• Program Duration

    11 months

  • Learning Format

    Live, Online, Interactive

Why Join this Program

  • icons
    Earn an Elite Certificate

    Program completion certificate from EICTA Consortium, issued by E&ICT Academy, IIT Kanpur

    Program completion certificate from EICTA Consortium, issued by E&ICT Academy, IIT Kanpur

  • icons
    Unlock Exclusive Opportunities

    Live virtual masterclass delivered by IIT Kanpur faculty

  • icons
    Experience Hands-on Learning

    Applied learning through 18+ hands-on projects, including 3 capstone

  • icons
    AI-Powered Job Assistance

    Get AI-powered resume creation, profile optimization, mock interviews, and custom job opportunities

Corporate Training

Enroll your employees into this program, NOW!

GenAI & Machine Learning Course Overview

The Professional Certificate Course in Generative AI and Machine Learning is designed to help you build practical, job-relevant AI skills through a structured learning path. The curriculum covers applied data science, ML, deep learning, and GenAI, with exposure to areas such as natural language processing, reinforcement Learning and agentic AI.

Key Features

  • 11 months of interactive, industry expert-led live virtual learning delivered through a future-ready AI curriculum
  • Exclusive Live virtual academic masterclass delivered by IIT Kanpur faculty
  • Program completion certificate from EICTA Consortium, issued by E&ICT Academy, IIT Kanpur
  • Work on 18+ real-world projects and build a powerful project portfolio, including 3 capstone projects across diverse industry domains
  • Master 30+ industry-leading AI/ML tools, frameworks, and libraries including Python, LangChain, GitHub Copilot, Keras, OpenCV, NumPy, Matplotlib, and more
  • Gain 20+ in-demand AI and ML skills, including model training and optimization, neural network architectures, NLP, speech recognition, GANs, VAEs, transformers, RAG, and more.
  • Participate in the AI Impact Lab featuring hands-on AI use cases led by IIT professor
  • Receive a course completion certificate hosted on the Microsoft Learn portal for Microsoft Courses.
  • 3-day campus immersion at IIT Kanpur

GenAI & ML Certification Advantage

Our curriculum empowers you with the expertise needed to thrive in your career. Through systematic learning and practical industry projects, you'll adeptly address intricate challenges and remain at the forefront of the AI & ML field.

  • Program Certificate

    Program Benefits

    • Certificate from EICTA Consortium, issued by E&ICT Academy, IIT Kanpur, within 45 days of your cohort end date
    • Live-online academic masterclass delivered by IIT Kanpur faculty
    • 3-day campus immersion at IIT Kanpur
  • Microsoft Azure Certificate

    Partnering with Microsoft

    • Receive a course completion certificate hosted on the Microsoft Learn portal for microsoft course

GenAI & Machine Learning Course Details

The program covers diverse subjects including Python programming, supervised and unsupervised learning, deep learning, generative AI, prompt engineering, LLMs, NLP, and much more.

Learning Path

  • IITK AIML: Program Induction

    The session is designed to set context and align expectations for the learning journey ahead. Learners are introduced to the program structure, learning flow, delivery format, and key milestones, along with guidance on how to make the most of live sessions, hands-on components, and assessments. This session also helps learners understand how the program’s components connect, ensuring a smooth, well-paced overall experience.

