Microsoft Certified Training Program

AI/ML/GenAI/Agentic AI Job-Oriented Program with 8 Certifications

Master AI, Machine Learning, Generative AI, and Agentic AI with cutting-edge tools and technologies. Gain hands-on experience through real-world projects and 8 industry-recognized certifications that will prepare you for the most in-demand roles in AI and Cloud.

15 Weeks

Intensive Training

Live Sessions

Expert Instructors

100% Job

Placement Support

8 Certificates

Microsoft Official

4.5 ( 141 Ratings )
Learners
40000 +

About Our Course

The AI/ML/GenAI/Agentic AI Job-Oriented Program with 8 Certifications is your step-by-step path to becoming job-ready in cloud-powered artificial intelligence and machine learning. You’ll start from the fundamentals and move into deep learning, generative AI, agentic frameworks, and MLOps—all while practicing on 20+ real-world projects and 130+ hands-on labs. Along the way, you’ll prepare for eight top certifications. By the end, you won’t just have a suite of credentials—you’ll have the practical skills and confidence to step into roles like AI/ML Engineer, GenAI Specialist, Agentic AI Developer, MLOps Engineer, or Cloud Data Scientist. 

Who Should Enroll in This Program?

AI Enthusiasts & Researchers

Deepen your understanding of cutting-edge technologies and experiment with real-world AI/ML models, prompt engineering, and generative platforms in guided labs

Data Analysts & Scientists

Advance your analytical capabilities by mastering machine learning, generative AI, and agentic frameworks to create actionable insights and automate data-driven workflows.​

Project Managers & Tech Leads

Bridge business and technical teams by gaining foundational AI/ML knowledge and hands-on experience managing AI initiatives for digital transformation projects.​

Fresh Graduates & Career Switchers

Driven to enter the high-growth world of AI/ML with global certifications, guided projects, and hands-on labs.

Tools Covered

Key Outcomes of This Program

8 Globally Recognized Certifications

Earn AWS, Azure credentials that make your profile stand out to top employers.

Hands-On Experience

Gain hands-on experience with top industry tools and frameworks like AWS SageMaker, Azure ML Studio, Hugging Face, LangChain, and CrewAI through 130+ guided labs, designed to build your practical expertise in AI/ML.

Real-World Projects

Build a portfolio of 20+ industry-focused projects, from fraud detection systems and GenAI chatbots to agentic trading bots and multi-cloud AI deployments—demonstrating proof of capability to hiring managers.

Job-Ready Skills

Master high-demand skills in machine learning, generative AI, agentic frameworks, cloud MLOps, and secure AI—preparing you for diverse roles like AI/ML Engineer, GenAI Specialist, and Cloud Data Scientist.

Career Support & Extras

Receive personalized resume reviews, LinkedIn optimization, mock interviews, and access to career mentoring, ensuring you confidently present your skills and secure offers.

Expert Mentorship & Lifetime Access

Get 24/7 support from AI/ML cloud experts, plus a full year of access to course resources and updates to stay ahead in this rapidly evolving field.

