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Certificate Program in Artificial Intelligence

Certificate Program in Artificial Intelligence

Application closes 4th Jun 2026

Why Artificial Intelligence?

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    AI is reshaping how businesses operate

    AI is driving structural changes across functions. Organizations are scaling AI to unlock value.

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    Build the most in-demand skill today

    AI isn’t replacing jobs; people who use AI are. Stay relevant and future-proof your career.

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PROGRAM OUTCOMES

Elevate your skills in Applied AI

Learn techniques to extract actionable insights and solve complex business challenges

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    Develop practical LLM workflows using prompt engineering, fine-tuning, RAG systems and AI agents.

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    Understand core AI concepts, identify problems, and associated ethical implications.

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    Understand NLP, GenAI, and experiment with models like Transformers & Stable Diffusion.

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    Construct and train neural networks and understand architectural differences.

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    Implement and evaluate supervised learning algorithms and anomaly detection techniques.

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    Apply key Python libraries to prepare data for analysis and modeling.

Earn a Certificate of Completion from Johns Hopkins University

  • #7 National University Rankings

    #7 National University Rankings

    U.S. News & World Report, 2026

  • #2 Computer Information Technology

    #2 Computer Information Technology

    U.S. News & World Report, 2026

  • #14 Best Global University

    #14 Best Global University

    U.S. News & World Report, 2026

  • #1 Biomedical Engineering Program

    #1 Biomedical Engineering Program

    US News and World Report, 2026

KEY PROGRAM HIGHLIGHTS

Why choose the Applied AI program

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    Learn from a top-ranked university

    Learn from expert JHU faculty and industry leaders.

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    Industry-aligned curriculum

    Learn the foundations of Python, GenAI, and Deep Learning, gain valuable insights, and apply your skills to transition into AI roles.

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    Hands-on learning

    Drive business value by building practical AI solutions through hands-on projects and real-world case studies.

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    Live masterclasses and mentorship

    Experience live mentorship by industry experts and live faculty-led masterclasses for structured, personalized learning.

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    AI-assisted learning

    AI-powered tools support learning content and practice activities to enhance learning efficiency and engagement.

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    Dedicated support

    Access a dedicated Program Manager and Academic Learning Support, which includes discussion forums and peer groups.

Skills you will learn

GENERATIVE AI

AGENTIC AI WORKFLOWS

RETRIEVAL-AUGMENTED

GENERATION PROMPT ENGINEERING

MACHINE LEARNING

STATISTICAL ANALYSIS

GENERATIVE AI

AGENTIC AI WORKFLOWS

RETRIEVAL-AUGMENTED

GENERATION PROMPT ENGINEERING

MACHINE LEARNING

STATISTICAL ANALYSIS

Advance your career with AI

  • AI top priority

    For 75% companies

  • GenAI Job Surge

    411% rise in roles

  • AI Skills Pay

    56% premium wages

  • Up to $ 150K

    avg annual salary

Careers in Artificial Intelligence:

Roles you can unlock with Artificial Intelligence skills

  • AI Specialist

  • AI Analyst

  • Machine Learning Engineer

  • AI Solutions Associate

  • NLP Engineer

  • AI Product Manager

  • AI Strategy Consultant

  • Prompt Engineer

Our alumni work at top companies

  • Overview
  • Learning Journey
  • Curriculum
  • Projects
  • Tools
  • Certificate
  • Faculty
  • Mentors
  • Reviews
  • Career Support
  • Fees
  • FAQ
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This program is ideal for

Working professionals at various stages of their careers looking to advance their knowledge and skills in AI.

  • STEM graduates

    Looking to secure their first role in the data and AI industry, with a focus on building a strong foundation in AI.

  • Early to mid-career

    Seeking to transition into AI roles by acquiring the skills necessary to navigate and excel in this fast-evolving field.

  • Senior professionals

    Looking to upskill in AI and deepen their understanding of advanced AI concepts and applications relevant to their industry.

