Meet Your Instructor

A Globally recognized expert in AI

With nearly two decades of experience, Mohammad Arshad has successfully enabled businesses to monetize their data and AI products. His technical and strategic expertise has helped 5 of the largest companies in the world, 10 SMEs, and 3 startups build effective Data and AI Strategies.

He founded Decoding Data Science in 2020, a successful AI Strategy consulting practice, and expanded into education in 2022 with the launch of DDS Academy.

Accenture, HP, Dell, LinkedIn, MAF, and other leading companies have recognized Mohammad as a Data Science and Strategy expert since 2005.

Mohammad Arshad has been teaching technical and non-technical audiences since 2008, making him a seasoned mentor and coach in the industry. He has helped 3000+ individuals get their Dream jobs.

Linkedin: 58K + Followers

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Proven Success

High employment rate with residents hired by top companies worldwide.

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Residents from over 50 countries have successfully completed the program.

Easy To Use

Project-Based Learning: Build your portfolio with real projects from leading companies

Monetize Your Learning

Freelancing, Startups and Collaborations

Flexibility

Online and self-paced program to accommodate your schedule.

Project-Based Learning

Build your portfolio with real projects from leading companies

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What will you learn?

Cohort Starting 12 APR 2025 The most beginner-friendly curriculum in Generative AI.

Zero to Pro in 4 months.

1. Understanding Generative AI

--- 1.1 What is Generative AI?

--- 1.2 Generative AI in Action

--- 1.3 Generative AI in Action

2. Python Basics

--- 2.1 Python Essentials for Beginners

--- 2.2 Writing Your First Python Program

--- 2.3 Control Flow and Functions in Python

3. Setting Up Your Environment

---3.1 Installing Python and IDEs

---3.2 Working with Virtual Environments

---3.3 Introduction to Command Line for Python

1. UI Design Principles

---1.1 UI Design Essentials

--- 1.2 Tools for Designing Application Interfaces

---1.3 Prototyping Your Application Interface

2. Version Control with Git

---2.1 Introduction to Git and Version Control

---2.2 Common Git Commands

---2.3 Resolving Git Conflicts

3. Common Git Commands

---3.1 Setting Up and Using GitHub

---3.2 Collaborating with Teams on GitHub

---3.3 Managing GitHub Repositories Effectively

1. Understanding GROQ

---1.1 Introduction to GROQ

---1.2 Why GROQ is Important for AI Applications

---1.3 Exploring GROQ Syntax and Functions

2. Working with APIs in Python

--- 2.1 Understanding APIs and REST Principles

--- 2.2 Making HTTP Requests with Python

--- 2.3 Parsing API Responses

3. Building Your First Python App

---3.1 Structuring a Python Application

---3.2 Integrating APIs into Your Python Application

3.3 Error Handling and Debugging in API Calls

1. Understanding Large Language Models (LLMs)

---1.1 Introduction to LLMs

---1.2 How LLMs Generate Text

---1.3 Open Source and Closed Source

---1.4 Popular LLMs in the Industry

2.Exploring the LLM Playground

---2.1 Setting Up and Accessing the Playground

---2.2 Experimenting with Prompts

---2.3 Evaluating Model Outputs

3. Building Practical Understanding

---3.1 Customizing Prompts for Specific Tasks

---3.2 Limitations of LLMs

---3.3 Applications of LLMs Across Industries

1. Basics of Generative AI Configuration

---1.1 Understanding Generative AI Configurations

---1.2 Exploring Key Parameters (Temperature, Max Tokens, Top-p)

---1.3 Customizing Configurations for Specific Tasks

2. Comparing and Choosing LLMs

---2.1 Overview of Major LLMs (GPT, Claude, Llama, etc.)

