Professional Certificate in Artificial Intelligence (AI) and Machine Learning (In collaboration with Purdue University & IBM)

Professional Certificate in Artificial Intelligence (AI) and Machine Learning 

In collaboration with Purdue University & IBM

• Live online masterclasses conducted by industry experts
• 6 Months length program (6-8 h/week weekend classes)
• Ask us for the next cohort and schedule details!

Advance your career with this AI and Machine Learning certification program, offered in partnership with Purdue University Online and IBM. Gain hands-on experience and expertise in high-demand areas like AI automation, ChatGPT, large language models (LLMs), deep learning, neural networks, chatbots, agentic frameworks, and more.

Key Features

• Course and material are in English
• Intermediate - Advanced level 
• 6 months duration of live classroom hands-on training delivered by industry experts (6-8h/week weekend classes)
• 350+ hours of study time and practice recommended
• 1 year access to learning platform & class recordings
• Join exclusive IBM-led hackathons and Ask Me Anything (AMA) sessions
• Program certificate from Purdue University Online & IBM
• Work on 3 capstone projects and over 15 hands-on exercises across diverse industry sectors
• Interact with learners and mentors via Slack.
• Dedicated cohort manager and mentoring sessions. 
• Join the prestigious Purdue University alumni community

About Purdue University

Purdue University is a leading public research university known for creating practical solutions to some of today’s most pressing problems. Recognized by U.S. News & World Report as one of the top 10 Most Innovative Universities in the U.S. for four consecutive years, Purdue is at the forefront of groundbreaking research and innovation.

What added value does Purdue University contribute to the program?

The program curriculum is designed and reviewed with the assistance of the university, which gives the program quality legitimacy and a co-branded certificate of completion. Please be aware that the live classes are not held by actual University faculty staff but by many experienced Industry experts who are suitable for each topic.

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
• Explore ChatGPT and discover its practical applications across various industries

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. Programming Refresher
2. Applied Data Science WithPython
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. Academic Masterclass
5. Industry Masterclass by IBM
6. Agentic AI Masterclass

COURSE CONTENT DETAILS

COURSE 1: Programming Refresher

In this module, you will develop essential Python skills crucial for your progress throughout the program.

Learning Outcomes

• Acquire proficiency in both procedural and object-oriented programming
• Install Python and its integrated development environment (IDE)
• Become familiar with Jupyter Notebook and its applications
• Understand Python’s data types, operators, and string functions
• Learn about the various types of loops in Python
• Explore the concept of variable scope within functions
• Describe methods, attributes, and access modifiers in Python

Topics Covered

• Fundamentals of Programming
• Python Data Types and Operators
• Conditional Statements and Loops in Python
• Introduction to Python Programming
• Python Functions
• Threading

COURSE 2: Applied Data Science with Python

After completing the Programming Refresher, we will move on to the Applied Data Science module with Python. This course provides a comprehensive foundation in data science, covering data preparation, model building, and evaluation. Learn Python essentials like strings, Lambda functions, lists, NumPy, linear algebra, and key statistical concepts. Master hypothesis testing (Z-test, T-test, ANOVA), data manipulation with pandas, and data visualization using Matplotlib, Seaborn, Plotly, and Bokeh.

Learning Outcomes

• Explain the fundamentals of data science and its applications
• Develop a strong understanding of NumPy and its applications
• Calculate measures of central tendency and dispersion in data
• Apply principles of linear algebra in data analysis, including its application in calculus
• Apply Python concepts related to strings, Lambda functions, and lists
• Examine different hypothesis tests, including Z-test, T-test, and ANOVA
• Create compelling visualizations using Matplotlib, Seaborn, Plotly, and Bokeh
• Work with pandas’ two primary data structures: Series and DataFrame

Topics Covered

• Introduction to Data Science
• NumPy
• Statistics Fundamentals
• Advanced Statistics
• Data Analysis
• Data Visualization
• Essentials of Python Programming
• Linear Algebra
• Probability Distributions
• Working with pandas
• Data Wrangling
• End-to-End Statistics Applications in Python

COURSE 3: Machine Learning

Upon understanding the Applied Data Science with Python module, we move on to Machine Learning. This course covers key machine learning types and applications, focusing on supervised learning (regression, classification), unsupervised learning (clustering, ensemble modeling), and the ML pipeline. Gain hands-on experience with TensorFlow, Keras, and PyTorch, including building a recommendation engine.