  • Tech leaders

    Who want to leverage AI for strategic planning, innovation, and data-driven business decision-making

Experience a unique learning journey

Our pedagogy is designed to ensure a holistic learning experience

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    Learn from world-renowned faculty

    Learn critical concepts through live masterclasses and recorded video lectures by JHU faculty

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    Engage with your mentors

    Clarify your doubts and gain practical skills during weekly live sessions with industry experts

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    Work on hands-on projects

    Work on projects to apply the concepts & tools learnt in the module

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    Get personalized assistance

    Our dedicated program managers will support you through your learning journey

Elevate Your Skills with an Optional Paid Program

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Certificate Program in AI and Agentic AI Engineering

Build autonomous, goal-driven AI agents capable of perceiving, reasoning, and acting independently.

  • Learn from world-renowned JHU faculty and experts
  • Deploy and manage AI systems with MLOps and LLMOps
  • Scale and lead AI initiatives with confidence
  • Earn Continuing Education units upon program completion

Reach out to program advisor for more details.

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PROGRAM CURRICULUM

The curriculum is designed by the faculty of Johns Hopkins University and leading industry practitioners. It is taught by best-in-class professors, practicing industry experts, and professionals from leading global companies.

  • Self-paced

    Modules

  • Live

    Faculty-led masterclasses

  • Live

    Mentorship sessions

Pre-Work

This module will help learners navigate the AI landscape by identifying key problems and solution spaces, enabling businesses to leverage AI technologies effectively, acquire fundamental programming skills in Python to build the necessary foundation to develop AI solutions to support business initiatives, and apply foundational concepts of probability and statistics as a foundation to analyze data and inform strategic decision-making.

AI Landscape - Problems and Solution Spaces

This section focuses on building a foundational understanding of AI and its subfields, empowering participants to distinguish between AI, ML, DL, and Generative AI; trace the historical evolution of the field; recognize real-world problems that AI can effectively address; and critically evaluate the ethical and societal implications of AI applications.

Introduction to Python

This section focuses on establishing essential programming foundations by setting up a Python development environment using tools like Google Colaboratory and VS Code, and gaining proficiency in core Python constructs such as data types and data structures to support AI development.

Fundamentals of Probability and Statistics

This week focuses on developing a strong grasp of probability and statistics concepts by interpreting and applying descriptive statistics and probability distributions within AI contexts, enabling participants to make informed decisions under uncertainty.

Course 01: Python for Data-driven Insights

This module focuses on building a strong foundation in data analytics and applying the ROAD framework to identify and solve key business challenges using data-driven approaches. Learners will also utilize Python for data transformation and feature engineering to prepare datasets, and conduct exploratory analysis and visualization to uncover trends and patterns in data, enabling informed business decisions and strategic planning.

Week 01 | Introduction to Data Analytics

This week focuses on developing strong requirement formulation skills critical to AI projects, enabling participants to identify key components of well-constructed requirements using the ROAD framework, evaluate them for clarity and feasibility, and craft specific, manageable, and relevant problem statements.

Week 02 | Python Fundamentals for AI

This week provides a foundation in essential Python programming constructs like conditional statements and functions, and libraries for AI, enabling participants to gain hands-on experience with NumPy and Pandas for efficient data manipulation and analysis.

Week 03 | Python for Data Transformation and Feature Engineering

This week focuses on data preparation for AI workflows, enabling participants to load, clean, and preprocess data, apply feature engineering techniques, and effectively handle missing data and outliers to get data ready for analysis and modeling.

Week 04: Exploratory Analysis and Visualization

This week emphasizes exploratory data analysis, enabling participants to uncover patterns, trends, and anomalies in data, create informative visualizations, and communicate insights through clear and impactful visual representations.

Week 05 | Introduction to Statistical Analysis

This week introduces foundational statistical concepts for AI, enabling participants to utilize inferential statistics to draw meaningful conclusions from sample data via confidence intervals and hypothesis testing, aiding businesses in making informed decisions and supporting strategic planning and risk assessment.

Week 06: Learning Break

Learning breaks are structured pauses that allow you to consolidate concepts, complete pending work, and reinforce your understanding before progressing further.

Week 07 : Project 01

Work on an industry-relevant project using in-demand tools and techniques, guided by expert mentors.

Course 02: Applied Machine Learning for Predictive Analytics

This module focuses on building a strong foundation in core machine learning techniques by exploring supervised and unsupervised learning approaches and their real-world applications. It enables participants to model, classify, cluster, and detect patterns in data using algorithms such as linear regression, decision trees, K-means clustering, and anomaly detection techniques. Learners will also explore techniques for tuning and evaluating these models for optimizing predictive performance.