---2.2 Strengths, Limitations, and Use Cases of LLMs

---2.3 Accessing and Using LLM APIs

3. Building and Presenting Your 2nd Project

---3.1 Implementing the Project with Configured LLMs

---3.2 Documenting Configuration Choices and Results

---3.3 Showcasing Your Project to Stakeholders

1. Introduction to LLM Wrappers

---1.1 Understanding the Purpose of LLM Wrappers

---1.2 Designing Wrappers for Efficiency and Scalability

--- 1.3 Exploring Tools and Libraries for Building Wrappers

2. Managing Function Calls with LLMs

---2.1 Introduction to Function Calls in LLMs

---2.2 Implementing API Calls for AI-Powered Applications

---2.3 Handling Errors and Debugging Function Calls

3. Data Integration Techniques

---3.1 Connecting LLMs to External Data Sources

---3.2 Preprocessing and Cleaning Data for Integration

---3.3 Building Real-Time Data Pipelines for LLMs

4. End-to-End Project: Building an Integrated LLM Solution

---4.1 Defining Project Goals and Architecture

--- 4.2 Building and Testing Your Integrated Solution

--4.3 Documenting and Presenting Your Project

1. Introduction to Hugging Face Ecosystem

---1.1 Overview of Hugging Face and Its Role in AI

---1.2 Key Tools and Libraries in the Hugging Face Ecosystem

---1.3 Exploring Pre-trained Models and Datasets

2. Building Your Open-Source Project

---2.1 Setting Up Your Project Repository on GitHub

---2.2 Selecting and Fine-Tuning Pre-trained Models

---2.3 Creating a Reusable and Documented Project

3. Contributing to the Open-Source Community

---3.1 Understanding Open-Source Contribution Standards

---3.2 Best Practices for Code Quality and Documentation

---3.3 Submitting Pull Requests and Engaging with the Community

4. Finalizing and Showcasing Your Project

---4.1 Testing and Deploying Your Open-Source Project

---4.2 Writing a Clear and Engaging README File

---4.3 Presenting Your Project to the AI Community

1. Understanding Retrieval-Augmented Generation (RAG)

---1.1 What is RAG and Why It Matters

---1.2 What is RAG and Why It Matters

---1.3 Benefits and Use Cases of RAG

2. Introduction to Llama Index

---2.1 Overview of Llama Index

---2.2 Setting Up and Exploring Llama Index

---2.3 Integrating Llama Index with LLMs

3. Building a RAG System with Llama Index

---3.1 Designing a RAG Pipeline

---3.2 Implementing the RAG Workflow

---3.3 Testing and Optimizing the RAG System

4. Finalizing and Presenting Your RAG Project

---4.1 Documenting Your RAG System

---4,2 Deploying the RAG System

---4.3 Showcasing Your Project on linkedin and github

1. Understanding Vector Embeddings

---1.1 What is a Vector Database and Why Use It?

---1.2 Building a Search-Enhanced Generative AI Application

---1.3 Optimizing Query Performance in Vector Databases

2. Introduction to Vector Databases

---2.1 Designing Your Vector Embedding and Database Project

---2.2 Implementing and Testing the Solution

---2.3 Setting Up Your First Vector Database

3. Integrating Vector Databases with Generative AI

---3.1 Combining Embeddings with Generative AI Models

---3.2 Building a Search-Enhanced Generative AI Application

---3.3 Optimizing Query Performance in Vector Databases

4. Building and Deploying a Vector-Powered AI Solution

---4.1 Designing Your Vector Embedding and Database Project

---4.2 Implementing and Testing the Solution

---4.3 Deploying and Demonstrating Your AI Solution

1. Deep Dive into RAG Concepts

---1.1 Revisiting RAG Fundamentals

---1.2 Advanced Retrieval Strategies

---1.3 RAG in Complex Use Cases

2. Advanced Tools and Techniques for RAG

---2.1 Implementing RAG with Vector Databases

---2.2 Optimizing RAG Performance

---2.3 Handling Large-Scale Datasets in RAG

3. Integrating Advanced RAG with Generative AI

---3.1 Combining RAG with LLM APIs

---3.2 Building Context-Aware Generative AI Systems

---3.3 Error Handling and Troubleshooting in RAG Pipelines

4. Designing and Deploying an Advanced RAG Project

---4.1 Planning a RAG Solution for Real-World Applications

---4.2 Implementing Advanced RAG Pipelines

---4.3 Showcasing and Deploying Your RAG Project

1. Understanding Tokenization

---1.1 What is Tokenization?

