Professional Certificate in Artificial Intelligence (AI) and Machine Learning (In collaboration with Michigan Engineering Professional Education & IBM

Professional Certificate in Artificial Intelligence (AI) and Machine Learning

In collaboration with Michigan Engineering Professional Education, University of Michigan & IBM

• Receive a program completion certificate from Michigan Eng Pro-Ed
• Build high-demand capabilities such as AI automation, AI literacy, and more
• 6 Months length program (6-8 h/week weekend classes)
• Ask us for the next cohort and schedule details!

Boost your career with this comprehensive AI and machine learning program, combining theory, hands-on projects, and practical exercises under the academic excellence of Michigan Engineering Professional Education. You’ll master AI and ML fundamentals and learn to apply leading tools and techniques to solve real-world business problems.

The program features live virtual classes, interactive projects, labs, and masterclasses for an engaging learning experience. The curriculum covers key areas including mathematics and statistics, Python programming, machine learning, deep learning, agentic and generative AI, prompt engineering, explainable AI, ChatGPT, natural language processing, and more.

Key Features

• Course and material are in English
• Intermediate - Advanced level
• 6 months program (5-6 hours/week weekend classes)
• 200+ hours of live online instruction delivered by industry experts
• 350+ hours recommended study time
• 1 year access to learning platform & class recordings
• Gain practical experience through 12+ hands-on projects, including three capstones spanning multiple industries.
• Get hands-on exposure to 15+ tools, including ChatGPT, Claude, Descript, Julius.ai, Zapier, and Python.
• Attend masterclasses by industry thought leaders and IBM experts
• Industry-recognized IBM course certificates
• Program completion certificate issued by Michigan Engineering Professional Education.
• Secure Michigan Eng Pro-Ed digital badge

14+ Skills Covered

• Generative AI
• Agentic AI
• Prompt Engineering
• Conversational AI
• Large Language Models LLMs
• ML Model Training and Optimization
• ML Model Evaluation and Validation
• Ensemble Learning
• Deep Learning
• Natural Language Processing NLP
• Speech Recognition
• Statistics
• Machine Learning Algorithms
• Supervised and Unsupervised Learning

Engaging Learning Experience

• Peer Interaction
• Enjoy a true classroom-like environment by connecting with fellow learners and engaging with mentors in real time through Slack.
• Flexible Learning
• Never fall behind—access recorded sessions anytime to catch up and stay aligned with your cohort.
• Mentorship Sessions
• Receive expert support from mentors to resolve doubts, get project guidance, and enhance your learning journey.
• Dedicated Support
• Benefit from a Cohort Manager who provides personalized assistance and ensures you stay on track toward success.

About University of Michigan

The University of Michigan is a leading public research university in the United States, globally recognised for academic excellence, innovation, and leadership in science and engineering. Michigan Engineering Professional Education, part of the university’s College of Engineering, delivers industry-focused programs led by expert U.S.-based faculty, helping professionals and leaders apply cutting-edge research and engineering practices to real-world business and technology challenges.

What added value does University of Michigan contribute to the program?

The program curriculum is reviewed and approved by Michigan Engineering Professional Education, Please be aware that the live classes are not held by actual University faculty staff but by many experienced Industry experts. While the instructors themselves are not employees of the University of Michigan. Beyond content approval, Michigan Engineering Professional Education also oversees instructor evaluation, quality assurance, learner satisfaction, and overall program outcomes which gives the program quality legitimacy and a co-branded certificate of completion.

Learning Objective

• Learn to write efficient Python code and leverage its powerful libraries for data analysis and AI projects
• Build skills in data wrangling, exploration, visualization, and hypothesis testing to support informed, data-driven decisions
• Understand and apply a range of machine learning algorithms, including concepts like regularization, overfitting, and model tuning
• Gain expertise in designing and training neural networks, including convolutional and recurrent models, for complex data tasks
• Utilize the SciPy library and its modules—such as Integrate, Optimize, Stats, IO, and Weave—for scientific and technical computing
• Develop the ability to build conversational chatbots and AI-driven customer service agents
• Implement text-to-speech functionality and automated speech recognition technologies
• Learn about LLMs like ChatGPT, Gemini and their applications in different fields.

Target Audience:

This program is tailored for mid to senior-level IT professionals who want to elevate their careers or move into roles focused on AI, machine learning, and generative AI. Whether you're looking to deepen your technical skills, pivot to AI-centric projects, or step into leadership positions in data science and AI, the program offers comprehensive knowledge, practical experience, and a respected certification to help you stay competitive in the evolving tech industry.