Week 08 | Regression Techniques

This week provides a comprehensive introduction to supervised learning, enabling participants to model data using linear and polynomial regression, evaluate performance with key metrics, and apply regularization techniques to prevent overfitting.

Week 09 | Classification Methods

This week introduces key classification algorithms like decision trees and random forests, and enables participants to tune the models and evaluate the performance of these models using different metrics.

Week 10 | Clustering Algorithms

This week introduces the principles of unsupervised learning and enables participants to apply clustering algorithms like KMeans, Hierarchical Clustering, and DBSCAN, along with evaluating clustering outcomes using suitable metrics and visualizations.

Week 11 | Anomaly Detection

This week introduces techniques for anomaly detection and enables participants to apply algorithms like Isolation Forest and One-Class SVM, while exploring real-world applications such as fraud detection and system health monitoring.

Week 12: Project 02

Work on an industry-relevant project using in-demand tools and techniques, guided by expert mentors.

Week 13: Learning Break

Learning breaks are structured pauses that allow you to consolidate concepts, complete pending work, and reinforce your understanding before progressing further.

Course 03: Neural Networks and Computer Vision

This module builds a strong foundation in deep learning and modern AI by exploring neural network models. Learners will explore the fundamental concepts of neural networks and their application to classification tasks, examine the unique architecture of CNNs and its applications such as object detection and facial recognition, understand the principles behind diffusion models, and how to generate high-quality images from textual descriptions.

Week 14 | Neural Networks for Classification

This week provides a comprehensive introduction to neural networks, enabling participants to understand the role of perceptrons and activation functions in deep learning, and build, train, and apply artificial neural networks to real-world problems.

Week 15 | CNNs for Vision Applications

This week focuses on Convolutional Neural Networks (CNNs) for image analysis, enabling participants to build image classification models, implement advanced architectures like ResNet, VGG, and Inception, develop a facial expression recognition system, and gain an introductory understanding of object detection techniques such as YOLO and Faster R-CNN.

Week 16 | Image Generation with Stable Diffusion

This week focuses on exploring the power of AI to create and transform images using diffusion models. Participants will learn how to generate stunning visuals from text, experiment with creative editing techniques like inpainting and image translation, and discover how to guide image creation using outlines, depth, or poses, all through hands-on tools and intuitive workflows.

Week 17: Project 03

Work on an industry-relevant project using in-demand tools and techniques, guided by expert mentors.

Week 18: Learning Break

Learning breaks are structured pauses that allow you to consolidate concepts, complete pending work, and reinforce your understanding before progressing further.

Course 04: Generative AI for NLP

This week introduces core concepts in Natural Language Processing (NLP) and Generative AI, enabling participants to understand tokenization, embeddings, and large language model architectures.

Week 19 | NLP and Generative AI for Text Analytics

This week introduces core concepts in Natural Language Processing (NLP) and Generative AI, enabling participants to understand tokenization, embeddings, and large language model architectures.

Week 20 | Retrieval-Augmented Generation for Contextualized Q&A

This week explores Retrieval-Augmented Generation (RAG), enabling participants to understand its architecture, implement RAG to enhance language model performance and relevance, and apply evaluation techniques to align Large Language Models (LLMs) with reliable sources.

Week 21 | Building Agentic AI workflows

This week focuses on practical applications of Large Language Models (LLMs), enabling participants to understand key considerations when working with LLMs, apply fine-tuning and prompt engineering for specific tasks, and integrate LLMs into real-world workflows and solutions.

Week 22: Project 04

Work on an industry-relevant project using in-demand tools and techniques, guided by expert mentors.

Sample Case Studies

Apply your learning through real-world case studies guided by global industry experts. Please note: All case studies and projects outlined are indicative and subject to change.