---1.2 Types of Tokenization (Word, Subword, and Character)

---1.3 Tokenization Challenges and Solutions

2. Preprocessing Data for Fine-Tuning

---2.1 Data Cleaning and Normalization

---2.2 Handling Large and Imbalanced Datasets

---2.3 Formatting Data for Fine-Tuning LLMs

3. Tools and Libraries for Tokenization and Preprocessing

---3.1 Tokenization with Hugging Face Tokenizers

---3.2 Automating Preprocessing with Python Libraries

---3.3 Handling Multilingual and Specialized Data

4. Fine-Tuning Ready: Building and Validating the Dataset

---4.1 Combining Tokenization and Preprocessed Data

---4.2 Validating and Debugging the Dataset

---4.3 Finalizing the Dataset for Fine-Tuning

1. Introduction to Fine-Tuning LLMs

--- 1.1 Why Fine-Tune LLMs?

--- 1.2 Overview of Fine-Tuning Approaches

--- 1.3 Challenges in Fine-Tuning

2. Parameter-Efficient Fine-Tuning (PEFT)

--- 2.1 What is PEFT?

--- 2.2 Low-Rank Adaptation (LoRA) Basics

--- 2.3 Advanced LoRA Concepts

3. Exploring Quantized LoRA (QLoRA)

--- 3.1 Introduction to QLoRA

--- 3.2 Key Optimizations in QLoRA

--- 3.3 Comparing LoRA and QLoRA

4. Hands-On Fine-Tuning Project

--- 4.1 Dataset Preparation

--- 4.2 Model Training

--- 4.3 Deployment and Monitoring

1. What is Chain of Thought (CoT) Prompting?

--- 1.1 What is Chain of Thought (CoT) Prompting?

--- 1.2 Applications of CoT in AI Tasks

no curriculum

Self Paced Course, Materials and Recordings

Pre Work and 2 years Access to the Content

[MC 1] Python Github Profile Generative AI Intro

[MC 2] AI Application Architecture, UI, and Github

[MC 2] How World Wide Web Works ?

[MC3] Introduction to API

[MC 4] Building your first Chatbot using AI to help you

[MC 5] How LLM Works

[MC 6] OpenAI Playground, Configurations & Building First AI Bot

[MC 7] LLM Wrapper and Function Calling

[MC 8] Deploying App in Hugging Face & Intro to Open Source

[MC9] Hugging face & Llama Index

[MC10] RAG and Vectordb using weavite

[MC11] Advanced RAG and FineTuning

[MC11 W] Amazon bedrock+ Langchain

[MC 12 ] Intro to AI agents and ReACT prompting

[MC 13] AI Agents Decoded with Cover Letter Project

[MC 14] Multi-Agent Application with Crew AI

Environment & Packages

IDE & Best Practices

How much Python you need

Python Data Type

Python Data Structures

Control Flow

Functions

Advanced Function: Lamda, String , Random etc

Assigment 1: EMI Calculator

Basic Python Assessment

Numpy

Panda

API & JSON

3 ways of using FAST API

Python for DS Assessment

Build a Weather Application using Python JSON and Weather API

What is FAST API ( Framework) ?

Assignment : Who is the richest?

Assignment : Unicorn companies Analysis

Getting Started with Gradio UI

Get Started with Github

Why do you want to use Git?

How does Git Work?

Install & Configure Git

[PDF] Comprehensive Guide Git

Git for Version Control

Make Changes to Files

Push your Code with Git

Generative AI for busy people

But what is a GPT? Visual intro to transformers

How LLM Works?

Components of LLM

OpenAI Playground & Configuration

Prompt Engineering

Getting OpenAI Key

Generative AI ToolKit

What is Hugging Face ?

