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PGP in Artificial Intelligence & Machine Learning: Business Applications

PGP in Artificial Intelligence & Machine Learning: Business Applications

Master AI applications and secure a future-ready career

Application closes 27th Feb 2025

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Program Outcomes

Elevate your career with advanced AI skills

Become an AI & Machine Learning expert

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    Lead AI innovation by mastering core AI & ML concepts & technologies

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    Build AI applications with GenAI, NLP, computer vision, predictive analytics, and recommendation systems

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    Build an impressive, industry-ready portfolio with hands-on projects.

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    Earn a bonus certificate in Python Foundations to strengthen your skills

Earn a Postgraduate certificate from UT Austin

  • U.S. News & World Report, 2024

    #7 Public University in the U.S.

    U.S. News & World Report, 2024

  • ranking 4

    #4 in MS - Business Analytics

    QS World University rankings, 2023

  • ranking 6

    #6 in Executive Education - Custom Programs

    Financial Times, 2022

  • us news

    #7 Business Analytics (In USA)

    U.S. News & World Report, 2022

Key program highlights

Why choose the AI & ML program

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    Learn from world’s top university

    Earn a certificate from a world-renowned university, taught by the esteemed faculty of UT Austin

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    Industry-ready 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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    Learn at your convenience

    Gain access to 200+ hours of content online, including lectures, assignments, and live webinars which you can access anytime, anywhere

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    8+ hands-on projects & 10+ tools

    Build projects made using data from top companies like Uber, Netflix, and Amazon and get hands-on training with projects and case studies

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    Get expert mentorship

    Interact with mentors who are experts in AI and get guidance to complete and showcase your projects

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    Personalized program support

    Get 1:1 personal assistance from a Program Manager to complete your course with ease.

Skills you will learn

Programming Fundamentals

Machine Learning

Computer Vision

Generative AI

Foundational Skills Certification

Problem-Solving Skills

Portfolio Development

Deep Learning

Natural Language Processing

AI Applications

Programming Fundamentals

Machine Learning

Computer Vision

Generative AI

Foundational Skills Certification

Problem-Solving Skills

Portfolio Development

Deep Learning

Natural Language Processing

AI Applications

view more

Secure top AI & machine learning jobs

  • $15 trillion

    AI net worth by 2030

  • $118 billion

    AI industry revenue

  • Up to $ 150K

    Avg annual salary

  • 97 million

    new jobs by 2025

Careers in AI & ML

Here are the ideal job roles in AI sought after by companies in India

  • AI Engineer

  • Machine Learning Engineer

  • AI Research Scientist

  • Prompt Engineer

  • Big Data Engineer

  • NLP Engineer

  • Deep Learning Engineer

  • Business Intelligence Developer

  • Compute Vision Engineer

  • AI Consultant

Our alumni work at top companies

  • Overview
  • Career Transitions
  • Why GL
  • Learning Journey
  • Curriculum
  • Projects
  • Tools
  • Certificate
  • Faculty
  • Mentors
  • Reviews
  • Career support
  • Fees
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This program is ideal for

The PG program in AI & ML empowers you to align your learning with your professional aspirations

View Batch Profile

  • Young professionals

    Kickstart your career in AI with foundational & advanced skills , real-world projects, and industry insights to ease into new roles

  • Mid-senior professionals

    Advance to senior roles with leadership learning, practical experience, and advanced AI/ML concepts

  • Project Managers

    Effectively manage AI/ML projects from implementation to deployment with expertise in tools, methodologies, and best practices

  • Tech Leaders

    Lead AI innovation with strategic insights, advanced AI & ML skills, and the ability to drive business transformation

Upskill with one of the best AI programs

  • UT Austin Programs

    Other Courses

  • Certificate

    hands upPG Certificate from UT Austin

    hands downNo university certificate

  • Gen AI modules

    hands upExtensive coverage of Gen AI topics

    hands downLimited coverage

  • Live mentored learning

    hands upLive interactive online classes with industry professionals 

    hands downLimited to no live classes

  • Career support

    hands upYes, with mock interviews and job boards

    hands downNo career support

  • Hands-on projects

    hands up10+ lab sessions, 8 projects & 40+ case studies

    hands downFewer projects

  • Program support

    hands upDedicated support to complete your course

    hands downLimited support

Experience a unique learning journey

Our pedagogy is designed to ensure career growth and transformation

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    Learn with self-paced videos

    Learn critical concepts from video lectures by faculty & AI experts

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    Engage with your mentors

    Clarify your doubts and gain practical skills during the weekend mentorship sessions

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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 whenever you need

Get an exclusive free preview of the course

Explore faculty videos and mentorship sessions. Get insights into relevant case-studies and projects.