Retail Chatbot Development

E-COMMERCE Learn how to create an AI-powered chatbot using Prompt Engineering and Large Language Models (LLMs) to improve customer service and help with order tracking in e-commerce. Skills you will learn: Large Language Models, Prompt Engineering, AI Agents, Context-Aware Chatbots, Order Tracking Automation, Python

Online Purchase Behavior Analysis

MARKETING Explore how to use statistical methods to identify the factors that influence online purchase decisions, helping to improve marketing campaigns and boost conversions. Skills you will learn: Statistical Analysis, Statistical Testing, Data-Driven Decision Making, Python

Cardiovascular Risk Prediction

HEALTHCARE Learn how to build a classification model using patient health data to assess the risk of heart disease and help improve preventive care. Skills You will learn: Classification Modeling, Health Data Analysis, Risk Prediction, Preventive Care

AI-Based Visual Content Creation

Explore how to build an AI solution that generates personalized images from text to support creative campaigns for brands and NGOs. Skills you will learn: Generative AI, Visual Content Generation, Prompt Engineering, Image Generation, Stable Diffusion, Python

Work on hands-on projects and case studies

Gain practical, hands-on skills and leverage AI to solve business challenges.

  • Hands-on

    projects

  • Real-world

    case studies

  • In-demand

    tools and techniques

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RETAIL, E-COMMERCE

Retail Order Query Chatbot

Description

Develop an AI-powered ‘secretary’ that assists users in managing emails more efficiently by highlighting the most urgent messages, summarizing email threads, and improving overall productivity. The project aims to leverage Generative AI models for classifying, prioritizing, and summarizing emails to provide concise, actionable insights for users.

Skills you will learn

  • Large Language Models
  • Prompt Engineering
  • AI Agents
  • Context-Aware Chatbots
  • Order Tracking Automation
  • Python
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E-COMMERCE, DIGITAL MARKETING

Measuring What Drives Online Purchase Decisions

Description

Develop a Generative AI system that automates the drafting of NIH research proposals by aligning ideas with Notices of Funding Opportunities (NOFOs) and generating high-quality, structured content.

Skills you will learn

  • Statistical Analysis
  • Statistical Testing
  • Data-Driven Decision Making
  • Python
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HEALTHCARE, MEDICAL ANALYTICS

Predicting Cardiovascular Disease Risk

Description

Develop an AI-driven predictive model to identify high-risk loan applicants with greater accuracy. By analyzing applicant data, credit scores, and loan types, the model reduces subjectivity in loan approvals and flags potential defaults before they occur.

Skills you will learn

  • Decision Trees
  • Ensemble Learning
  • Random Forests
  • Model Tuning
  • Model Evaluation
  • Classification
  • Python
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MARKETING, CREATIVE TECHNOLOGY

AI-Powered Visual Content Generation System

Description

Explore how to build an AI solution that generates personalized images from text to support creative campaigns for brands and NGOs.

Skills you will learn

  • Generative AI
  • Visual Content Generation
  • Prompt Engineering
  • Image Generation
  • Stable Diffusion
  • Python
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RESEARCH, ACADEMIA

LLM-Powered Research Assistant

Description

Learn how to create a smart assistant that summarizes research papers, extracts key information, and highlights trends to make academic work easier.

Skills you will learn

  • Natural Language Processing
  • Agentic AI
  • Large Language Models
  • Prompt Engineering
  • Text Summarization
  • Metadata Extraction
  • Python
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AVIATION ANALYTICS

Flight Delay Analysis

Description

Explore how to study aviation data to find patterns that help improve flight scheduling and operational efficiency.

Skills you will learn

  • Python
  • Numpy
  • Pandas
  • Seaborn
  • Univariate Analysis
  • Bivariate Analysis
  • Exploratory Data Analysis
  • Statistical Analysis
  • Business Recommendations
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HEALTHCARE

Intelligent Healthcare System

Description

Understand how to use EHR data and machine learning to support early intervention and deliver personalized care.

Skills you will learn

  • Python
  • Numpy
  • Pandas
  • Seaborn
  • Exploratory Data Analysis
  • Supervised Learning
  • Anomaly Detection
  • Scikit-Learn
  • Business Recommendations
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E-COMMERCE ANALYTICS

Intelligent Fashion Tagging System

Description

Learn how to create a deep learning model that automatically tags clothing items based on their visual features to improve catalog management.

Skills you will learn

  • Python
  • Numpy
  • Exploratory Data Analysis
  • Neural Networks
  • Convolutional Neural Networks
  • TensorFlow
  • Object Detection
  • YOLO
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Travel Planning Agentic AI

Build an AI-Powered Agentic System

Description

Understand how to design a personalized trip planner using large language models and agentic AI to recommend destinations and create itineraries based on user preferences.

