AI/ML/GenAI/Agentic AI Job Oriented Program with 8 Certifications
Enroll Now in the AI/ML/GenAI/Agentic AI Job Oriented Program Certifications – Hand-picked & Structured for Your Success!
Videos
120+ Topics
Labs
100+ Labs
Projects
15+ Projects
EXAM QA
1000+ QA's
Course Overview
It’s not just a trend—it’s the reality we’re living in. Traditional roles are being replaced by AI-driven automation, and professionals who don’t adapt are quickly being left behind. You might be great at what you do, but without AI skills, you’re at risk of becoming irrelevant.
But here’s the good news: You have the power to future-proof your career today.
Our AI/ML Job-Oriented Program is designed to make you an AI-ready professional. With 8 industry certifications and real-world hands-on experience, this program ensures that you’re not just learning AI—you’re learning how to apply it to solve business problems, boost your career, and stand out in the job market.
Key Features of the Course
- 8 Certifications from AWS, Azure, and MLOps that demonstrate your expertise
- Real-world projects that you can showcase in interviews.
- Career-ready training that bridges the gap between knowledge.
- Practical, hands-on training in AI/ML, Generative AI, and MLOps
Course Breakdown
Video Courses
Hands-on Labs
Project Work- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Topic: Introduction to Containers
- Topic: Understanding Docker
- Topic: Introduction to Kubernetes (K8s)
- Topic: Kubernetes Basics
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Insights from Our Achievers..
Real people, real results. See how our students transformed their careers in cloud computing and DevOps.
Why Choose Us?
Practice Questions
Get AI/ML-focused questions including full-length and topic-wise quizzes to reinforce your understanding.
Online Courses
Gain access to 10+ hours of structured AI/ML training content, covering foundational to advanced concepts with real-world use cases.
Hands-on Labs
Work on 100+ industry-relevant AI/ML hands-on labs and projects using AWS, Azure, and LangChain tools.
Experts Support
Get personalized support from industry AI/ML experts to resolve your queries and guide your learning.
Job Aspects
Understand the AI/ML job landscape, in-demand skills, and role-based training. Learn how to align your learning with job market expectations and prepare for hiring pipelines.
On-Job Support
Get continuous support even after you start your job. Our experts help you with real-world implementation, resolving blockers, and succeeding in your workplace.
Course Validity
Enjoy 1-year unlimited access to all training videos, labs, and resources for AI/ML job readiness.
Testimonials/ Feedback
Hear from 45,000+ learners who transitioned into AI/ML careers through our job-oriented program.
What Our Trainees Say
Trusted by thousands of satisfied trainees across multiple platforms
Md Saifur Rahman.
Verified Trainee
"Enrolling in K21 Academy was one of the best decisions I've made for my career. Their structured learning path, hands-on labs, and constant support gave me the confidence and skills I needed to succeed."
Samuel Laleye
Verified Trainee
"My experience with K21 Academy was extraordinary. The instructors are highly skilled, and the support team provides extensive and personalized assistance throughout the job search. A special thanks to Manish ."
King Spidey
Verified Customer
"Excellent teaching and support! Nice resources... Howver I felt in Python ML classes student need to work many hours other than class room hours. For example it took 1 week of self study on the NumPy topic, the class room hours on this topic is 3 hours!!!"
Shanky K.
Verified Trainee
"I had great learning experience with K21Academy. The instructors have indepth knowledge of cloud, devops and AI. They're very professional at the same time humble to let you ask any doubts during the live sessions. Their backend support staff is incredible."
Osaghae Wellington
Verified Trainee
"If you are looking to launch a fulfilling and technically skilled career, especially in the cloud, K21Academy is the place to start. My personal journey is a testament to this. I transitioned from a general IT support role to an Azure Cloud Engineer, a transformation entirely powered by K21Academy's program."
Afise Wilfred
Verified Trainee
"I was satisfied with the content of the material and the presentation skills of the lecturer Sonam. I also loved the way K21 Academy manages the students and follow up on them encouraging the to do hands-on and support available , to do certifications, applications for jobs and getting High Paying jobs eventually."
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.