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Elevate Your Skills with On-Campus Immersion (Optional Paid Program)

Decision Science and AI Program

In the 3-day immersive on-campus program you can:

  • Connect with like-minded AI professionals.

  • Immerse in On-Campus Learning for 3 Days

  • Earn 1.9 Continuing Education Units (CEUs) on successful completion of the program

  • Create Intelligent Decision Science Systems

Reach out to your Program Advisor for more details

Comprehensive Curriculum

The curriculum has been designed by the faculty at McCombs School of Business at the University of Texas at Austin.

  • 7 months

    learning content

  • 9+

    languages & tools

Foundations

The Foundations module comprises two courses where we get our hands dirty with Python programming language for Artificial Intelligence and Machine Learning and Statistical Learning, head-on. These two courses set our foundations for Artificial Intelligence and Machine Learning online course so that we sail through the rest of the journey with minimal hindrance. Welcome to the program.

Self-paced Module: Introduction to Data Science and AI

Gain an understanding of the evolution of AI and  Data Science over time, their application in industries, the mathematics and statistics behind them, and an overview of the life cycle of building data driven solution.

  • The fascinating history of Data Science and AI
  • Transforming Industries through Data Science and AI
  • The Math and Stats underlying the technology
  • Navigating the Data Science and AI Lifecycle

Self-paced Module: Pre-Work

Gain a fundamental understanding of the basics of Python programming and build a strong foundation of coding to build AI applications.

Module 1: Python Foundations

Python is an essential programming language in the tool-kit of an AI & ML professional. In this course, you will learn the essentials of Python and its packages for data analysis and computing, including NumPy, SciPy, Pandas, Seaborn and Matplotlib.

  • Python Programming Fundamentals

Python is a widely used high-level, interpreted programming language, having a simple, easy-to-learn syntax that highlights code readability.

This module will teach you how to work with Python syntax to executing your first code using essential Python fundamentals

  • Python for Data Science - NumPy and Pandas

NumPy is a Python package for scientific computing like working with arrays, such as multidimensional array objects, derived objects (like masked arrays and matrices), etc. Pandas is a fast, powerful, flexible, and simple-to-use open-source library in Python to analyse and manipulate data.

This module will give you a deep understanding of exploring data sets using Pandas and NumPy.

  • Data Visualization using Python

Data visualization is an important skill and one can create compelling visual representations of data to enable effective analysis and communication of insights. Python provides libraries to do this in a simple and effective manner.

  • Exploratory Data Analysis

Exploratory Data Analysis, or EDA, is essentially a type of storytelling for statisticians. It allows us to uncover patterns and insights, often with visual methods, within data.

This module will give you a deep insight into EDA in Python and visualization tools-Matplotlib and Seaborn.

  • Data Pre-processing

Data preprocessing is a crucial step in any machine learning project and involves cleaning, transforming, and organizing raw data to improve its quality and usability. The preprocessed data is used both analysis and modeling. 

  • Analyzing Text Data

Text data is one of the most common forms of data and analyzing it plays a crucial role in extracting valuable insights from unstructured information in human language. This module covers different text processing and vectorization techniques to efficiently extract information from raw textual data.

Self-paced Module: Statistical Learning

Statistical Learning is a branch of applied statistics that deals with Machine Learning, emphasizing statistical models and assessment of uncertainty. This course on statistics will work as a foundation for Artificial Intelligence and Machine Learning concepts learnt in this AI ML PG program.

  • Descriptive Statistics
    The study of data analysis by describing and summarising numerous data sets is called Descriptive Analysis. It can either be a sample of a region’s population or the marks achieved by 50 students.
    This module will help you understand Descriptive Statistics in Python for AI ML.
  • Inferential Statistics
    Inferential Statistics helps you how to use data for estimation and assess theories. You will know how to work with Inferential Statistics using Python.
  • Probability & Conditional Probability
    Probability is a mathematical tool used to study randomness, like the possibility of an event occurring in a random experiment. Conditional Probability is the likelihood of an event occurring provided that several other events have also occurred.
    In this module, you will learn about Probability and Conditional Probability in Python for AI ML.
  • Hypothesis Testing
    Hypothesis Testing is a necessary Statistical Learning procedure for doing experiments based on the observed/surveyed data.
    You will learn Hypothesis Testing used for AI and ML in this module.
  • Chi-square & ANOVA
    Chi-Square is a Hypothesis testing method used in Statistics, where you can measure how a model compares to actual observed/surveyed data.
    Analysis of Variance, also known as ANOVA, is a statistical technique used in AI and ML. You can split observed variance data into numerous components for additional analysis and tests using ANOVA.
    This module will teach you how to identify the significant differences between the means of two or more groups.