• Mid-level IT professionals
• – Individuals with several years of experience in tech looking to upskill.
• Senior IT professionals or tech leads
• – Those aiming to transition into leadership roles in AI or data-driven departments.
• Professionals seeking a career shift into AI/ML/GenAI
• – Those from adjacent tech fields (e.g., software development, data engineering, analytics) who want to move into AI-focused roles.
• Individuals aiming to lead AI-driven projects
• – Project managers, architects, or product owners looking to lead initiatives involving AI technologies.
• Aspiring AI/Data Science leaders
• – Professionals preparing for strategic or managerial roles in AI and data science teams.

Prerequisites:

• Preferably 2+ years of full-time work experience.
• A foundational understanding of programming and mathematics is beneficial.
• Be at least 18 years old and have a High School Diploma or equivalent

Learning Path

1. Python Refresher with AI
2. Applied Data Science With Python
3. Machine Learning
4. Deep Learning Specialization
5. Generative AI Literacy
6. Generative AI Essentials by IBM
7. Foundation Models and Generative AI Platforms by IBM
8. Capstone Project

Electives

1. Prompt Engineering Essentials by IBM
2. NLP and Speech Recognition
3. Advanced Generative AI
4. Masterclasses by IBM
5. Agentic AI Masterclass

COURSE CONTENT DETAILS

COURSE 1: Python Refresher with AI

Python is the core programming language behind AI development, making this course an essential starting point for AIML. By mastering Python fundamentals, you’ll build a solid technical foundation to progress into AI model training, deployment, and governance.

Learning Outcomes

• Write and execute Python programs to solve real-world problems
• Use data types, operators, and control structures to build efficient code
• Apply functions, loops, and error handling for structured programming
• Manage files effectively for AI and machine learning applications
• Build foundational coding skills for AI model development

Topics Covered

• Introduction to Python Programming
• Python Data Types and Operators
• Conditional Statements and Loops
• Error and File Handling
• Python Functions

COURSE 2: Applied Data Science with Python

This course introduces core data science concepts such as data preparation, model building, and evaluation. You’ll strengthen your Python skills with topics like strings, lists, and lambda functions, while working with NumPy, linear algebra, and key statistical concepts including central tendency, dispersion, skewness, covariance, and correlation. The curriculum also covers hypothesis testing techniques such as Z-tests, T-tests, and ANOVA, along with data manipulation using pandas and data visualization with Matplotlib.

Learning Outcomes

• Build a strong understanding of the end-to-end data science process
• Develop proficiency in Python and key libraries for data science applications
• Learn to use NumPy and pandas for effective data manipulation and analysis
• Create clear, engaging visualizations with Matplotlib, Seaborn, Plotly, and Bokeh
• Gain practical skills in data wrangling and data preprocessing techniques

Topics Covered

• Introduction to data science concepts
• Core Python programming essentials
• Fundamentals of NumPy
• Linear algebra principles
• Statistical foundations
• Probability distributions
• Advanced statistical methods
• Working with pandas for data handling
• Data analysis, wrangling, and visualization techniques
• Applying statistics end to end using Python

COURSE 3: Machine Learning

This module explores the various types of machine learning and their real-world applications, covering the full ML pipeline with an emphasis on supervised learning through regression and classification models. It also introduces unsupervised learning, clustering methods, and ensemble techniques, while comparing frameworks such as TensorFlow and Keras and providing hands-on experience with PyTorch to build a recommendation system.

Learning Outcomes

• Analyze different types of machine learning and their key characteristics
• Explore supervised learning and its use in real-world applications
• Understand overfitting and underfitting, including how to identify and prevent them
• Learn various regression models and their practical use cases
• Examine ensemble techniques such as bagging, boosting, and stacking

Topics Covered

• Core machine learning fundamentals
• Supervised learning techniques
• Regression models and their applications
• Classification models and their use cases
• Unsupervised learning methods
• Ensemble learning approaches
• Recommendation systems

COURSE 4: Deep Learning Specialization

This course covers the core concepts and practical applications of deep learning, emphasizing how it differs from traditional machine learning. Topics include neural networks, forward and backward propagation, TensorFlow 2 and Keras, performance optimization, model interpretability, convolutional neural networks (CNNs), transfer learning, object detection, recurrent neural networks (RNNs), autoencoders, and PyTorch.

Learning Outcomes

• Understand the key differences between deep learning and machine learning
• Learn how deep learning is applied across real-world use cases
• Explore forward and backward propagation in deep neural networks (DNNs)
• Understand hyperparameter tuning and model interpretability techniques
• Apply dropout and early stopping to enhance model performance
• Build expertise in convolutional neural networks (CNNs) for tasks such as object detection

Topics Covered

• Neural networks and deep neural networks (DNNs)
• Forward and backward propagation in DNNs
• TensorFlow 2 and Keras frameworks
• Hyperparameter tuning techniques
• Performance optimization methods
• Convolutional neural networks (CNNs) and object detection
• Transfer learning using pre-trained models
• Recurrent neural networks (RNNs) and autoencoders
• PyTorch for deep learning applications
• Model interpretability and explainability

COURSE 5: Generative AI Literacy

After exploring various deep learning specializations, we explore machine learning types and learn to develop generative AI algorithms, including chatbots, LLMs, and AI-driven image/video generation.