  • IITK AIML: Mathematics & Statistics Essentials
    • Foundational concepts in coordinate geometry and calculus
    • Introduction to statistics, probability, and data analysis
    • Understanding data types and descriptive measures
    • Sampling techniques and their business applications
    • Hypothesis testing and inferential statistics
    • Correlation analysis and interpreting relationships in data
  • IITK AIML: Python Refresher with AI
    • Programming foundations and core Python concepts
    • Python syntax, variables, data types, operators, and execution flow
    • Working with data structures such as lists, tuples, dictionaries, and sets
    • Conditional statements, loops, and comprehensions
    • Writing modular code using functions and object-oriented programming principles
    • File handling and error handling in Python
    • Introduction to AI-assisted coding and code generation
  • IITK AIML: Applied Data Science with Python
    • Introduction to data science and the end-to-end data analysis process
    • Core Python concepts relevant to data analysis
    • Working with numerical data and arrays
    • Fundamentals of linear algebra and statistics for data science
    • Probability distributions and statistical inference
    • Data manipulation, cleaning, and preparation techniques
    • Exploratory data analysis and hypothesis testing
    • Data visualization principles and techniques using Python libraries
    • Applying statistical concepts to real-world datasets
  • IITK AIML: Machine Learning
    • Introduction to machine learning concepts, types, and workflows
    • Supervised learning techniques for regression and classification
    • Regression models, including linear and non-linear approaches
    • Model evaluation metrics and validation techniques
    • Regularization methods and hyperparameter tuning
    • Classification techniques, including binary, multiclass, and multi-label classification
    • Handling imbalanced datasets using sampling and ensemble-based approaches
    • Ensemble learning methods such as bagging, boosting, and stacking
    • Unsupervised learning techniques, including clustering and dimensionality reduction
    • Recommendation system concepts and common implementation approaches
  • IITK AIML: Deep Learning with Keras and TensorFlow
    • Core concepts of deep learning and how it differs from machine learning
    • Artificial neural networks, including perceptrons, activation functions, and backpropagation
    • Deep neural networks and training workflows
    • Building and training models using Keras and TensorFlow
    • Working with PyTorch for deep learning applications
    • Model optimization techniques, including gradient-based methods, regularization, and dropout
    • Convolutional neural networks for image-based tasks
    • Transfer learning and the use of pre-trained models
    • Recurrent neural networks and sequence modeling techniques
    • Transformer-based models for natural language processing
    • Introduction to autoencoders and unsupervised deep learning approaches
  • IITK AIML: Generative AI Literacy
    • Overview of generative AI and how it differs from traditional AI systems
    • Understanding large language models and generative workflows at a high level
    • Prompt engineering fundamentals and techniques for improving output quality
    • Using generative AI for writing, research, planning, and content generation
    • Practical applications of generative AI for productivity and collaboration tasks
    • Introduction to AI-enabled workflows using tools such as ChatGPT
    • Reviewing, refining, and validating AI-generated outputs
    • Ethical, legal, and security considerations in the use of generative AI
    • Emerging trends in generative AI, including agentic AI and future system capabilities
  • IITK AIML: Advanced Generative AI
    • GenAI model types, including autoencoders, GANs, and transformer-based architectures
    • Attention mechanisms, self-attention, and multi-head attention in transformer models
    • Retrieval-augmented generation (RAG) concepts, workflows, and use cases
    • Fundamentals of large language models, including architecture, training stages, and operations
    • Building GenAI applications using the LangChain framework
    • Advanced prompt engineering techniques, including zero-shot, few-shot, and chain-of-thought prompting
    • Designing structured prompts and prompt templates for complex tasks
    • Fine-tuning and customization of large language models, including reinforcement learning from human feedback (RLHF)
  • IITK AIML: Capstone Project

    The program concludes with a capstone project that brings together the concepts and skills learned across the learning path. Guided by expert mentors, learners work on a real-world business problem and apply AI and generative AI concepts to design an end-to-end solution. The capstone helps reinforce both technical understanding and problem-solving skills, while resulting in a portfolio-ready project that demonstrates practical experience and job readiness to prospective employers.

Electives:

  • IITK AIML: Git and GitHub for AI & ML Professionals

    This elective introduces version control concepts and collaborative workflows using Git and GitHub, which are essential for managing AI and machine learning projects. It focuses on how code is tracked, shared, reviewed, and maintained across teams, with an emphasis on actual development practices. Learners gain practical experience with local and remote repositories, branch management, conflict resolution, and GitHub Actions to automate workflows, helping them understand how modern AI projects are developed and maintained in professional environments.