130+ Labs

Practice Labs

200+ Videos

Video Course

20 Projects

Projects Course

500+ QA's

Q&A Exams

Course Break-Down

Lessons:
  • Topic : Overview of AI, ML, DL, and GenAI
  • Topic : Comparison: AI vs ML vs DL vs GenA
  • Topic : Machine Learning vs Traditional Programming
  • Topic : Types of Machine Learning
  • Topic : Common Use Cases for AI/ML & GenAI
Lessons:
  • Topic: Introduction to Python for Machine Learning & Basics
  • Topic: Python Data Structures, Control, and Functions
  • Topic: Introduction to Machine Learning & Key Packages (NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn)
  • Topic: Exploratory Data Analysis (EDA) & Feature Engineering
  • Topic: Supervised Learning (Regression & Classification)
  • Topic: Ensemble Learning & Advanced ML Algorithms
  • Topic: Unsupervised Learning & Clustering
  • Topic: Regularization, Cross Validation, Hyperparameter Tuning, and Dimensionality Reduction
  • Topic: PySpark, SQL, & Data Engineering
  • Topic: Deep Learning with PyTorch & TensorFlow
  • Topic: Neural Networks & Advanced Architectures (CNN, RNN, LSTM)
  • Topic: Deep Learning & Time Series Analysis
Labs:
  • Hands on Lab: Installing Python & Setting up Jupyter Notebook/Google Colab
  • Hands on Lab: Create & Work with Lists & Tuples
  • Hands on Lab: Create & Understand Dictionaries
  • Hands on Lab: Control Statements
  • Hands on Lab: Function Arguments and Class Objects
  • Hands on Lab: Central Tendency
  • Hands on Lab: Matplotlib & Seaborn, Pandas, NumPy
  • Hands on Lab: Build a Simple Machine Learning Classification Model Using Scikit-Learn
  • Hands on Lab: Linear Regression & Logistic Regression
  • Hands on Lab: Support Vector Machine (SVM)
  • Hands on Lab: Decision Tree, Random Forest, XGBoost, Naive Bayes, KNN (K-Nearest Neighbors)
  • Hands on Lab: Model Evaluation Metrics
  • Hands on Lab: Feature Engineering
  • Hands on Lab: Dimensionality Reduction
  • Hands on Lab: Hyperparameter Tuning
  • Hands on Lab: Ensemble Learning
  • Hands on Lab: K-Means Clustering Analysis
  • Hands on Lab: Association Rule Mining
  • Hands on Lab: Frequent Pattern Growth
Lessons:
  • Topic: Introduction to Cloud
  • Topic: Cloud Characteristics
  • Topic: Benefits of Cloud
  • Topic: Capex vs Opex
  • Topic: Cloud Service Models (IaaS, PaaS, SaaS)
  • Topic: Shared Responsibility Model
  • Topic: Cloud Deployment Models (Public, Private, Hybrid)
  • Topic: Choosing a Cloud Platform (AWS, Azure, GCP, OCI)
  • Topic: Multi-Cloud Strategy
Lessons:
  • Topic: Cloud & AWS Overview
  • Topic: AWS Global Infrastructure & Regions
  • Topic: IAM: Users, Groups, Policies & Roles
  • Topic: Compute Services: EC2, Lambda, ECS, EKS, Fargate
  • Topic: Storage & Networking: S3, EBS, VPC, Subnet, Gateway, LB
  • Topic: Database Services: RDS, DynamoDB, ElastiCache
  • Topic: Automation & Monitoring: CloudFormation, CloudWatch, CloudTrail
  • Topic: Application & DevOps Services: SNS, SES, SQS, CodeCommit, CodeBuild
  • Topic: AWS Architecture & Access Methods
Labs:
  • Hands on Lab: Create AWS Free Tier Account
  • Hands on Lab: Set Up CloudWatch Billing Alerts
  • Hands on Lab: Manage AWS Costs & Budgets
  • Hands on Lab: Troubleshoot Billing Issues
  • Hands on Lab: Connect Linux Machine on AWS
  • Hands on Lab: Create & Connect Windows Machine on AWS
  • Hands on Lab: Install & Configure AWS CLI
  • Hands on Lab: Create Ubuntu EC2 Instance
  • Hands on Lab: Create IAM User with Administrator Access
Lessons:
  • Topic: Azure Account & Subscription
  • Topic: Azure Services Overview
  • Topic: IAM & Azure Active Directory
  • Topic: Compute: Virtual Machines, App Service, Functions, Logic App
  • Topic: Compute: Docker & Kubernetes (ACR, ACI, AKS)
  • Topic: Networking & Storage Services
  • Topic: Data Services: SQL, Synapse, Data Factory, Data Lake
  • Topic: Data Protection: Backup & Restore
  • Topic: Monitoring: Log Analytics
Labs:
  • Hands on Lab: Register Azure Free Trial Account
  • Hands on Lab: Switch to Pay-as-you-go Account (Optional)
  • Hands on Lab: Create Budget (Billing Alert)
  • Hands on Lab: Create Windows VM (Quick Start)
  • Hands on Lab: Troubleshoot Connection to VM on Cloud