Learning Outcomes

• Learn various types of machine learning
• Learn about chatbots and large language models
• Understand generative AI algorithms
• Explore multiple tools available for AI tasks

Topics Covered

• Transformer Algorithms
• ChatGPT Models
• Neural Networks
• GANs and VAE
• Large Language Models (LLMs)
• Image Generation and Video Generation

COURSE 6: Generative AI Essentials by IBM

After developing generative AI literacy, we explore the fundamentals, evolution, and applications of GenAI across text, image, audio, video, virtual worlds, code, and data. Learn about popular models and tools like GPT, DALL-E, Stable Diffusion, and Synthesia and their impact across industries.

Learning Outcomes

• Demonstrate use cases of generative AI for text, image, and code generation
• Describe generative AI and its evolution
• Enhance skills required for success in data science by applying generative AI techniques
• Demonstrate use cases of generative AI for text generation
• Contrast generative AI with discriminative AI
• Overcome data preparation and querying challenges using generative AI models

Topics Covered

• Artificial Intelligence
• Know About How To Use Generative AI
• Implementing AI models Using Hugging Face, TensorFlow, and PyTorch
• ChatGPT
• Learning and understanding LLMs
• Natural Language Processing

COURSE 7: Foundation Models and Generative AI Platforms by IBM

Upon completing the Generative AI Essentials by IBM module, we move forward to foundation models, pre-trained AI platforms, and their role in text, image, and code generation. Explore IBM Watson, Hugging Face, IBM Granite, GPT, FLAN, and Llama, and gain hands-on experience with generative AI use cases.

Learning Outcomes

• Describe the features, capabilities, and applications of IBM Watsonx
• Explore the ability of foundation models to generate text, images, and code using pre-trained models
• Explain Hugging Face as a community for building AI models for everyone
• Demonstrate understanding of the course concepts through a graded quiz and project

Topics Covered

• Deep Learning and Large Language Models
• Generative AI Models
• Foundation Models
• Pretrained Models: Text-to-Image Generation
• Pre-trained Models: Text-to-Text Generation
• Pretrained Models: Text-to-Code Generation
• IBM Watsonx.ai: Generative AI for Business
• Hugging Face: Open Source Generative AI

COURSE 8: Capstone Project

Implement the skills you have gained throughout this program in this capstone project. You will solve industry-specific challenges by leveraging various AI and ML tools and techniques learned in the program modules. This project will help you showcase your new expertise to potential employer

ELECTIVE 1: Prompt Engineering Essentials by IBM

Master AI prompting techniques to interact eectively with models like ChatGPT, Gemini, and Claude. Learn prompt engineering frameworks, optimize AI outputs, and apply the Chain-of-Thought (CoT) approach to enhance AI reasoning and accuracy for better problem-solving and applications.

ELECTIVE 2: NLP and Speech Recognition

This advanced course focuses on applying machine learning techniques to large-scale natural language data. It covers natural language understanding, feature engineering, natural language generation, automated speech recognition, speech-to-text and text-to-speech technologies, voice assistants, and building Alexa skills. By the end, you’ll gain a strong grasp of the principles behind NLP and speech recognition, enabling you to create advanced, real-world applications.

ELECTIVE 3: Advanced Generative AI

This course examines the creative capabilities of generative AI, covering models such as variational autoencoders (VAEs), generative adversarial networks (GANs), large language models (LLMs), and transformer architectures. You’ll explore attention mechanisms, LangChain workflow design, and advanced prompt engineering, building the skills needed to design and optimize applications using state-of-the-art LLMs for targeted use cases.

ELECTIVE 4: Masterclasses by IBM

Join live, interactive online masterclasses led by IBM experts. These sessions build essential skills in artificial intelligence, generative AI, and machine learning, helping you drive innovation and stay competitive across industries.

ELECTIVE 6: Agentic AI Masterclass

This masterclass explores the next evolution of AI—autonomous agents capable of planning, reasoning, and executing complex tasks with minimal human involvement. You’ll learn how to design, build, and deploy AI-powered agents.