  • IITK AIML: NLP and Speech Recognition

    This elective introduces the foundations of natural language processing and speech analytics, covering how text and audio data are represented, processed, and analyzed using machine learning and deep learning techniques. It covers core NLP concepts, including text preprocessing, vectorization, embeddings, and sequence modeling, as well as attention-based approaches used in modern language systems. The module also extends into speech recognition by introducing audio analytics, signal-processing fundamentals, feature-extraction techniques, and deep learning approaches for speech and audio generation

  • IITK AIML: Reinforcement Learning

    This elective introduces the core ideas behind reinforcement learning and how agents learn through interaction with an environment. It covers the reinforcement learning framework, key concepts such as rewards, policies, and value functions, and decision-making models, such as Markov decision processes. The module helps learners build strong conceptual understanding and applied familiarity with reinforcement learning techniques through practical examples and simulated environments

  • IITK AIML: Microsoft Azure AI Fundamentals

    This elective introduces key artificial intelligence concepts and explains how AI capabilities are delivered through Microsoft Azure services. It focuses on understanding common AI workloads, such as machine learning, computer vision, natural language processing, and GenAI, and how Azure tools and services can be used to address them. Learners become familiar with the fundamental principles of AI solutions on the Azure platform, including responsible AI practices and the identification of appropriate services for different problem types. This elective provides a foundational understanding of cloud AI and supports preparation for the Microsoft Azure AI Fundamentals certification

  • IITK AIML: AI Impact Lab (Hands-On AI Use Cases with IIT Professors)

    It is a hands-on elective designed to bridge the gap between classroom learning and real-world application. Through guided sessions led by IIT professors, learners work on practical AI use cases that mirror industry problems. The lab focuses on applying supervised learning for insurance risk classification, using convolutional neural networks for retail shelf compliance detection, and leveraging LSTM-based models to forecast telecom network loads. This experience helps learners translate theoretical concepts into practical solutions, strengthening their ability to address business problems using AI techniques

  • IITK AIML: Academic Masterclass by IIT Kanpur

    This elective offers learners the opportunity to engage in an interactive online masterclass led by distinguished faculty members from IIT Kanpur. The session focuses on sharing insights into recent developments and emerging techniques across data science, artificial intelligence, generative AI, and machine learning. Through expert-led discussions and structured presentations, the masterclass helps learners deepen their understanding of how these fields are evolving and connect academic perspectives to real-world relevance

  • IITK AIML: Masterclass on Agentic AI

    This masterclass introduces learners to emerging concepts in agentic AI and the design of AI agents that can plan, reason, and act across multi-step workflows. The session focuses on understanding how agentic systems operate, the role of autonomy in modern AI applications, and how such systems are being explored across different problem contexts. Through interactive discussions and examples, learners gain exposure to the principles behind building intelligent agents and the considerations involved in deploying autonomous or semi-autonomous AI systems in real-world settings

Contact Us

1-844-532-7688

(Toll Free)

15+ Skills Covered

  • Generative AI
  • Prompt Engineering
  • Large Language Models
  • Generative AI Architectures
  • Generative AI Models
  • Machine Learning Algorithms
  • Model Training and Optimization
  • Ensemble Methods
  • Deep Learning
  • Natural Language Processing
  • Computer Vision
  • Reinforcement Learning
  • Speech Recognition
  • Supervised and Unsupervised Learning
  • Model Evaluation and Validation

30+ Tools Covered

AIML_TensorFlowAIML_LangChainAIML_GradioAIML_KerasAIML_PytorchAIML_Pandas_NewAIML_SeabornAIML_SciPyAIML_GitHub CopilotAIML_SciKitAIML_OpenCVAIML_MatplotlibAIML_NumPyAIML_Dall EAIML_Hugging FaceAIML_GitAIML_Google ColabAIML_VScodeAIML_OpenAIAIML_ChatGptAIML_PythonAIML_ChromaAIML_GitHubAIML_GitHub ActionsAIML_NLTK_NewAIML_SpacyAIML_GensimAIML_PydubAIML_LibrosaFSA_AIM_Tool_PyPDF