Lessons:
  • Topic: Amazon Bedrock and Generative AI
  • Topic: Prompt Engineering
  • Topic: Amazon Q
  • Topic: AI, ML, Deep Learning, and GenAI
  • Topic: AWS Managed AI Services
  • Topic: Amazon SageMaker
  • Topic: AI Challenges and Responsibilities
  • Topic: AWS Security Services for AI Solutions
Labs:
  • Hands on Lab: How To Request Access to Bedrock Foundation Models on AWS Account
  • Hands on Lab: Setting Up and Managing Guardrails with Amazon Bedrock Foundation Models
  • Hands on Lab: Watermark Detection with Amazon Bedrock
  • Hands on Lab: Create, Deploy & Manage Amazon Q Business and Amazon Q Apps
  • Hands on Lab: Exploring AWS AI Services with Amazon Comprehend, Translate, Transcribe, and Textract
  • Hands on Lab: Enhancing Clinical Documentation with Amazon Comprehend Medical & Transcribe Medical
  • Hands on Lab: Text & Vector Embedding with Amazon Titan
  • Hands on Lab: Invoke Zero-Shot Prompt for Text Generation
  • Hands on Lab: AI Stylist Creating Personalized Outfit
Lessons:
  • Topic: AI Overview & Fundamentals
  • Topic: Computer Vision with Azure AI
  • Topic: Natural Language Processing (NLP) with Azure AI
  • Topic: Document Intelligence & Knowledge Mining
  • Topic: Generative AI with Azure AI
Labs:
  • Hands on Lab: Explore Azure AI Services
  • Hands on Lab: Explore Automated Machine Learning in Azure Machine Learning
  • Hands on Lab: Analyze images in Vision Studio
  • Hands on Lab: Detect faces in Vision Studio
  • Hands on Lab: Read text in Vision Studio
  • Hands on Lab: Analyze text with Language Studio
  • Hands on Lab: Use Question Answering with Language Studio
  • Hands on Lab: Use Conversational Language Understanding with Language Studio
  • Hands on Lab: Explore Speech Studio
  • Hands on Lab: Extract form data in Document Intelligence Studio
  • Hands on Lab: Explore an Azure AI Search index (UI)
  • Hands on Lab: Explore Copilot in Microsoft Edge
Lessons:
  • Topic: Introduction to Generative AI
  • Topic: Planning a Generative AI Project
  • Topic: Amazon Bedrock: Getting Started
  • Topic: Prompt Engineering Foundation
  • Topic: Amazon Bedrock Application Components
  • Topic: Amazon Bedrock Foundation Models
  • Topic: Amazon SageMaker
  • Topic: LangChain
  • Topic: Generative AI Architecture Patterns
Labs:
  • Hands on Lab: Mitigating Image Bias with Effective Prompt
  • Hands on Lab: Advanced Prompt Techniques
  • Hands on Lab: Extract Insights from Call Transcripts
  • Hands on Lab: Building a RAG Knowledge Management System
  • Hands on Lab: Developing AI-driven question-answer Model
  • Hands on Lab: Craft Prompts & Summarize Text: Playground
  • Hands on Lab: Generate Images with Titan ImageGeneratorG1
  • Hands on Lab: Generating Personalized Service Emails
  • Hands on Lab: Abstractive Text Summarization
  • Hands on Lab: Building Intelligent ReAct Agents
  • Hands on Lab: Text & Vector Embedding with Amazon Titan
  • Hands on Lab: Automating Python Code Generation
Lessons:
  • Topic: Get Started with Azure OpenAI Service
  • Topic: Build Natural Language Solutions with Azure OpenAI Service
  • Topic: Prompt Flow for LLM Apps in AI Foundry
  • Topic: Generate Code with Azure OpenAI Service
  • Topic: Generate Images with Azure OpenAI Service
  • Topic: Implement RAG with Azure OpenAI Service
  • Topic: Responsible Generative AI Fundamentals
  • Topic: LangChain
Labs:
  • Hands on Lab: Deploy a model in Azure OpenAI Studio
  • Hands on Lab: Integrate Azure OpenAI into your app
  • Hands on Lab: Utilize prompt engineering in your application
  • Hands on Lab: Generate and improve code with Azure OpenAI Service
  • Hands on Lab: Prepare to develop an app in Visual Studio Code
  • Hands on Lab: Validate C++ Code errors Using Azure AI Studio
  • Hands on Lab: Generate images with a DALL-E model
  • Hands on Lab: Mitigating Image Bias with Effective Prompts Azure AI
  • Hands on Lab: Invoke Foundation Models for Text Generation Using Advanced Prompt Techniques: Zero-Shot, One-Shot, Few-Shot, and Chain of Thought
  • Hands on Lab: Building RAG Application With Langchain
  • Hands on Lab: Develop a multimodal generative AI app