Learning Outcomes

• Examine various types of machine learning and understand their unique characteristics
• Explore supervised learning and its wide range of applications
• Analyze different regression models and identify their suitability for specific scenarios
• Evaluate and compare different machine learning frameworks, including TensorFlow and Keras
• Analyze the machine learning pipeline and gain a comprehensive understanding of it
• Build a recommendation engine using PyTorch
• Learn various types of unsupervised learning methods and determine their appropriate use

Topics Covered

• Machine Learning
• Regression and Its Applications
• Unsupervised Learning
• Recommendation Systems
• Supervised Learning
• Classification and Its Applications
• Ensemble Learning

COURSE 4: Deep Learning Specialization

After mastering Machine Learning, the next ideal step is to learn Deep Learning Specialization. This course equips you with the skills to deploy deep learning models using AI and ML frameworks. Learn the distinction between deep learning and machine learning, explore neural networks, CNNs, RNNs, transfer learning, object detection, and autoencoders, and gain hands-on experience with TensorFlow, Keras, and PyTorch to build and optimize models.

Learning Outcomes

• Gain a comprehensive understanding of different types of neural networks
• Learn the concepts of forward propagation and backward propagation in deep neural networks (DNN) 
• Learn about dropout and early-stopping techniques and their implementation
• Grasp the fundamentals of recurrent neural networks (RNN)
• Understand the basics of PyTorch and learn how to create a neural network using PyTorch
• Gain experience in convolutional neural networks (CNN) and object detection

Topics Covered

• Introduction to Deep Learning
• Object Detection
• PyTorch
• TensorFlow
• Model Optimization and Performance Improvement
• Getting Started with Autoencoders
• Artificial Neural Networks Recurrent
• Neural Networks (RNN)
• Deep Neural Networks
• Transfer Learning
• Transformer Models for Natural Language Processing (NLP)
• Convolutional Neural Networks (CNN)

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 Diusion, 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
• Pretrained Models: Text-to-Image Generation
• IBM Watsonx.ai: Generative AI for Business
• Pre-trained Models: Text-to-Text Generation
• Foundation Models
• Pretrained Models: Text-to-Code Generation
• 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

Explore advanced ML algorithms for language data processing, including NLU, NLG, and feature engineering for AI-driven text analysis. Gain expertise in automated speech recognition (ASR), speech-to-text, and text-to-speech conversion, and learn to develop and optimize voice assistants like Alexa and AI conversational interfaces.

ELECTIVE 3: Advanced Generative AI

Explore the role of transformers in modern AI and their impact on language and data processing. Analyze neural networks for generative tasks, including VAEs, GANs, transformers, and autoencoders. Learn attention mechanisms, study models like GPT and BERT, and compare their architectures, applications, and strengths in AI development

ELECTIVE 4: Academic Masterclass

Attend this live online masterclass conducted by Purdue University Online faculty and staff to get insights about the latest advancements in AI, ML and Gen AI.

ELECTIVE 5: Academic Masterclass

Attend this live industry masterclass led by IBM industry experts to explore cutting-edge technologies used in AI and Machine learning.

ELECTIVE 6: Agentic AI Masterclass

The Agentic AI Masterclass will dive deep into the next evolution of AI—autonomous AI agents that can plan, reason, and execute complex tasks with minimal human intervention. Learn how to design, build, and deploy AI-driven agents.

Project Options

Expense Tracker Using Python

In this project, you will build a simple expense tracker application that allows users to record daily expenses, categorize them, and generate spending reports using Python programming concepts such as data structures, control structures, and functions.

Real Estate Pricing

Use feature engineering to identify the top factors that influence price negotiations in the homebuying process.

Entertainment Analysis

Perform cluster analysis to create a recommended playlist of songs for users based on their user behavior.

Healthcare Application

Leverage deep learning algorithms to develop a facial recognition feature that helps diagnose patients for genetic disorders and their variations.

Song Classification with Cluster Analysis

Perform cluster analysis to create personalized song playlists for users based on their listening behavior.

Lending Club Loan Data Analysis

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

Financial Institution Modelling

Use deep learning to construct a model that predicts potential loan defaulters and ensures secure and trustworthy lending opportunities for a financial institution.

AI Recommendation Engine for Marketing

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

Predicting Employee Iteration with Machine Learning

Build a machine learning model to predict employee attrition by analyzing work habits and factors influencing their desire to stay with the company.

Virtual Project Management Consultant

Develop prompts for ChatGPT to function as a virtual project management consultant, providing advice on project planning, risk management, team collaboration, and performance tracking.

Road Safety Analysis of Autopilot Feature

Analyze accident data involving Tesla’s autopilot feature to assess the impact of autopilot technology on road safety.

Sales Strategy Analysis

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

Home Loan Data Analysis

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

ChatGPT Based Storytelling

Design an interactive storytelling adventure using ChatGPT to create unique, engaging narratives without coding.

Text to Design Platform with DALLE and Gradio

Create a platform that transforms text prompts into striking designs using OpenAI’s DALL-E and Gradio UI. Explore AI’s impact on digital content creation for marketing.

AI-Powered Vehicle Detection for Smarter Transport Systems

Build an AI model using deep learning to detect, classify, and localize vehicles in real-time, aiding AV tech and intelligent transport systems.

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.

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.