Data Analytics course Key Benefits

Upcoming Batches

Select Your City

Click Here

Live Virtual Classroom

$ 1299
$ 899
  • Top-Notch Faculty with Rich Industry Experience
  • Exhaustive Curriculum
  • 20+ Assignments and Real-Life Projects
  • Assured Post-Training Support and Guidance
  • Life Time access to Self-Paced Learning
Upcoming Batches


Show All Batches Enroll Now

Program Highlights

ExcelR DevOPs training

Top-Notch Faculty

ExcelR DevOPs training

Exhaustive Course Curriculum

ExcelR Data Analyst training

Job Readiness

ExcelR DevOPs training

Real-life Projects


Skills Covered




Prompt engineering



Tools Covered






Hugging face





  • Beginner Level
  • A Simple Chatbot designed to answer frequently asked questions, providing quick and accurate responses to enhance user interaction and customer service efficiency on a website or platform. Its simplicity makes it ideal for beginners in AI and programming.
  • Intermediate
  • The Random Compliment Generator brightens someone's day by producing unique and heartfelt compliments, leveraging natural language processing to generate positive messages for personal or business use. It's a simple yet powerful tool to spread positivity.
  • Intermediate
  • This tool leverages advanced natural language processing to condense articles into concise summaries, allowing users to quickly absorb information and save time in today's fast- paced world. It's an essential tool for students and professionals alike.
  • Advanced
  • An Email Auto-Completion Tool that predicts and suggests how to finish sentences, improving efficiency in email correspondence and enhancing productivity for professionals and casual users alike. It's a game-changer for business communications.
  • Advanced
  • The Interactive Story Generator crafts engaging narratives that respond to user choices, offering a personalized story experience for entertainment or educational purposes, using complex algorithms to ensure variability and interest. It's an innovative approach to storytelling.
  • Advanced
  • A Cross-Language Document Summarizer that provides quick translations and summaries of documents in multiple languages, streamlining communication in multilingual environments and enhancing global business operations. This tool is invaluable for international organizations.


Case Studies

  • Leveraging LLMs for real-time customer interaction, this AI personal shopping assistant uses chatbot creation and text generation to offer personalized product recommendations, elevating the e-commerce experience.
  • Utilizing GANs, this platform generates innovative design patterns for various industries, aiding in rapid prototyping and offering a creative edge by producing unique, AI- inspired designs.
  • Implementing autoencoders, this precision agriculture tool processes crop imagery to identify health patterns, facilitating informed farm management decisions and enhancing yield through data-driven insights.

Career Progression and Salary Trends

Placement Process - ExcelR


Learning Path

Why ExcelR

ExcelR DevOPs training

Industry-Based Course Curriculum

ExcelR DevOPs training

Value Added Courses: Python and Azure

ExcelR DevOPs training

Work Hands-on with 30+ Assignmentss

ExcelR DevOPs training

Job Readiness Program with our 2000+ partner companies

ExcelR DevOPs training

Support through WhatsApp, Calls, & Emails

ExcelR DevOPs training

Lifetime eLearning Access

Course Curriculum

Generative AI

  • Overview of Generative AI
  • Generative AI vs. Traditional AI
  • Use Cases
  • Understanding AI: Basics and Use Cases
  • Differentiating ML, DL and AI
  • What is NLP?
  • History of NLP
  • NLP End to end workflow
  • Stopwords
  • Tokenization
  • Stemming
  • Lemmatization
  • POS tagging
  • One hot encoding
  • Bag of words
  • Unigram
  • Bigram
  • ngram
  • Word embeddings Skip Gram
  • Word2vec model
  • RNN
  • LSTM Models & GRU Models
  • Transfer learning
  • Encoder-decoder architecture
  • Attention mechanism
  • Transformer
  • BERT
  • Hands-on experience with text translation using the encoder-decoder architecture
  • LLM
  • Use Cases
  • Text Generation
  • Chatbot Creation
  • Foundations of Generative Models & LLM
  • Generative Adversarial Networks (GANs)
  • Autoencoders in Generative AI
  • Significance of Transformers in AI
  • "Attention is All You Need" - Transformer Architecture
  • Reinforcement Learning
  • RLHF
  • Encoder Models i.e.
  • BERT
  • Decoder Models GPT
  • Encoder Decoder Model i.e.
  • T5
  • Real-world applications and case studies of LLMs
  • Instruction fine-tuning
  • Fine-tuning on a single task
  • Multi-task instruction fine-tuning
  • Model evaluation
  • Benchmarks
  • Parameter efficient fine-tuning (PEFT)
  • PEFT techniques 1: LoRA
  • PEFT techniques 2: Soft prompts
  • Lab 2 walkthrough
  • Rouge1
  • BLEU
  • Meteor
  • CIDEr
  • Reinforcement Learning
  • LLM Applications
  • Deployment Strategies
  • Hardware Requirements
  • Langchain: A Framework for LLMs
  • LLM Operations
  • Scalability
  • Best Practices
  • In-depth GCP
  • Model Evaluation
  • Prompt Design
  • Azure ML
  • Azure Cognitive Services
  • Azure Databricks
  • AWS Sagemaker
  • AWS Jumpstart
  • AWS Bedrock
  • Responsible AI
  • Google's Approach
  • Ethical Issues