  • Hands on Lab: Build a custom copilots with prompt flow in the Azure AI Foundry portal
  • Hands on Lab: Create a generative AI app that uses your own data
  • Hands on Lab: Fine-tune a language model for chat completion in the Azure AI Foundry
  • Hands on Lab: Evaluate generative AI performance
Lessons:
  • Topic: AWS Data Ingestion
  • Topic: Amazon EBS and Kinesis Data Streams
  • Topic: Data Transformation & Integrity
  • Topic: Amazon SageMaker & Built-In Algorithms
  • Topic: Model Training, Tuning, and Evaluation
  • Topic: Generative AI Model Fundamentals
  • Topic: Developing Generative AI Applications
  • Topic: Security, Identity, and Compliance, Management and Governance
Labs:
  • Hands on Lab: Build ETL Jobs with AWS Glue
  • Hands on Lab: Amazon Kinesis Data Streams
  • Hands on Lab: Preparing Data for TF-IDF using Sagemaker Notebook
  • Hands on Lab: Glue DataBrew
  • Hands on Lab: Setting Up Jupyter Notebook Environment in SageMaker Studio
  • Hands on Lab: Create & Manage SageMaker Studio: Deploy & Test SageMaker JumpStart Foundation Models
  • Hands on Lab: SageMaker Studio, Canvas, and Data Wrangler
  • Hands on Lab: Build a Bedrock Agent with Action Groups, Knowledge Bases, and Guardrails
Lessons:
  • Topic: Get Started with Azure AI Services
  • Topic: Computer Vision Solutions with Azure AI
  • Topic: Natural Language Processing (NLP) Solutions with Azure AI
  • Topic: Knowledge Mining Implementation with Azure AI Search
  • Topic: AI Document Solutions on Azure
  • Topic: Generative AI Solution Implementation
  • Topic: Planning and Managing an Azure AI Solution
Labs:
  • Hands on Lab: Create a Language Understanding Model with the Azure AI Language Service
  • Hands on Lab: Create an Azure AI Search Solution
  • Hands on Lab: Create a Custom Skill for Azure AI Search
  • Hands on Lab: Enrich a Search Index in Azure AI Search with Custom Classes
  • Hands on Lab: Implement Enhancements to Search Results Using Azure AI Search
  • Hands on Lab: Create a Knowledge Store with Azure AI Search
  • Hands on Lab: Analyze Text with Azure AI Search
  • Hands on Lab: Recognize and Synthesize Speech using Azure AI Speech SDK
  • Hands on Lab: Translate Text with the Azure AI Translator Service
  • Hands on Lab: Use Prebuilt Document Intelligence Models
Lessons:
  • Topic: Data Engineering in AWS (S3, Glue, Athena, Kinesis, EMR)
  • Topic: Data Analysis & Transformation (Time Series, Quicksight, Spark, Data Pipelines)
  • Topic: Machine Learning Fundamentals
  • Topic: Machine Learning with SageMaker
  • Topic: High-Level ML Services (Comprehend, Lex, Rekognition, Forecast, etc.)
  • Topic: Machine Learning Implementation & Operations
  • Topic: Transformer Models & Generative AI
  • Topic: Designing and Implementing ML Systems
  • Topic: Deep Learning & Hyperparameter Tuning
Labs:
  • Hands on Lab: Build a sample chatbot using Amazon Lex
  • Hands on Lab: Create & manage SageMaker Studio: Deploy & Test SageMaker Jumpstart Foundation Models
  • Hands on Lab: Prepare, Analyze Training Data for ML with SageMaker Data Wrangler & Clarify
  • Hands on Lab: Hyperparameter Optimization using SageMaker Amazon SageMaker Automatic Model Tuning (AMT)
  • Hands on Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker
  • Hands on Lab: Build, Train, and Deploy a Machine Learning Model with Amazon SageMaker AI
  • Hands on Lab: Package and deploy classical ML and LLMs easily with Amazon SageMaker
  • Hands on Lab: Prepare, Analyze Training Data for ML
  • Hands on Lab: Train a Deep Learning Model with AWS DL Containers
Lessons:
  • Topic: Explore & Configure the Azure ML Workspace
  • Topic: Experiment with Azure Machine Learning
  • Topic: Optimize Model Training with Azure ML
  • Topic: Manage and Review Models in Azure ML
  • Topic: Deploy and Consume Models with Azure ML
  • Topic: Develop Generative AI Applications in Azure AI Foundry
Labs:
  • Hands on Lab: Train a model with the Azure Machine Learning Designer
  • Hands on Lab: Find the best classification model with Automated Machine Learning
  • Hands on Lab: Executing a training script as a command job in Azure Machine Learning
  • Hands on Lab: Run pipelines in Azure Machine Learning