Skills you will learn

  • Python
  • HuggingFace
  • Sentence Transformers
  • Large Language Models
  • Prompt Engineering
  • LangChain
  • OpenAI
  • LangGraph
  • Agentic AI

Master in-demand AI & ML tools

Gain hands-on experience with 20+ tools and techniques

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    Python

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    Pandas

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    NumPy

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    Scikit-Learn

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    Hugging Face

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    Llama Cpp

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    ChatGPT

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    ChatGPT API

Earn a certificate of completion from Johns Hopkins University

Get CEUs and a globally recognized credential from a top U.S. university and showcase it to your network.

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* Image for illustration only. Certificate subject to change.

Meet your faculty

Learn from renowned faculty with expertise across AI, machine learning, and applied analytics.

  • Dr. Ian McCulloh  - Faculty Director

    Dr. Ian McCulloh

    Director of AI Executive & Professional Education, Johns Hopkins University

    Served as Chief Data Scientist and MD, AI, Accenture Federal Services

    Author of three books and over 100 peer-reviewed papers

    Know More
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  • Dr. Anthony (Tony) Johnson   - Faculty Director

    Dr. Anthony (Tony) Johnson

    Senior Professional Staff Member and Research Scientist, Applied Physics Laboratory, Whiting School of Engineering, Johns Hopkins University

    Former AI researcher at HEC Montréal specializing in deep learning

    Expert in NLP, computer vision, and meta-learning for real-world AI tasks

    Know More
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Interact with our mentors

Interact with dedicated and experienced AI experts who will guide you through your learning journey

  •  Prabhat Bhattarai - Mentor

    Prabhat Bhattarai linkin icon

    Data Scientist Apple
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  •  Priyanka Singhal  - Mentor

    Priyanka Singhal linkin icon

    Assistant Vice President (AI Research), US Bank
    Company Logo
  •  Vinicio Desola Jr  - Mentor

    Vinicio Desola Jr

    Senior AI Engineer Newmark
    Newmark Logo
  •  Marcelo Guarido de Andrade - Mentor

    Marcelo Guarido de Andrade linkin icon

    Research Assistant at University of Calgary University of Calgary
    University of Calgary Logo
  •  Omid Badretale - Mentor

    Omid Badretale linkin icon

    Senior Research Data Scientist | Alternative Data RBC Capital Markets
    RBC Capital Markets Logo
  •  Pankaj Kumar  - Mentor

    Pankaj Kumar linkin icon

    Data Science Manager Republic Finance
    Republic Finance Logo
  •  Sundeep Pothula  - Mentor

    Sundeep Pothula linkin icon

    Product Data Scientist Moveworks
    Moveworks Logo

Get your dream job with dedicated career support

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    1:1 career sessions

    Interact personally with industry professionals through career mentoring sessions to get valuable insights and guidance

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    Interview preparation

    Gain insights into what recruiters look for. Access career prep material and practice with AI-powered mock interviews.

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    Resume & profile review

    Polish your resume using an AI-assisted builder to better showcase your skills and experience.

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    E-portfolio

    Build an industry-ready portfolio to showcase your mastery of skills and tools

Course Fees

The course fee is USD 3,700

Advance your career

  • benifits-icon

    Understand core AI concepts, identify problems, and associated ethical implications

  • benifits-icon

    Understand NLP, GenAI, and experiment with models like Transformers & Stable Diffusion

  • benifits-icon

    Implement and evaluate RAG systems to enhance LLM performance through context

  • benifits-icon

    Develop practical LLM workflows using prompt engineering, fine-tuning, and AI agents

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Easy payment plans

Avail our EMI options & get financial assistance

  • INSTALLMENT PLANS

    Upto 12 months Installment plans

    Explore our flexible payment plans

    View Plans

Third Party Credit Facilitators

Check out different payment options with third party credit facility providers

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*Subject to third party credit facility provider approval based on applicable regions & eligibility

Take the next step

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Apply to the program now or schedule a call with a program advisor

Unlock exclusive course sneak peek

Application Closes: 4th Jun 2026

Application Closes: 4th Jun 2026

Talk to our advisor for offers & course details

Registration process

Our registrations close once the requisite number of participants enroll for the upcoming batch. Apply early to secure your seat.

  • steps icon

    1. Fill application form

    Register by completing the online application form.