Transformers 🤗 Using Pipeline

Working with Pre-trained Models

Sharing Models and Tokenizers

The 🤗 Datasets Library

The 🤗 Tokenizers Library

Hugging Face Spaces with Gradio

Gradio Integration for Interactive NLP Demos

Gradio UI Assignment

LLM Memory & Vector Embeddings

Retrieval-Augmented Generation (RAG)

Llama Index

Langchain and its components

Vector Embeddings & Memory Code Examples

Build a Movie Recommender System

Vector Embedding with Weavite

Deployment using MLflow

Deployment Models and CI/CD in Generative AI

Containerization & what it means to an engineering team

Flowise AI

Multi-Agent Application with Crew AI

Crew AI

Build your own code interpreter

Intro to Agents

OnDemand AI agents

Linkedin Growth

Resume / Portfolio

Enterpeneur Mindset

Communication

Staying upto Date

Project Automate Your Job with AI Agents

Correct way to Apply for Opportunities

Putting your AI Product on Marketplace

Meet your goals with tailored tracks

Forget the one-size-fits-all approach

For Job Seekers

<< Get a job in gen-AI >> Build a killer portfolio of Gen-AI products. Get personalized mentoring on interview prep and resume building. Land your dream GenAI job.$ 15,000+ value in portfolio building and career coaching, with potential salaries averaging $80,000–$120,000 annually

For Founders

<< Build a gen-AI Product >> Create a new Gen-AI product or integrate AI into an existing product. Gain access to our network of 100+ Gen AI startup founders and industry experts to guide you. $35,000+ value in product creation strategies and expert networking opportunities.

For Agency Owners

<< Service businesses with Gen-AI >> Provide full-stack Gen-AI solutions to clients and build a service business. Receive personalized guidance on pricing, lead generation to accelerate your agencies growth. $20,000+ value in operational training and business consulting, with a revenue potential of $50,000–$100,000 in the first year.

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$600 worth of Extra 3 Hours mentorship

Receive personalized guidance with 3 extra hours of one-to-one mentoring, valued at $600. Our expert mentors will help you refine your project, overcome challenges, and maximize your learning experience.

$500 Cloud Credits

Kickstart your AI application with $500 worth of cloud credits. Use these credits to scale your project, deploy your models, and explore advanced cloud-based tools without worrying about infrastructure costs.

$4,000 Worth of AI Guild Content Access

Gain exclusive access to $4,000 worth of AI Guild content. This comprehensive library of AI resources includes tutorials, courses, and industry insights to accelerate your learning and development.

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Get Mentored by the Future, Today

Michael Stattelman

CTO , Falcons.ai

I'm a seasoned innovator, driving cutting-edge technology projects across diverse sectors. Certified by Google and Nvidia in AI development and deployment, I lead transformative initiatives, articulating visionary concepts and igniting collective passion for unparalleled achievements. With deep technical understanding, I ensure visionary ideas translate into tangible plans, yielding concrete results. My impact spans Gaming, Pharma, and Precision Agriculture, leaving a legacy of game-changing projects that showcase the transformative power of strategic leadership in advanced AI.

Priya M Nair

CEO of ZWAG AI Solutions

Priya M. Nair is the Founder and CEO of ZWAG AI Solutions, a Conversational Tech company from the UAE serving clients globally. Using the latest Ai technology powered by our proprietary Language Model: Z-LANG 66B, we build solutions that connect humans and machines to deliver innovative and impactful

Dr Anish Roychowdhury

Consultant & Educator

Anish is a Data Science Consultant and Educator with a total career experience of 20+ years across industry and academia. Having both taught in full time and part time roles at leading B Schools and Technology Schools, including SP Jain School of Global Management, and Plaksha University , Chandigarh. He has also held leadership roles in multiple organizations He holds a Ph.D., in computational Micro Systems from IISc Bangalore), with a Master’s Thesis in the area of Microfabrication, (Louisiana State University, USA) with several peer reviewed publications and awarded conference presentations

Amr Mohamed Hassanin

Founder, CTO for Startups

Tech and Business Leader with over 18 years of experience in software engineering, product development, and tech startups. Holder of an MBA from Middlesex University. Experienced mentor and advisor for startups.