  • Hands on Lab: Perform hyperparameter tuning with a sweep job
Lessons:
  • PyCaret – Low-code ML with built-in experiment tracking
  • MLflow – Model tracking, packaging, and deployment
  • DAGsHub – Collaboration and version control for data and models
  • CI/CD with Git & GitHub – Workflow automation for training and deployment pipelines
  • Docker & Kubernetes – Containerization and scalable model deployment
  • AWS MLOps – Model deployment and monitoring with SageMaker + CodePipeline
  • Azure MLOps – End-to-end ML workflows with Azure ML + DevOps/GitHub Actions 
Labs:
  • Hands on Lab: Versioning and Tracking Models with MLFlow
  • Hands on Lab: Implementing Data Versioning Using DVC
  • Hands on Lab: Building a Shared Repository with DagsHub and MLFlow
  • Hands on Lab: Building and Automating ML Models Using Auto-ML Tools
  • Hands on Lab: Monitoring Model Explainability and Data Drift with SHAP and Evidently
  • Hands on Lab: Creating and Deploying Containerized ML Applications
  • Hands on Lab: Deploying Automated ML Services Using BentoML
  • Hands on Lab: Implementing CI/CD Pipelines with GitHub Actions for MLOps
  • Hands on Lab: Tracking Model Performance and Data Drift Using Evidently AI
  • Hands on Lab: Ensuring Data and Model Integrity with Deepchecks
Lessons:
  • Introduction to LLMs and Tokenization
  • Advanced Prompting Techniques
  • Introduction to RAG Systems
  • Building RAG Systems from Scratch
  • Introduction to AI Agents
  • Advanced AI Agent Frameworks
  • RAG with Hybrid and Multi-Vector Models
  • Automating Agentic Workflows with N8N
  • Designing Explainable and Ethical AI
  • Building Multi-Agent Systems
Labs:
  • Hands on Lab: LangGraph Quickstart Walkthrough
  • Hands on Lab: Setting Up and Running Common Workflows
  • Hands on Lab: Server and Template Quickstarts
  • Hands on Lab: Deployment with LangGraph Cloud
  • Hands on Lab: Developing a Customer Support Assistant
  • Hands on Lab: Generating Prompts Based on User Requirements
  • Hands on Lab: Building a Code Assistant
  • Hands on Lab: Implementing Agentic RAG
  • Hands on Lab: Building Adaptive, Corrective, and Self-RAG Models
  • Hands on Lab: Developing SQL-Integrated Agents for Grounded Reasoning
  • Hands on Lab: Implementing Plan-and-Execute Functionality
  • Hands on Lab: Reasoning Without Direct Observation
  • Hands on Lab: Exploring LLMCompiler for Task Execution
  • Hands on Lab: Building a Network of Agents
  • Hands on Lab: Creating Supervisor-Agent and Hierarchical Team Systems
  • Hands on Lab: Implementing Authentication and Access Control in Agent Systems
  • Hands on Lab: Basic Reflection Implementation
  • Hands on Lab: Building Reflexion and Tree of Thoughts Models
  • Hands on Lab: Developing Self-Discover Agent Systems
  • Hands on Lab: Implementing Agent-Based Evaluation Strategies
  • Hands on Lab: Utilizing LangSmith Evaluators for Performance Metrics
  • Hands on Lab: Web Research with STORM
  • Hands on Lab: Implementing TNT-LLM for Large-Scale Text Mining
  • Hands on Lab: Building Agents for Competitive Programming
  • Hands on Lab: Developing Complex Data Extraction with Function Calling 
Lessons:
  • AI Fundamentals for Project Management
  • AI-Powered Project Planning & Risk Management
  • Building a Project Management Plan for AI Projects
  • AI Tools & Technologies for Project Managers
Labs:
  • Hands on Lab: Using ChatGPT as a Virtual Assistant
  • Hands on Lab: Resource Allocation Using Claude.AI
  • Hands on Lab: Task Creation and Auto-Scheduling with Reclaim.AI
  • Hands on Lab: Generate User Stories for AI Resume Screening System using Deepseek.AI
  • Hands on Lab: Create a Free Trial Account on Asana
  • Hands on Lab: Using AI in Asana
  • Hands on Lab: Create a Free Trial Account on JIRA
  • Hands on Lab: Using AI in JIRA – Atlassian
  • Hands on Lab: Create a Test Account on Monday.com
  • Hands on Lab: Using AI in Monday.com
  • Hands on Lab: Building a Project Plan for AI Resume Screening System 
Lessons:
  • Topic: Introduction to Containers
  • Topic: Understanding Docker
  • Topic: Introduction to Kubernetes (K8s)
  • Topic: Kubernetes Basics