Industry Projects

  • Project 1

    Use the EpsilonGreedy Method in Music Recommendation

    Build a recommendation system using reinforcement learning that balances exploring new songs with exploiting user preferences to improve personalized music suggestions

  • Project 2

    Stock Trading Using Deep QLearning

    Train a reinforcement learning agent to make trading decisions by predicting profit and loss using deep neural networks on real market data.

  • Project 3

    Sales Strategy Analysis

    Analyze AAL's sales data across different Australian states to identify high-revenue states and develop sales programs for underperforming states.

  • Project 4

    Marketing Campaigns

    Evaluate comprehensive marketing datasets to measure campaign efficiency, attribution, and design A/B testing strategies for maximum ROI on digital campaigns

  • Project 5

    Employee Turnover Analytics

    Deploy machine learning techniques to analyze workforce-related data and predict employee attrition risks, supporting proactive HR strategies

  • Project 6

    Creating Cohorts of Songs

    Leverage unsupervised learning and clustering algorithms to group songs by musical attributes and user preferences for recommendation systems

  • Project 7

    Home Loan Data Analysis

    Develop a deep learning model to predict the likelihood of loan defaults using historical data, ensuring a secure lending process.

  • Project 8

    Lending Club Loan Data Analysis

    Create a deep learning model to predict loan defaults using historical data, addressing an imbalanced dataset with numerous features.

  • Project 9

    Crafting an AI Powered HR Assistant

    Design and implement a conversational AI assistant to automate HR document search and response, integrating advanced NLP and retrieval-augmented generation

  • Project 10

    Creating Designs by Leveraging OpenAI and Gradio UI

    Utilize OpenAI’s generative models and low-code Gradio UI to build creative design prototypes and rapid visual concepts in multiple formats

  • Project 11

    Analyzing Customer Orders Using Python

    Analyze real-world customer order data using Python to derive insights through wrangling, statistics, and visualizations for better business decisions

  • Project 12

    Building a Python Adventure Game using GitHub Copilot

    Use AI to understand the state of historical structures and recommend the best places to visit to tourists

  • Project 13

    Predict CNC Machine Tool Failures With Regression Models

    Implement regression-based predictive maintenance to anticipate equipment failures, reducing downtime and service costs

  • Project 14

    Classify Insurance Risks With Supervised Learning

    Deploy supervised machine learning algorithms to analyze and classify insurance policy risk levels for industry compliance

  • Project 15

    Detect Retail Shelf Compliance Using CNN Based Image Recognition

    Build a computer vision solution with convolutional neural networks to monitor retail shelf standards in real time

  • Project 16

    Forecast Telecom Network Loads With LSTM Models

    Apply LSTM-based recurrent neural networks for forecasting dynamic network loads and optimizing telecom infrastructure performance

  • Project 17

    Autonomous Driving

    Build an AI model using a deep learning framework that predicts the type of vehicle present in an image, analyze the usage of autopilot and its effect on road safety

  • Project 18

    Preserving Heritage Enhancing Tourism With Al

    Develop an Al model using TensorFlow to classify historical structures from images. Perform exploratory data analysis and build a recommendation system

  • Project 19

    Sales Forecasting

    Analyze historical sales data across multiple restaurants and build predictive models to forecast item-level demand, enabling better business planning and decision-making

Disclaimer - The projects have been built leveraging real publicly available datasets from organizations.

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An Immersive Learning Experience

Learn from Industry Experts

Learn from instructors focused to equip you with hands-on skills and industry first curriculum.