María Carbajal

Tech Lead at Loud Intelligence

I oversee the strategic integration and deployment of innovative technologies across our company. I hold degrees in Electronics Engineering and Computer Science from the Institute of Technology and Higher Studies of Monterrey and an MBA specializing in International Management from ESCP Business School. Additionally, I am accredited in Applied Data Science from Columbia University and hold certifications in Artificial Intelligence, Business, and Blockchain technologies. With over twelve years of diverse professional experience, my career began as a Sales Consultant at Hewlett-Packard, where I developed sales solutions for the United States and Canada.

Safik Hossain

Data Science Manager

Safik is a highly experienced Data Scientist with over a decade of expertise in data analysis, machine learning, and strategic decision-making. With a proven track record in leading high performance data science team and delivering innovative solution across various industries.

Tim Daines

Fractional Product Officer

Tim believes in today's fast-paced world, too much time and money is wasted on product idea meetings. To improve performance, leaders must simplify and accelerate the launch of new experiences, especially in a world where artificial intelligence and humans will co-exist with digital products. Over 15 years, Tim has guided executives and teams to build responsible AI products across healthcare, life sciences, sustainability, and education. Through his design and product roles at Cambridge Consultants (Capgemini), QuantumBlack (AI by McKinsey&Co), Hitachi Vantara (Hitachi), Amazon (AWS), and high-growth startups, Tim has become an expert at simplifying and accelerating the launch of new digital experiences in today's fast-paced world.

Hemant Jain

Life & Career Transformation Coach

I am Success Coach Hemant Jain, a Life & Career Transformation Coach. I help individuals to break their glass walls and ceilings to go higher and achieve more. I work with mid and senior management professionals (who are stuck in their role and haven't had a promotion in 3+ years) to help them with their next big promotion and create a balance in their lives. My Offerings 1. A short-term coaching program on Preparing and Cracking job Interview 2. A 12-month intensive personal transformation Coaching Program for mid and senior level Professionals

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Podcast by 2 AI leaders about AI Residency

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The Next Tech Revolution

Generative AI is at the forefront of the next technological revolution, reshaping industries from healthcare to entertainment.

High Demand, High Reward

As businesses seek to harness the power of AI, demand for skilled professionals in this field is skyrocketing.

Future-Proof Your Career

Gain skills that are not just in demand today but will be essential in the technologically advanced future

Frequently Asked Questions

Can't find an answer? Don't hesitate to reach out!

This course transcends rudimentary AI models like ChatGPT, delving into the intricate engineering facets of Generative Artificial Intelligence. Its primary objective is to furnish participants with hands-on experience in constructing GenAI products from the ground up. The curriculum encompasses advanced concepts and pragmatic applications, equipping learners with a comprehensive understanding of this cutting-edge domain.

This educational endeavor is an ideal fit for individuals harboring an intellectual curiosity about Generative Artificial Intelligence, professionals aspiring to pivot their careers towards the burgeoning field of GenAI, or any individual captivated by the prospect of crafting their own GenAI products from scratch.

You will immerse yourself in practical, hands-on projects that emulate real-world situations, enabling you to gain invaluable experience. These meticulously crafted projects are strategically designed to fortify your comprehension of Generative Artificial Intelligence and facilitate the development of an impressive portfolio, one that showcases your adeptness in constructing AI-driven products with proficiency.

Absolutely, upon the successful culmination of this course, you will be awarded a Professional Certificate from DDS, an esteemed recognition that attests to your hard-earned expertise in the dynamic realm of Generative Artificial Intelligence.

With businesses increasingly seeking to integrate AI into their operations, the demand for skilled GenAI professionals is rapidly growing. This course equips you with the relevant, up-to-date skills that are highly sought after in various industries, future-proofing your career.

What will you master?

Through these tools and frameworks, learn the most important

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We offer a 100% satisfaction guarantee on all of our courses. If you're not completely satisfied within the first 30 days of enrollment, we'll provide you with a full refund, no questions asked.*