Machine Learning

The next module is the Machine Learning online course, where you will learn Machine Learning techniques and all the algorithms popularly used in Classical ML that fall in each category.

Module 2: Machine Learning

In this module, understand the concept of learning from data, build linear and non-linear models to capture the relationships between attributes and a known outcome, and discover patterns and segment data with no labels.

Supervised Machine Learning aims to build a model that makes predictions based on evidence in the presence of uncertainty. In this course, you will learn about Supervised Learning algorithms of Linear Regression and Logistic Regression.

  • Linear Regression

Linear Regression is one of the most popular supervised ML algorithms used for predictive analysis, resulting in producing the best outcomes. You can use this technique to assume a linear relationship between the independent variable and the dependent variable. You will cover all the concepts of Linear Regression in this module.

  • Decision Trees

A decision tree is a Supervised ML algorithm, which is used for both classification and regression problems. It is a hierarchical structure where internal nodes indicate the dataset features, branches represent the decision rules, and each leaf node indicates the result.

  • Unsupervised Learning

Unsupervised Learning finds hidden patterns or intrinsic structures in data. In this machine learning online course, you will learn about commonly-used clustering techniques like K-Means Clustering and Hierarchical Clustering along with Dimension Reduction techniques like Principal Component Analysis.

  • K-Means Clustering

K-means clustering is a popular unsupervised ML algorithm, which is used for resolving the clustering problems in Machine Learning. In this module, you will learn how the algorithm works and later implement it. This module will teach you the working of the algorithm and its implementation.

Module 3: Advanced Machine Learning

Ensemble methods help to improve the predictive performance of Machine Learning models. In this machine learning online course, you will learn about different Ensemble methods that combine several Machine Learning techniques into one predictive model in order to decrease variance, bias or improve predictions.

  • Bagging and Random Forests

In this module, you will learn Random Forest, a popular supervised ML algorithm that comprises several decision trees on the provided several subsets of datasets and calculates the average for enhancing the predictive accuracy of the dataset, and Bagging, an essential Ensemble Method.

  • Boosting

Boosting is an Ensemble Method which can enhance the stability and accuracy of machine learning algorithms, converting them into robust classification, etc.

  • Model Tuning

Model tuning is a crucial step in developing ML models and focuses on improving the performance of a model using different techniques like feature engineering, imbalance handling, regularization, and hyperparameter tuning to tweak the data and the model. This module covers the different techniques to tune the performance of an ML model to make it robust and generalized.

Artificial Intelligence & Deep Learning

The AI and Deep Learning course will take us beyond the traditional ML into the realm of Neural Networks. From the regular tabular data, we move on to training our models with unstructured data like Text and Images.

Module 4: Introduction to Neural Networks

In this module, implement neural networks to synthesize knowledge from data, demonstrate an understanding of different optimization algorithms and regularization techniques, and evaluate the factors that contribute to improving performance to build generalized and robust neural network models to solve business problems.

  • Deep Learning and its history

Deep Learning carries out the Machine Learning process using an ‘Artificial Neural Net’, which is composed of several levels arranged in a hierarchy. It has a rich history that can be traced back to the 1940s, but significant advancements occurred in the 2000s with the introduction of deep neural networks and the availability of large datasets and computational power.

  • Multi-layer Perceptron

The multilayer perceptron (MLP) is a type of artificial neural network with multiple layers of interconnected neurons, including an input layer, one or more hidden layers, and an output layer. It is a versatile architecture capable of learning complex patterns from data.

  • Activation functions

Activation Function is used for defining the output of a neural network from numerous inputs.

  • Backpropagation

Backpropagation is a key algorithm used in training artificial neural networks, enabling the calculation of gradients and the adjustment of weights and biases to iteratively improve the performance of a neural network.

  • Optimizers and its types

Optimizers are algorithms used to adjust the parameters of a neural network model during training to minimize the loss function. Different types of optimizers are Gradient Descent, RMSProp, Adam, etc.