Project works

Project 1: Predict University Admission using Amazon SageMaker Canvas

Leverage Amazon SageMaker Canvas to build a machine learning model that predicts university admission outcomes based on student data.

Project 2: Housing Price Prediction with Amazon SageMaker Autopilot

Use SageMaker Autopilot to automatically build and deploy a model that predicts housing prices based on various features like location and property type.

Project 3: Real-Time Stock Data Processing

Implement a real-time data processing pipeline to track and analyze stock market data, providing immediate insights and predictions.

Project 4: Credit Card Fraud Detection Using Amazon SageMaker

Build a machine learning model on SageMaker to detect fraudulent credit card transactions by analyzing transaction patterns.

Project 5: Building an end-to-end MLOps pipeline using AWS SageMaker

Design and implement a complete MLOps pipeline in AWS SageMaker to automate model training, deployment, and monitoring.

Project 6: Build RAG using AWS Bedrock & SageMaker Notebook

Create a Retrieval-Augmented Generation (RAG) model that integrates with AWS Bedrock and SageMaker Notebook for efficient data retrieval and text generation.

Project 7: Chatbot Using Azure AI Search and OpenAI With Our Own Data

Develop a customized chatbot using Azure AI Search and OpenAI’s models to process and respond to user queries based on your own data.

Project 8: Synthetic Data Generation with LLM

Generate synthetic data using Large Language Models (LLMs) to augment training datasets for machine learning projects.

Project 9: Building RAG Application With Langchain

Create a Retrieval-Augmented Generation (RAG) application using Langchain to improve the retrieval and generation of relevant content for various use cases.

Project 10: AI-Enhanced Advertisement Generation

Use AI to automate the creation of targeted advertisements, enhancing their relevance and effectiveness based on user behavior and preferences.

Project 11: Multi-Tasking Agent with Azure OpenAI

Develop a multi-tasking AI agent using Azure OpenAI to handle various tasks like text generation, data analysis, and decision-making based on user input.

Project 12: Movie Recommender with Azure ML

Create a machine learning model using Azure ML to recommend movies to users based on their preferences and viewing history.