Flexi Learn

Missed a class? Access recordings to always maintain learning progress and keep up with your cohort.

Mentoring session(s)

Expert guidance sessions from mentors for doubt clarifications, project assistance, and learning support.

Learning Support

Get a dedicated Cohort Manager for all your queries and help you succeed at every learning step.

Learn from Industry Experts
Learn from instructors focused to equip you with hands-on skills and industry first curriculum.
Flexi Learn
Mentoring session(s)
Learning Support

Program Advisors and Trainers

Program Advisors

  • Amitendra Srivastava

    Amitendra Srivastava

    Chief Data Scientist at Intelytica

    Amitendra’s expertise lies in utilizing data analysis and machine learning techniques to solve complex business problems and drive strategic decisions. As Chief Data Scientist, he leverages the power of data to create value and drive innovation.

  • Raghav Goel

    Raghav Goel

    Generative AI & Data Science Consultant

    A passionate and successful corporate trainer who has delivered 150+ training sessions for corporates in India, Middle East, USA, and South East Asia for corporate clients like Publicis Sapient, KPMG, Capgemini, Coforge, ITC, DXC, Huawei, and IBM.

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Program Trainers

  • Phani Karnati

    Phani Karnati

    20+ years of experience

    Head of AI and ML, VHaVe.ai

  • Manjunatha Gummaraju

    Manjunatha Gummaraju

    28+ years of experience

    AI Agents & GenAI Educator

  • Prashant Nair

    Prashant Nair

    9+ years of experience

    AI Quantum Researcher

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Batch Profile

The diversity of our cohorts brings intensity to classroom conversations & interactions. Our AI and ML course caters to learners from an array of sectors and experiences.

  • Industry
    The class consists of learners from excellent organizations and diverse industries
    Industry
    IT - 40%Consultancy - 30%Other - 15%BFSI - 15%
    Companies
    Amazon
    Cognizant
    Infosys
    Tata Consultancy Services
    Genpact
    Accenture
    Deloitte
    PricewaterhouseCoopers
    Ernst & Young
    Dell
    Citigroup
    Wells Fargo

Learner Reviews

Admission Details

Application Process

The application process consists of 3 simple steps. An offer of admission will be made to the selected candidates and can be accepted by the candidates by paying the admission fee.

STEP 1

Submit Application

Tell us a bit about yourself and why you want to do this course

STEP 2

Application Review

An admission panel will shortlist candidates based on their application

STEP 3

Admission

Selected candidates can join the program by paying the admission fee

Eligibility Criteria

For admission to this GenAI and Machine Learning Course, candidates should have:

A bachelor's degree with an average of 50 percent or higher marks
Basic understanding of mathematics and programming concepts
Preferably, 2+ years of formal work experience

Apply Now

Program Benefits

  • Live Online academic masterclass by IIT Kanpur faculty
  • Program completion certificate from EICTA Consortium
  • 3 days campus immersion at IIT Kanpur
  • Exposure to PyTorch, Keras, and other prominent tools
  • Simplilearn's Job AssistPlus access for career support

GenAI & Machine Learning Course FAQs

  • What is the Professional Certificate Course in Generative AI and Machine Learning?

    This is an 11-month comprehensive online program designed to provide a deep understanding of artificial intelligence, machine learning, and generative AI. The course combines theoretical knowledge with applied learning through live classes, hands-on projects, and masterclass from IIT Kanpur faculty, preparing participants for advanced roles in the AI domain.

    • Core Objective: The course aims to provide in-depth coverage of machine learning, deep learning, Natural Language Processing (NLP), generative AI, prompt engineering, computer vision, and reinforcement learning.

    • Learning Format: It employs a live, online, and interactive format with virtual classroom sessions led by industry experts to create an immersive learning environment.

    • Certification: Upon successful completion, participants receive a program completion certificate from EICTA Consortium, issued by E&ICT Academy, IIT Kanpur, a prestigious credential that validates their skills.