  • Weight Initialization and Regularization

Weight initialization is the process of setting initial values for the weights of a neural network, which can significantly impact the model's training and convergence. Regularization is a technique used in machine learning/ neural networks to prevent the model from overfitting, which helps improve the model's generalization ability.

Module 5: Natural Language Processing with Generative AI

This course will help you get introduced to the world of natural language processing, gain a practical understanding of text embedding methods, gain a practical understanding of the working of different transformer architectures that lie at the core of large language models (LLMs), explore how retrieval augmented generation (RAG) integrates information retrieval to improve the accuracy and relevance of responses from an LLM, and design and implement robust NLP solutions using open-source LLMs combined with prompt engineering techniques.
  • Word Embeddings

Natural Language Processing (NLP) is a branch of AI that focuses on processing and understanding human language to facilitate the interaction of machines with it. Word embeddings allow us to numerically represent complex textual data, thereby enabling us to perform a variety of operations on them. This module introduces participants to the world of NLP, covers different word embedding techniques, and the steps involved in designing and implementing hands-on solutions combining word embedding methods with machine learning techniques for solving NLP problems

  • Attention Mechanism and Transformers

Transformers are neural network architectures that develop a context-aware understanding of data and have revolutionized the field of NLP by exhibiting exceptional performance across a wide variety of tasks. This module dives into the underlying workings of different transformer architectures and how to use them to solve complex NLP tasks.

  • Large Language Models and Prompt Engineering

Large Language Models (LLMs) are ML models that are pre-trained on large corpora of data and possess the ability to generate coherent and contextually relevant content. Prompt engineering is a process of iteratively deriving a specific set of instructions to help an LLM accomplish a specific task. This module introduces LLMs, explains their working, and covers practices to effectively devise prompts to solve problems using LLMs.

  • Retrieval Augmented Generation

Retrieval augmented generation (RAG) combines the power of encoder and generative models to produce more informative and accurate outputs from a knowledge base. This module will provide a thorough coverage of leveraging sentence transformers to encode data, vector databases to store and efficiently retrieve information from the encoded data, and LLMs to use the information to enhance the quality and relevance of the generated output.

Module 6: Introduction to Computer Vision

This course will introduce you to the world of computer vision, demonstrate an understanding of image processing and different methods to extract informative features from images, build convolutional neural networks (CNNs) to unearth hidden patterns in image data, and leverage common CNN architectures to solve image classification problems.

  • Image Processing

Computer Vision is a branch of AI that focuses on understanding and extracting meaningful insights from image data. This module provides an overview of the world of computer vision and covers techniques to process images and extract meaningful patterns from them.

  • Convolutional Neural Networks

Given the complex nature of image data, convolutional neural networks (CNNs) are utilized to capture relevant spatial information in images. Transfer learning is a method to leverage the underlying knowledge needed to solve one problem to other problems. This module will cover the fundamentals of CNNs, how to build them from scratch, and how to leverage common CNN architectures via transfer learning to solve different image classification problems

Module 7: Model Deployment

This course will help you comprehend the role of model deployment in realizing the value of an ML model and how to build and deploy an application using Python.

  • Introduction to Model Deployment
Model deployment is the process of making a trained machine learning model accessible to a wider audience by operationalizing it. This module introduces participants to model deployment, provides an overview of its need in generating business value from ML models, and serializing and deploying ML models using Python libraries like Streamlit.
  • Containerization

Containerization is the process of packaging applications and their dependencies into self-contained units called containers to ensure consistent execution across different environments. This module dives into packaging ML models and their dependencies into containers using Docker and simplifying deployment of the ML models using Python libraries like Flask.


Self-paced Module: Generative AI

Get an overview of Generative AI, what ChatGPT is and how it works. delve into the business applications of ChatGPT, and an overview of other generative AI models/tools via demonstrations.

  • ChatGPT and Generative AI - Overview
  • ChatGPT - Applications and Business
  • Breaking Down ChatGPT
  • Limitations and Beyond ChatGPT
  • Generative AI Demonstrations

Self-paced Module: Recommendation Systems

The last module in this Artificial Intelligence and Machine Learning online course is Recommendation Systems. A large number of companies use recommender systems, which are software that select products to recommend to individual customers. In this course, you will learn how to produce successful recommender systems that use past product purchase and satisfaction data to make high-quality personalized recommendations.