Frequently Asked Questions (FAQs)
We have multiple instructors that are highly qualified (Microsoft, DevOps, AWS, AI/ML or Google) and have command of new technologies like Multi-Cloud, AI/ML & DevOps. All are subject matter experts and are trained by K21Academy for providing online training so that participants get a great learning experience.
Yes, absolutely. Let me explain how.
I always suggest my students to perform practical hands-on labs in order to gain practical knowledge to implement in real world problems. These hands-on labs are included in the training for real world implementations.
Include these real-world experiences in your resume after completing these practical labs if you want to get a higher-paying job. These real-world interactions will count as experience.
We understand that everyone works hard and is pressed for time. Although juggling work, study, and life can be difficult; people who make time for themselves become successful.
The good news is that we offer well-done recorded videos of the LIVE sessions so you can watch and learn at your own pace even if you don’t have time to attend the LIVE sessions. Along with access to the training portal, we also offer you the chance to take this course again for FREE for a limited time.
19 years ago (2004), I moved to UK, & had a job of £2400 salary per month. In order to earn more, I looked at a training from Oracle for 5 days which was worth £3000.
But due to budget constraint, I decided to learn everything on my own but ended up wasting 8 months trying to learn everything on my own. After which, I invested £3000 & A month later, I bagged a big opportunity, a year long contract & many more offers which in return got me 10x of the amount I invested.
Don’t worry. We’ll take you step-by-step hand through the very basics Concepts & help you with every query you get.
We’re here to support you at every step of your learning journey. For anyone who enroll in our training program, we provide a dedicated WhatsApp group and Ticketing System, so you can share your queries there or you can also ask live with expert trainer in the next Live Session you attend.
Check out the Success stories of our customers and get the motivation from them
Here it is :
Our Success Stories
No, there are no such prerequisites for learning this training, a basic understanding of cloud concepts and programming skills, especially in Python. You can just familiarize yourself with Cloud services and foundational AI/ML principles.
Don’t stress about prepping for the interview – we’ve got you covered! Our experts will assist you with everything from creating a great CV to preparing for the interview. We’ll even provide you some sample CVs to give you an idea of what the current job market is looking for. Trust us, we’ve got your back!
Without a doubt, you can start learning with no IT experience.
Definitely, yes. Learning AI/ML is a great way to increase your chances of getting a higher-paying job. This is because there is a high demand for these skills in the job market. In addition, the training will give you hands-on experience, which will make you more marketable to employers.
We’ll help you create a demo account on the cloud so you can practice on your own and gain hands-on experience. Once you have practical knowledge, you can add it to your resume and start applying for jobs.
Yes, you will get the specially curated Step by Step Hands-on lab documents which you can practice.
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 Month 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 & applying to at least 50 jobs & you feel that the program is not worth the money you invested, you will receive a full refund.
Yes, you can attend our demo session, please register yourself here and book your FREE seat at: https://k21academy.com/aimlfreeclass
We help our students to get connected to prospective employers. We help them in identifying the right job from the right job portal and by sharing sample CVs & help them with modifying & preparing their own CV/Resume according to the Job profile/Description.
We prepare them for the Job Interviews by providing sample Interview Questions, and by doing mock interviews with them. We also provide some project work/labs as a part of training only.
Having said that, please understand that we don’t guarantee any placements however if you go through the course diligently and complete the project/labs you will get very good hands-on experience & conceptual knowledge to work with an organization & project.
Having any other queries not listed about? Contact us at contact@k21academy.com & Our Experts will get in touch to sort your queries or connect us on WhatsApp
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1 Year FREE Unlimited Retakes
This offer provides individuals with the opportunity to retake a course or training program for free within the span of 1 year.
Ask Questions Learn & Grow
Emphasizing an interactive learning environment, this feature encourages participants to actively on daily basis.
1 Year On Job Support
Receive assistance and guidance related to the application of their learning in a real-world, job-related context for 1 Year.
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The positive feedback and satisfaction of over 45,000+ customers who have previously engaged with the our and our quality service.
Session Recording for 1 Year
Get access to recordings sessions for a duration of 1 year. This offers the advantage of reviewing content at any time.
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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!