Project Options

• Analyzing Customer Orders Using Python
• Building a Python Adventure Game With GitHub Copilot
• Sales Analysis
• Marketing Campaigns
• Employee Turnover Analytics
• Creating Cohorts of Songs
• Crafting an AI-Powered HR Assistant: A Use Case for Nestle
• Creating Designs by Leveraging OpenAI and Gradio UI
• Home Loan Data Analysis
• Lending Club Loan Data Analysis
• Autonomous Driving
• Preserving Heritage: Enhancing Tourism With AI
• Sales Forecasting
• ChatGPT-Based Storytelling

FREQUENTLY ASKED QUESTIONS

How is the program delivered?

The course is delivered entirely online through live virtual classes, offering an 80:20 blend of experiential training and theoretical learning. You'll engage in hands-on projects, case studies, and interactive sessions led by industry experts. You will get access to a learning platform to keep track of all the schedules and materials.

What does the class schedule look like? Are there recordings?

The course typically spans about 6 months, with an estimated 8–10 hours of weekly study combining live sessions and self-paced learning. There will be mostly weekend classes with a variety of dates. In between courses, there will be a lot of hands-on projects to complete. Please email us to get the details of the schedule of the program. If you miss a class, you can always watch the recording.

NOTE:

Attendance cannot be marked by simply watching the session recordings. Attendance is recorded only when a learner joins the live session. Since these are university-affiliated programs, the criteria are more stringent, as they are set by the universities themselves. However recordings will be available . Learners can view the specific certificate criteria for each course directly on their LMS

Can I work full-time while enrolled in this program?

Yes, you can! The program schedule is designed to help busy professionals with full-time work. You can attend live instructor-led sessions which are held on weekends at the designated time according to your schedule and then complete assignments/projects during your free time. Please ask us first for detailed schedule information.

Can I change my cohort after enrolling in the program?

Yes. You’re entitled to one free cohort change within the first 60 days of enrollment. If you’re unable to continue with your current cohort after using this option, you may request an additional transfer for a fee. For guidance on the process or assistance with your request, please reach out to our support team.

How Is This Program Different from Other Online AI Courses or MOOCs?

Unlike typical self-paced courses, this program offers a university-backed, interactive learning model with over 200 hours of live sessions led by industry experts. Learners benefit from real-time engagement, dedicated mentorship, and peer collaboration, ensuring higher completion and deeper understanding while providing a University co-branded certificate, adding credibility and long-term career value.

What are the advantages of joining this program?

Enrolling in this AI and ML course offers a range of valuable benefits that can advance your career and enhance your professional skill set:

• In-Depth Knowledge: Master the latest AI and machine learning technologies.
• Career Growth: Position yourself for high-level roles in the tech industry.
• Higher Earning Potential: Acquire in-demand skills that can lead to better compensation.
• Practical Learning: Work on real-world projects to reinforce your understanding through hands-on application.
• Expert Guidance: Learn from experienced instructors and leading industry professionals.
• Professional Networking: Build connections with fellow learners and AI industry experts for future opportunities.

This program prepares you to take on advanced roles such as machine learning engineer, data scientist, or AI researcher with confidence and credibility.

What is the current job outlook for AI and ML professionals?

Industries such as healthcare, e-commerce, retail, and automotive are rapidly embracing AI innovations, driving a growing demand for skilled professionals who can help them stay competitive. By enrolling in an AI and ML course, you gain the advanced skills and practical knowledge needed to thrive in this fast-evolving field.

According to Fortune Business Insights, the global AI market is projected to reach $1,771.62 billion by 2032—highlighting strong long-term growth and job stability in the sector.

Pursuing a certification in AI and ML not only keeps you ahead of industry trends but also significantly enhances your career prospects.

Is AI and ML difficult to learn?

AI and ML can be challenging because they involve advanced concepts in mathematics and statistics. However, with commitment and the right learning tools—like a structured AI and ML course—these subjects become much more approachable. Many online programs simplify complex topics through step-by-step modules, making it easier for learners of all backgrounds to grasp and master the material.

What career paths are suitable after completing the program?

Finishing this artificial intelligence course can lead to a variety of exciting career opportunities across industries that demand expertise in data analysis, machine learning model development, and AI innovation. Potential roles include:

• Data Scientist
• Machine Learning Engineer
• AI Researcher
• Business Intelligence Developer
• Robotics Engineer
• Software Engineer
• Computer Vision Specialist
• Natural Language Processing (NLP) Engineer

These roles span sectors like healthcare, finance, e-commerce, automotive, and more—making your skills highly versatile and in demand.

How qualified and effective are the trainers?

The instructors for this AI and ML certificate course are highly qualified professionals with expertise in applied data science, machine learning, and generative AI. They’ve been carefully selected through a rigorous screening process that includes profile evaluation, technical assessments, alumni feedback, and live training demonstrations. This ensures you learn from experienced trainers who are not only subject matter experts but also skilled in delivering effective, engaging instruction.