Capstone Project


  • Foundations of NLP
    • Introduction to NLP
    • Key Concepts and Terminologies
    • NLP Techniques and Algorithms
  • Chatbots and Their Evolution
    • Definition of Chatbots
    • Evolution of Chatbots
    • Types of Chatbots
  • Introduction to OpenAI and LLMs
    • Introduction to OpenAI and LLMs
    • What are LLMs?
    • How do LLMs work?
    • Types of LLMs
    • Practical uses of LLMs
  • Introduction to GPT and ChatGPT
    • Overview of GPT
    • ChatGPT Capabilities
    • GPT Architecture
  • Understanding GPT-3, GPT 3.5, and GPT-4
    • GPT-3 vs GPT-4
    • Advancements in GPT-4
    • Ethical Considerations
  • Setting Up the ChatGPT Environment
    • Accessing OpenAI API
    • API Keys and Rate Limits
    • Setup for Development
  • Building a Simple Chatbot with ChatGPT
    • Conversation Flows
    • GPT in Chatbots
    • Testing and Iteration
  • Training and Fine-tuning ChatGPT
    • Transfer Learning
    • Pre-training and Fine-tuning ChatGPT
    • Data for Training
    • Dataset Preparation
    • Fine-tuning Techniques
    • Model Performance Monitoring
  • Integrating ChatGPT with Other Services
    • Webhooks and APIs
    • Integration with Platforms
    • Chatbots for Social Media
  • Advanced Conversation Design
    • Context and Long Conversations
    • Personality and Tone
    • Advanced Scripting
  • RLHF and ChatGPT
    • Reinforcement Learning Principles
    • Human Feedback in Training
    • Role of RLHF in GPT
  • ChatGPT for Business Applications
    • Customer Service Automation
    • Personal Assistants
    • Sales and Marketing Bots
  • Safety and Ethical Considerations
    • Bias Detection and Mitigation
    • Ethical AI Use
    • Safety Measures
  • The Future of ChatGPT and Conversational AI
    • Trends and Predictions
    • Potential Upgrades
    • Future of AI and Society
  • Future of Chatbots and Conversational AI
    • Beyond ChatGPT: The next frontier
    • Opportunities and challenges

Prompt Engineering

  • Understanding AI: Descriptive vs Generative AI
    • The nature of AI
    • Comparison of descriptive and generative AI
  • Introduction to Natural Language Processing
    • Core concepts in NLP
    • Basics of language understanding
  • Understanding Large Language Models (LLMs)
    • Overview of LLMs
    • Their scope
    • Capabilities
    • Use cases
  • Introduction to GPT & Chat GPT
    • What is GPT
    • Its evolution
    • Generational changes
  • The Fundamentals of Prompt Engineering
    • What is prompt engineering
    • Its importance
    • Types of prompts
  • Content Generation with Prompts
    • Strategies for generating text
    • Video scripts
    • Music using prompts
  • Tokens and Parameters in AI
    • The role and understanding of tokens
    • Introduction to prompt parameters
  • Zero-Shot to Few-Shot Learning
    • Deep dive into zero-shot
    • One-shot
    • Few-shot learning
  • Fine-Tuning AI Model Parameters
    • Introduction to model parameter adjustments
  • Hallucinations and Bias in AI
    • Strategies for managing AI hallucinations and biases
  • Advanced Prompt Engineering Techniques
    • Methods for crafting complex prompts
    • Incorporating creativity and context
  • Refining and Optimizing Prompts
    • Techniques for prompt refinement and iterative improvement
  • Metrics for Evaluating Prompts
    • How to assess prompt quality and performance
  • Human Evaluation of Prompts
    • Techniques for collecting and analyzing human feedback on prompts
  • Testing Prompts on Different Models and Tasks
    • How to assess prompt performance across different AI models and tasks
  • Natural Language Processing
    • Question-Answering Systems
    • Conversational AI
    • Sentiment Analysis
    • Text Summarization
  • Code Generation with Prompt Engineering
    • GitHub Copilot Exploration
  • Image & Video Content Creation using prompt engineering
    • Using Midjourney and other tools. DALL-E 2 and GPT-4: A Comprehensive Overview Exploring the capabilities and limitations of DALL-E 2 and GPT-4 Real-world scenarios, case studies, tool-specific tips
  • Music Generation with Prompt Engineering
    • Create Poem
    • Music
  • Ethics & Bias in Prompt Engineering
    • Ethical considerations
    • AI transparency
    • Responsible AI usage

