps Category.
    • Implemented custom NER strategy to recognize telecom specific vocabulary with the correct tag using Spacy and BERT based model.
    • Developed a model using Roberta to identify valid question based on the telecom keywords using NER tag as a feature in the sequence classification model.
    • Added domain specific keyword to tokenizer vocab to enhance contextual understanding of Roberta model.
  • Software Developer (Jul, 2014–Nov, 2018) at Industry (MNC)
    1. Production Automation
    2. Application Development with Python
    3. Data Analysis with SQL
    4. Automation of Manual Task using UNIX Shell Script

Education

  • Post Graduate Diploma in Data Science (Oct, 2018–Sep, 2019) from International Institute of Information Technology Bangalore–scored 3.8/4
  • B TECH (Jul, 2009–Aug, 2013) from MAKAUT, KOLKATA

Fee details

    ₹800–2,000/hour (US$8.42–21.05/hour)

    It is negotiable.

Courses offered

  • Generative AI with Large Language Models: Mastering the Future of AI

    • ₹25000
    • Duration: 3 Months
    • Delivery mode: Online
    • Group size: 2
    • Instruction language: English, Hindi, Bengali
    • Certificate provided: No
    Welcome to Generative AI with Large Language Models (LLMs)! In this in-depth course, you will explore the powerful and cutting-edge world of generative AI, learning how to leverage large language models (LLMs) like GPT, Anthropic’s Claude, LLaMA, DeepSeek, and others for a variety of applications. This course covers everything from foundational concepts to advanced topics such as prompt engineering, fine-tuning LLMs, and the integration of new architectures like LangChain, LangGraph, and Agentic AI.

    Generative AI is a game-changer for industries like content creation, customer service, and data analysis, and this course will arm you with the tools and knowledge to implement these technologies in real-world applications. Through hands-on projects, you’ll fine-tune and deploy LLMs, create intelligent systems using Agentic AI, and leverage frameworks like LangChain for building advanced AI workflows.

    By the end of this course, you’ll not only have mastered the fundamentals of generative models but also be able to fine-tune LLMs for specialized use cases and apply them in creative and innovative ways.

    Certainly! Here's an updated version of the course details incorporating your mentioned topics:

    Course Title: Generative AI with Large Language Models: Mastering the Future of AI
    Course Description:
    Welcome to Generative AI with Large Language Models (LLMs)! In this in-depth course, you will explore the powerful and cutting-edge world of generative AI, learning how to leverage large language models (LLMs) like GPT, Anthropic’s Claude, LLaMA, DeepSeek, and others for a variety of applications. This course covers everything from foundational concepts to advanced topics such as prompt engineering, fine-tuning LLMs, and the integration of new architectures like LangChain, LangGraph, and Agentic AI.

    Generative AI is a game-changer for industries like content creation, customer service, and data analysis, and this course will arm you with the tools and knowledge to implement these technologies in real-world applications. Through hands-on projects, you’ll fine-tune and deploy LLMs, create intelligent systems using Agentic AI, and leverage frameworks like LangChain for building advanced AI workflows.

    By the end of this course, you’ll not only have mastered the fundamentals of generative models but also be able to fine-tune LLMs for specialized use cases and apply them in creative and innovative ways.

    Course Content:
    Module 1: Introduction to Generative AI and Large Language Models
    Lesson 1.1: What is Generative AI?
    Core Concepts and Applications
    Introduction to LLMs: GPT, LLaMA, Claude, DeepSeek, and more
    Lesson 1.2: Understanding Transformer Architectures
    Deep Dive into the Transformer Model
    Self-Attention Mechanism and its Role in LLMs
    Lesson 1.3: The Rise of Agentic AI
    Defining Agentic AI: Intelligent Autonomous Systems
    Key Applications of Agentic AI in Business and Automation
    Module 2: Working with Popular Large Language Models
    Lesson 2.1: GPT Models (GPT-3, GPT-4, and Beyond)
    Model Architecture and Capabilities
    Fine-tuning LLMs for Text Generation and Other Tasks
    Lesson 2.2: Exploring Anthropic’s Claude and Its Unique Features
    How Claude is Changing the Landscape of NLP
    Comparison between GPT and Claude Models
    Lesson 2.3: Understanding LLaMA and DeepSeek Models
    Introduction to Meta’s LLaMA and its Applications
    DeepSeek: A Powerful LLM for Enhanced Knowledge Retrieval
    Module 3: Advanced Topics in Fine-Tuning and Prompt Engineering
    Lesson 3.1: Fine-tuning LLMs for Specific Use Cases
    Techniques for Fine-tuning GPT, Claude, LLaMA, and other models
    Hands-on Training on Customizing Models for Domain-Specific Tasks
    Lesson 3.2: Prompt Engineering for LLMs
    Crafting Effective Prompts for Optimized Results
    Techniques for Prompt Chaining and Managing Large Workflows
    LangChain for Advanced Prompt Engineering
    Lesson 3.3: Using LangGraph for Workflow Automation with LLMs
    Introduction to LangGraph for AI Workflow Integration
    Building Complex Pipelines for Data Processing with LLMs
    Module 4: Real-World Applications and Projects
    Lesson 4.1: Text Generation and Content Creation
    Use Cases in Creative Writing, Blogging, and Marketing
    Hands-on Project: Building a Content Generation Model Using GPT or Claude
    Lesson 4.2: Building Conversational Agents with Agentic AI
    Designing Intelligent Chatbots with Agentic AI Architectures
    Creating Personalized AI Assistants for Business Applications
    Lesson 4.3: Knowledge Extraction and Summarization
    Using LLaMA and GPT for Text Summarization and Extractive Question Answering
    Project: Building a Document Summarizer with LangChain Integration
    Module 5: Fine-Tuning and Deployment Strategies
    Lesson 5.1: Reinforcement Learning and Fine-tuning with GPT and LLaMA
    How RLHF (Reinforcement Learning from Human Feedback) Improves LLMs
    Hands-on Exercise: Fine-tuning with Reinforcement Learning Techniques
    Lesson 5.2: Scalable Deployment of Large Language Models
    Cloud-based Deployment on Platforms like AWS, Google Cloud, and Hugging Face
    Managing Resources and Latency for LLM Deployments
    Lesson 5.3: Ethics and Responsible AI with LLMs
    Understanding Bias and Fairness in Generative AI Models
    Ensuring Transparency in AI Systems