  • steps icon

    2. Application screening

    A panel from Great Learning will review your application to determine your fit for the program.

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    3. Join program

    After a final review, you will receive an offer for a seat in the upcoming cohort of the program.

Batch start date

  • Online · 18th Jul 2026

    Admission closing soon

Frequently asked questions

Program Details
Admissions & Eligibility
Fee and Payment
Others
Program Details

What is the Certificate Program in Artificial Intelligence: Applied ML, GenAI, and Agents?

The Certificate Program in Artificial Intelligence: Applied ML, GenAI, and Agents is a five-month online program from Johns Hopkins University’s Whiting School of Engineering. It is designed for professionals and graduates who want to build practical, job-relevant skills in applied artificial intelligence and move beyond theory to real-world implementation. 


The program focuses on applying AI techniques to solve business and operational problems. Learners work with real datasets, industry case studies, and hands-on projects while attending live mentorship sessions with industry experts and faculty-led masterclasses from Johns Hopkins University.

What will I learn in this Artificial Intelligence program?

In this program, you will learn to: 


  • Understand core AI concepts, including Machine Learning, deep learning, and Generative AI, identify industry-relevant, solvable problems, and consider associated ethical implications. 

  • Apply key Python libraries (NumPy, Pandas) for data loading, cleaning, preprocessing, and feature engineering to prepare data for analysis and modeling. 

  • Analyze data using statistical methods and create visualizations to effectively communicate business insights across diverse industry scenarios. 

  • Implement and evaluate supervised learning algorithms (regression, classification), unsupervised learning (clustering), and anomaly detection techniques. 

  • Construct and train neural networks for classification tasks, including Convolutional Neural Networks (CNNs) for image classification and object detection, with a clear understanding of architectural differences. 

  • Comprehend fundamentals of Natural Language Processing (NLP) and Generative AI, including hands-on experimentation with models such as Transformers and Stable Diffusion. 

  • Implement and analyze Retrieval Augmented Generation (RAG) systems to improve language model performance through enhanced contextualization. 

  • Develop practical Large Language Model (LLM) workflows using Prompt Engineering, fine-tuning, and AI agents for seamless real-world integration.

What are the key features of the program?

The Certificate Program in Artificial Intelligence: Applied ML, GenAI, and Agents offers the following key features: 


  • Five-month program in an online learning format 
  • Live masterclasses led by Johns Hopkins University faculty 
  • Live mentorship sessions with global industry experts 
  • Recorded video lectures and self-paced learning modules 
  • Hands-on projects and real-world case studies across industries 
  • Dedicated support from a Program Manager 
  • Academic support through the Great Learning community, peer groups, and discussion forums 
  • A Certificate of Completion with 16 Continuing Education Units (CEUs) from Johns Hopkins University

What is the format and structure of this applied AI course?

The program is delivered fully online through a structured five-month format that includes: 


  • Recorded, self-paced video lectures 
  • Live mentorship sessions with industry experts 
  • Faculty-led masterclasses from Johns Hopkins University 
  • Hands-on projects and real-world case studies 
  • Self-paced reinforcement modules

Will I receive learning support during the program?

Yes. Learners receive continuous support throughout the Certificate Program in Artificial Intelligence: Applied ML, GenAI, and Agents. A dedicated Program Manager acts as a single point of contact, helping with scheduling, progress tracking, and access to resources. 


Learners also benefit from academic support through the Great Learning community, project discussion forums, peer groups, live mentorship sessions, and faculty-led masterclasses designed to reinforce key concepts.

What is the duration of the Certificate Program in Artificial Intelligence: Applied ML, GenAI, and Agents?

The program is five months long.

What is the weekly time commitment?

Learners are expected to dedicate approximately 8–10 hours per week to complete learning modules, attend live sessions, and work on hands-on projects.

Who are the faculty and mentors for JHU’s Artificial Intelligence course?

The program is designed and delivered by faculty from Johns Hopkins University, with deep expertise in applied AI, machine learning, neuroscience, and analytics. 

Core faculty include: 

  • Dr. Anthony (Tony) Johnson, Senior Professional Staff Member and Research Scientist, Johns Hopkins University 
  • Dr. Ian McCulloh, Manager of Artificial Intelligence Continuing and Executive Education, Johns Hopkins University 


Learners also receive guidance from industry mentors who are experienced AI practitioners from leading global organizations. 