Project 13: Predicting Diabetes using Azure ML

Build a machine learning model with Azure ML to predict the likelihood of diabetes in individuals based on medical and lifestyle data.

Project 14: Credit Card Fraud Detection with Azure ML

Implement a fraud detection model on Azure ML to identify and prevent fraudulent credit card transactions by analyzing transaction data patterns.

Project 15: Building an end-to-end MLOps pipeline using Azure DevOps & ML

Design and deploy a fully automated MLOps pipeline using Azure DevOps and Azure ML for seamless model training, deployment, and monitoring.

Project 16: Autonomous HR Agent for Employee Onboarding

Develop an AI-powered HR agent using Azure tools to autonomously manage the employee onboarding process, from document handling to training schedules.

Project 17: AI-Powered Financial Advisor Agent

Create an intelligent financial advisor using Azure ML to provide personalized financial recommendations and investment strategies based on user data.

Project 18: AI Agent for Legal Document Analysis

Build an AI agent that analyzes legal documents, extracts key information, and provides insights, leveraging Azure ML and OpenAI models.

Project 19: AI Agent for Healthcare Diagnostics Assistance

Design an AI agent to assist healthcare professionals in diagnosing medical conditions by analyzing patient data and providing diagnostic suggestions.

Project 20: AI Agent for E-commerce Customer Support

Implement a conversational AI agent for e-commerce platforms to provide personalized customer support, answer queries, and assist with product recommendations.

Skills You Need to Get Started

AI/ML Concepts

Master core AI/ML topics such as supervised and unsupervised learning, model evaluation, neural networks, and generative AI workflows.

Cloud Fundamentals

Develop a strong foundation in cloud computing, with a focus on AWS and Azure platforms, and learn how to leverage their services for AI solutions.

Programming Essentials

Start with Python, the language of choice for AI/ML projects, and gain experience in writing scripts for data manipulation, automation, and analysis.

Step-by-Step Learning

Progress from AI fundamentals to advanced concepts, including AI model deployment, cloud infrastructure management, DevOps for AI, and multi-cloud integration.

Hands-On Tools

Get practical experience with cutting-edge tools like AWS Bedrock, AWS Rekognition, AWS Lex, Azure AI Foundry, Azure Machine Learning and more, ensuring you're ready for real-world challenges in AI.

Why You Should Enroll

8 Industry Certifications

Earn globally recognized certifications in AI, Machine Learning, Generative AI, and Agentic AI, validating your skills and opening doors to high-paying AI/ML roles.

Hands-On Experience

Practice on 130+ labs and real-world projects, including building AI agents, implementing Generative AI models, and working with advanced tools like AWS Bedrock, Azure AI services, and LangChain.

Career Support

Receive personalized assistance with resume building, mock interviews, and tailored job strategies to fast-track your career in AI and Cloud technologies.

Higher Earning Potential

Position yourself for promotions, career transitions, and top salaries with in-demand expertise in AI, GenAI, and multi-agent AI systems.

Flexible Learning

Learn at your own pace with full access to training materials, hands-on labs, and course recordings for one year, so you can revisit anytime to reinforce your knowledge.

Why Choose Us for the AI/ML/GenAI/Agentic AI Job-Oriented Program (8 Certifications)?

Hands-On Learning

We focus on practical training with 130+ labs and projects—no boring theory.

Query Support Anytime

Get your doubts cleared quickly via WhatsApp, Ticketing System, or during live Q&A sessions.

Weekly Live Interactive Sessions

Learn directly from experts, ask questions, and stay on track with your learning.

Proven Roadmap

Designed by Atul, who has 20+ years of IT experience (a century in cloud years), our structured path takes you from beginner to certified professional.

Ongoing Guidance

Even after getting a job, you’ll continue to receive mentorship and support to succeed in real-world projects.

Course Validity

Get 1-year unlimited access to training materials, labs, and recordings. Learn at your own pace and revisit topics whenever needed.

Testimonials/Feedback

Join 45,000+ learners worldwide who have upskilled with us, transformed their careers, and landed high-paying cloud and data roles. Learn from their success stories—and start creating your own.