    Sample Hands-On Projects:

    1. Project: Conversational Q&A Using LLaMA-based Model
    Description:
    In this project, you will create an intelligent Conversational Q&A System using LLaMA-based models (Meta's LLaMA). This system will allow users to ask questions, and the AI will generate contextually accurate answers in real-time based on its knowledge base. The model will be fine-tuned to understand conversational nuances and give relevant responses. This project can be used for applications such as virtual assistants, customer support chatbots, and interactive AI systems.

    Skills Gained:

    Fine-tuning LLaMA for Q&A tasks.
    Integrating contextual understanding into conversation models.
    Building conversation pipelines that can dynamically respond to user queries.
    Data preprocessing for conversational AI training.
    2. Project: Language Translation Using Phi SLM Model
    Description:
    This project will guide you through creating a language translation system using the Phi SLM model (Sequence-to-Sequence Language Models). Phi SLM is designed to convert text from one language to another, leveraging deep learning techniques like transformer-based architectures. You'll fine-tune the Phi SLM model to handle different translation tasks (such as English to Spanish, French, etc.) and implement features for automatic detection and translation of user input.

    Skills Gained:

    Fine-tuning Phi SLM for translation tasks.
    Language detection and preprocessing for translation pipelines.
    Model evaluation for translation quality (BLEU score, ROUGE, etc.).
    Building and deploying a translation system in Python.

  • Generative AI with LLMs – Hands-On Applications with Chatbots

    • ₹25000
    • Duration: 3 Months
    • Delivery mode: Online
    • Group size: 3
    • Instruction language: English, Hindi, Bengali
    • Certificate provided: No
    This hands-on course introduces learners to the world of Generative AI using open-source large language models (LLMs) such as LLaMA, Mistral, and Falcon. Over 8 weeks, participants will learn to build, customize, and deploy intelligent chatbots and AI agents using cutting-edge tools like LangChain, Hugging Face Transformers, vector databases, and multi-agent frameworks. Key topics include prompt engineering, chatbot architecture, Retrieval-Augmented Generation (RAG), agent orchestration, Multi-Agent Communication Protocol (MCP), and deployment strategies. Through weekly projects and a final capstone, students will gain practical experience building real-world AI applications powered by open-source LLMs.

    Highlights:

    Learn LLM fundamentals and prompt engineering

    Build chatbots using open-source models (LLaMA, Mistral, Falcon, etc.)

    Integrate APIs and vector search (FAISS, Chroma)

    Implement Retrieval-Augmented Generation (RAG) pipelines

    Design autonomous agents and enable Agent-to-Agent communication using MCP

    Deploy full-stack AI applications with custom UI

    Capstone project: Multi-agent chatbot system with real-time knowledge retrieval and collaboration

5 Reviews
5 out of 5

User Photo December 26, 2020

Skilled in Data Science

Taught Machine Learning and has helped me in getting a command over the algorithms.
Gave us interactive online classes even during the Pandemic situation.
Provided full guidance in our final year Projects.

User Photo December 8, 2020

Very good teacher

He taught us in a nice way and also helped us whenever we have any doubts.

User Photo December 8, 2020

ML tution

Really happy to get your help in machine learning.

User Photo December 7, 2020

He is a good teacher

He teaches us very well

User Photo December 10, 2019

Good teacher

He taught us very nicely.

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