  •  What does an AI professional do?

    While the term ‘AI professional’ is broad, they are known to be the ones designing, developing, and implementing AI models and algorithms with the intention of solving complex problems and improving overall performance. They are usually proficient in:

    • Machine learning

    • Deep learning

    • Natural language processing (NLP)

    • Building intelligent systems

    • Automation and decision-making modules

    Proficiencies aside, they also work with other teams to better integrate AI into the business, optimize processes, and ensure ethical practices are followed.

  • How effective are the machine learning course trainers at Simplilearn?

     

    The trainers at Simplilearn bring with them a strong mix of industry expertise and practical experience into each live session. They specialize in core machine learning concepts covering algorithms, model building and evaluation, unsupervised learning techniques and recommendation engines to name a few. They help learners build confidence by simplifying the learning material into explanations that are easy to digest focusing on the foundations.

    Apart from delivering content, they focus on keeping every session interactive and engaging and even make themselves available afterward to clarify doubts and support learners throughout their learning journey.

     

  • What are the benefits of enrolling in this GenAI and machine learning course?

    This gen AI and machine learning course will provide you with a strong foundation in advanced AI skills and techniques, focusing on concepts like machine learning training, deep learning, natural language processing, generative AI, and reinforcement learning, opening the doors to opportunities in AI development, data science, and automation. Some benefits of this course include:

    • A certificate of completion from EICTA Consortium, issued by E&ICT Academy, IIT Kanpur

    • Academic Masterclass by IIT Kanpur faculty

    • Completion certificate hosted on the Microsoft Learn portal for Microsoft Course

    • Applied learning through 18+ hands-on projects including 3 capstone projects

  • What certificate will I receive from this GenAI and machine learning course?

    Completing this machine learning course will earn you a program certificate from EICTA Consortium, issued by E&ICT Academy, IIT Kanpur. This industry-recognized certificate will validate your machine learning skills and set you up for further success in multiple industries. What's more, the program's collaboration with Microsoft also means you will receive a course completion certificate hosted on the Microsoft Learn portal for Microsoft Course.

  • Is this course really 100% online? Do I need to attend any classes in person?

    This Machine Learning Course is entirely online, so there's no need to be in a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or mobile.

  • What will be the career path after completing this GenAI and machine learning Course?

    Artificial intelligence and machine learning have been and will continue to be in high demand. Completing this course will give you all the necessary skills and open the doors to some top job roles, including:

    • Data scientist

    • Machine learning engineer

    • Computer vision engineer

    • Data architect

  • Will missing a live class affect my ability to complete the course?

    Missing a live class will not affect your ability to complete the course. You can use our 'Flexi-Learn' feature, which allows you to watch the recorded session of any class you miss, at your convenience. This allows you to stay on par with the machine learning course content and meet the requirements to progress and earn your certificate. You can do this via the Simplilearn learning platform by selecting the missed class and watching the recording to have your attendance marked.

  • Can I change my cohort after enrolling in the program?

    Yes. You are eligible for one complimentary cohort change within the first 60 days of your enrollment. If you cannot continue in your current cohort and, have already used your complimentary change, you may request an additional cohort transfer by paying the applicable fee. For details on the process and support with your request, please contact our support team.

  • Can I get an extension if I need more time to complete the program?

    Yes. If your program access has expired and you still have outstanding assignments or projects, you can request an extension by paying a nominal fee. Please note that while you will continue to have lifetime access to recorded sessions, certain features, such as marking attendance, submitting projects, and receiving your certificate, require active program validity. Requesting an extension will enable you to fulfill any remaining program requirements.

  • Acknowledgement
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, OPM3 and the PMI ATP seal are the registered marks of the Project Management Institute, Inc.
  • *All trademarks are the property of their respective owners and their inclusion does not imply endorsement or affiliation.
  • Career Impact Results vary based on experience and numerous factors.