Capstone Project

Value added courses


  • Python Introduction - Programing Cycle of Python
  • Python IDE and Jupyter notebook


  • Variables
  • Data type

Code Practice Platform

  • create , insert , update and delete operation , Handling erros


  • Operator -Arthmatic ,comparison , Assignment ,Logical , Bitwise opeartor
  • Decision making - Loops


  • While loop, for loop and nested loop
  • Number type conversion - int(), long(). Float ()
  • Mathametical functions , Random function , Trigonometric function


  • Strings- Escape char, String special Operator , String formatting Operator
  • Build in string methods - center(), count()decode(), encode()


  • Python List - Accessing values in list, Delete list elements , Indexing slicing & Matrices
  • Built in Function - cmp(), len(), min(), max(), list comprehension


  • Tuples - Accessing values in Tuples, Delete Tuples elements , Indexing slicing & Matrices
  • Built in tuples functions - cmp(), len ()


  • Dictionary - Accessing values from dictionary, Deleting and updating elements in Dict.
  • Properties of Dist. , Built in Dist functions & Methods, Dict comprehension
  • Date & time -Time Tuple , calendor module and time module


  • Function - Define function , Calling function
  • pass by refernece as value , Function arguments , Anonymous functions , return statements
  • Scope of variables - local & global , Decorators and recursion
  • Map reduce and filter


  • Import statemnts , Locating modules - current directory , Pythonpath
  • Dir() function , global and location functions and reload () functions , Sys module and subprocess module
  • Packages in Python


  • Files in Python- Reading keyboard input , input function
  • Opening and closing files . Syntax and list of modes
  • Files object attribute- open , close . Reading and writing files , file Position.
  • Renaming and deleting files
  • Pickle and Json


  • mkdir methid, chdir () method , getcwd method , rm dir

Exception Handling

  • Exception handling - List of exceptions - Try and exception
  • Try- finally clause and user defined exceptions


  • OOP concepts , class , objects , Inheritance
  • Overriding methods like _init_, Overloading operators , Data hiding

Regular Expressions

  • match function , search function , matching vs searching
  • Regular exp modifiers and patterns

SQLite and My SQL

  • Data base connectivity
  • Methods- MySQL , oracle , how to install MYSQL , DB connection
  • create , insert , update and delete operation , Handling erros


  • Introduction to Django framwork , overview , environment
  • Apps life cycle , creating views
  • Application, Rest API

Intro to Cloud Computing

  • What is cloud computing, why it is important, cloud services, applications, benefits , architectures

Intro to Azure Cloud Platform

  • What is Azure, Why Azure, Azure services, Azure core architecture, core azure services domains, creation of azure account

Azure Cloud Applications

  • Intro to AI/ML services, What is azure ml designer studio, developing ml models, python and r applications in studio

Azure Cloud Services

  • Resource groups, virtual machine concepts , storage service, web apps, databricks environment , azure sql databases, billing etc.

Azure open AI Studio

  • What is azure open ai, open ai documentation, how to use azure open ai studio, creating applications, different models in azure open ai

Contact Our Team of Experts


Global Presence

ExcelR is a training and consulting firm with its global headquarters in Houston, Texas, USA. Alongside to catering to the tailored needs of students, professionals, corporates and educational institutions across multiple locations, ExcelR opened its offices in multiple strategic locations such as Australia, Malaysia for the ASEAN market, Canada, UK, Romania taking into account the Eastern Europe and South Africa. In addition to these offices, ExcelR believes in building and nurturing future entrepreneurs through its Franchise verticals and hence has awarded in excess of 30 franchises across the globe. This ensures that our quality education and related services reach out to all corners of the world. Furthermore, this resonates with our global strategy of catering to the needs of bridging the gap between the industry and academia globally.

ExcelR's Global Presence

Call Us