Note: Faculty and mentor lists are subject to change based on availability.

How do I earn the AI course certificate?

Upon successful completion of the program, you will receive a Certificate of Completion from Johns Hopkins University, along with 16 Continuing Education Units (CEUs). 


The certificate demonstrates practical proficiency in applied AI, machine learning, deep learning, and Generative AI. It adds value to your professional profile by showcasing hands-on experience and real-world application of AI skills.

What sets JHU’s AI program apart from other AI certificate courses?

This program stands out due to: 

  • Strong focus on applied, real-world AI use cases rather than theory alone 
  • Live faculty masterclasses combined with industry mentorship 
  • Hands-on projects across multiple domains, including healthcare, retail, aviation, and marketing 
  • Coverage of modern AI techniques, including Generative AI, RAG, and Agentic AI workflows 
  • Personalized learner support throughout the program

What tools and technologies will I use in the program?

Learners work with industry-relevant tools and technologies, including: 


  • Python and key data science libraries 
  • Machine learning and deep learning frameworks 
  • Large Language Models (LLMs) 
  • Prompt Engineering and RAG frameworks 
  • Computer vision and Generative AI models 


These tools are applied through hands-on projects and case studies.

Admissions & Eligibility

Who is this program for?

This program is designed for: 


  • Professionals seeking to transition into AI roles by acquiring the skills and knowledge necessary to navigate and excel in this fast-evolving field. 
  • Professionals looking to upskill in AI to stay ahead in their careers by deepening their understanding of advanced AI concepts and applications relevant to their industry. 
  • STEM (Science, Technology, Engineering, and Mathematics) graduates looking to secure their first role in the data and AI industry, with a focus on building a strong foundation of technical skills and practical experience. 


*The program enables non-technical professionals to transition into a career in AI and Machine Learning without any prior coding experience.

What is the admission process?

The admission process is conducted on a rolling basis and will be closed once the requisite number of candidates are enrolled. 


1. Fill the application form Register by completing the online application form. 

2. Application screening A panel from Great Learning will review your application to determine your fit for the program. 

3. Join the program An offer letter will be sent to the selected candidates.

Fee and Payment

What is the program fee?

The total program fee for the Certificate Program in Artificial Intelligence: Applied ML, GenAI, and Agents is $3,700. Flexible payment options may be available. Please contact a Program Advisor for details on payment plans and financial assistance.

Others

What are CEUs, and how are they useful?

Upon successful completion of the program, you will earn 16 Continuing Education Units (CEUs) along with a certificate of completion from Johns Hopkins University. CEUs are a measure of instructional time and reflect the estimated time an average student will spend completing the mandatory components of a course. CEUs are awarded upon successfully completing all course requirements, which may include assessments, assignments, evaluations, or a final project. Earning CEUs allows professionals to demonstrate their commitment to continuous learning and skill advancement.

What career support will be available with this Artificial Intelligence course?

The program offers comprehensive career resources and guidance designed to help translate program learning into professional advancement. 


  • Career Preparation Sessions: Includes two sessions focused on exploring ways to apply skills acquired from the program for professional advancement, with strategies for leveraging competencies developed through the program. 
  • Resume Builder: Access to a resume builder to create professional, impactful resumes aligned with targeted roles. 
  • Mock Interviews: Unlimited mock interviews powered by AI to practice and refine interview skills. 
  • Career Preparation Materials: Access to curated interview questions and preparation materials to support readiness for job opportunities.

How can I contact the program team for more information?

For any queries or assistance, you can reach out to the program team at office-apai-gl@jhu.edu or call +1 410 883 8765.

Delivered in Collaboration with:

Johns Hopkins University is collaborating with online education provider Great Learning to offer the Certificate Program in Artificial Intelligence: Applied ML, GenAI, and Agents. Great Learning is a professional learning company with a global footprint in 14+ countries. Its mission is to make professionals around the globe proficient and future-ready. This program leverages JHU's leadership in innovation, science, engineering, and technical disciplines developed over years of research, teaching, and practice. Great Learning manages the enrollments and provides industry experts, student counselors, course support, and guidance to ensure students get hands-on training and live personalized mentorship on the application of concepts taught by the JHU faculty.

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