What Our Trainees Say

Trusted by thousands of satisfied trainees across multiple platforms

Insights from Our Achievers..

FAQs – Frequently Asked Questions

Do I need coding knowledge to join this program?

Not at all! We will start with the basics of Python programming. No prior coding experience is needed. You’ll be guided step by step throughout the course.

Our instructors are certified experts from top organizations (Microsoft, AWS, Google, etc.), with real-world experience in AI, ML, GenAI, and Agentic AI. They are skilled at delivering engaging and practical online sessions to ensure a great learning experience.

Absolutely! Many of our learners started with little to no experience. With 8 certifications, hands-on projects, and dedicated interview preparation, you’ll be well-prepared for a job in AI/ML, even as a beginner.

You’ll have direct access to expert support and a dedicated community to quickly resolve any doubts or questions you may have. 

While certifications aren’t mandatory, they do enhance your resume and make you stand out to recruiters. We highly recommend applying for jobs as soon as you feel ready, even before receiving certifications.  

Yes, you can! Many learners from non-IT backgrounds have successfully transitioned into AI/ML roles after completing this program, gaining hands-on experience and in-demand skills. 

Yes! You’ll work on 100+ hands-on labs and real-world projects, such as building AI agents, implementing GenAI models, and working with multi-agent systems in cloud environments.

We provide comprehensive interview preparation, including mock interviews, practice questions, and tips specifically for AI/ML/GenAI roles, ensuring you’re confident and ready for the job market.

Many of our learners have experienced significant salary increases after transitioning into AI/ML roles. While salary depends on experience and location, this program equips you with the skills to land high-paying positions.

While we cannot guarantee a job, we offer robust job placement support, including interview prep, CV assistance, and job application guidance. With 8 certifications, hands-on labs, and real-world experience, you’ll be equipped for roles such as AI Engineer, Data Scientist, Machine Learning Engineer, or Cloud AI Architect. Many learners have successfully transitioned into high-paying AI/ML careers after completing the program.

Investing in your future is crucial. Whether you aim for a high-paying job, a career upgrade, or the chance to work on innovative AI projects, this program helps you reach your goals. We also offer flexible payment options.

Yes! Many learners with career gaps have successfully transitioned into AI/ML roles. The program is designed to guide you from fundamentals to advanced concepts, helping you regain confidence and restart your career.

Yes! You will have 1-year unlimited access to course recordings, labs, and materials. Learn at your own pace, and revisit the content anytime. Don’t let time hold you back from advancing your career.

6 Months Money Back Guarantee

When you join the K21Academy, you are fully protected by our 100% Money back guarantee.

We strive to provide the best training programs, but if you don’t get the desired results even after following every step of our learning style, you can claim your money back! 100% money-back guarantee covers the price of online training.

You have 6 Months from the date of the original purchase, to claim a refund. All you will be required to do is, show us the proof that you took action and attended sessions, completing the hands-on labs, Projects & applying to at least 50 jobs & get CV Reviewed (share proof) & you feel that the program is not worth the money you invested, you will receive a full refund.

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My 24+ Years of Experience with over 45,000+ trainees

I started my IT career in 2000 as an Oracle DBA/Apps DBA. The first few years were tough (<$100/month), with very little growth.

In 2004, I moved to the UK. After working really hard, I landed a job that paid me £2700 per month.

In February 2005, I saw a job that was £450 per day, which was nearly 4 times of my then salary.

So I looked at training from Oracle for 5 days. In November, I successfully transitioned to Oracle Security & IAM, and my career took off.

Around 2012–13, Cloud, DevOps & Cloud Automation were gaining popularity & there were many job opportunities in these fields.

So, I decided to make a change in my career path, and I transitioned from working on On-premises (Security, Infrastructure & Databases) to focusing on Cloud & DevOps.
Learning Cloud & DevOps gave me the opportunity to work with some of the world’s largest and most prestigious clients.

I then used the same roadmap with 45,000+ individuals (like you) to help them get their dream jobs.

If they can do it, you can do it too!