  • Popularity-based Model
    A popularity-based model is a recommendation system, which operates based on popularity or any currently trending models.
  • Market Basket Analysis
    Market Basket Analysis, also called Affinity Analysis, is a modeling technique based on the theory that if you purchase a specific group of items, then you are more probable to buy another group of items.
  • Content-based Model
    First, we accumulate the data explicitly or implicitly from the user. Next, we create a user profile dependent on this data, which is later used for user suggestions. The user gives us more information or takes more recommendation-based actions, which subsequently enhances the accuracy of the system. This technique is called a Content-based Recommendation System.
  • Collaborative Filtering
    Collaborative Filtering is a collective usage of algorithms where there are numerous strategies for identifying similar users or items to suggest the best recommendations.
  • Hybrid Recommendation Systems
    A Hybrid Recommendation system is a combination of numerous classification models and clustering techniques. This module will lecture you on how to work with a Hybrid Recommendation system.

Self-Paced Module: Introduction to SQL

This course will help you gain an understanding of the core concepts of databases and SQL, gain practical experience writing simple SQL queries to filter, manipulate, and retrieve data from relational databases, and utilize complex SQL queries with joins, window functions, and subqueries for data extraction and manipulation to solve real-world data problems and extract actionable business insights.

  • Introduction to DB and SQL 
  • Fetching, Filtering, and Aggregating Data 
  • Inbuilt and Window Functions 
  • Joins and Subqueries

Self-Paced Module: Multimodal Generative AI

This course will help you explore how to solve business problems by generating code using Generative AI tools, examine the capabilities of text-to-image and image-to-text GenAI tools like DallE through business use cases, and explore the speech recognition capabilities of audio-to-text GenAI tools like Whisper through business use cases.

  • Code Generation using GenAI 
  • Image Creation using GenAI 
  • Speech Recognition using GenAI

Career Assistance: Resume and LinkedIn profile review, interview preparation, 1:1 career coaching

This post-graduate certification program on artificial intelligence and machine learning will assist you through your career path to building your professional resume and reviewing your Linkedin profile. The program will also conduct mock interviews to boost your confidence and nurture you nailing your professional interviews. The program will also assist you with one-on-one career coaching with industry experts and guide you through a career fair.

Post Graduate Certificate from The University of Texas at Austin and 9.0 Continuing Education Units (CEUs)

Earn a Postgraduate Certificate in the top-rated Artificial Intelligence and Machine Learning online course from the University of Texas, Austin. The course’s comprehensive Curriculum will foster you into a highly-skilled professional in Artificial Intelligence and Machine Learning. It will help you land a job at the world’s leading corporation and power ahead your career transition.

On-Campus Immersion in Decision Science and AI (Optional Paid Program)

The Decision Science and AI is a 3-day on-campus Program that presents a valuable opportunity to explore AI use cases and become a driving force behind AI-driven initiatives within your organization. It comprises of dynamic discussions, collaboration with like-minded professionals, and engaging networking sessions hosted at the prestigious University of Texas at Austin.

Day 1

 

  • Welcome & Program Orientation
  • Introduction to Decision Sciences & AI
  • Campus Tour & Group Photo
  • Introduction to Dynamic Programming
  • Programming an AI agent to Play a Variant of Blackjack

Day 2

  • Introduction to Reinforcement Learning
  • Programming an AI Agent that learns by itself to play computer games
  • Session with Industry Mentor 
  • The Art and Science of Negotiations

Day 3

  • Project Brief and Active group work
  • Group work on Project 
  • Certifications and Photo Ops

Hands-on learning & AI training

Build industry-relevant skills with projects guided by experts.

  • 1,000+

    projects completed

  • 22+

    domains

  • 8

    real-world projects

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supervised learning

A Campaign to Sell Personal Loans

About the Project

Develop a predictive model using supervised learning to help a bank identify customers likely to purchase personal loans, analyze customer data, and deliver insights for targeted marketing

Skills you will learn

  • Data Preprocessing and Analysis
  • Supervised Learning Algorithms
  • Model Evaluation and Optimization
  • Business Problem Solving with AI/ML
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Feature Engineering & Model Tuning

Construction Material Strength

About the Project

Improve a predictive model for estimating construction material strength by applying feature engineering and model tuning, enhancing accuracy for better material selection and usage

Skills you will learn

  • Feature Engineering
  • Model Tuning and Optimization
  • Regression Techniques
  • Error Analysis and Performance Metrics
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ensemble techniques

Predict Potential Customers

About the Project

Use ensemble techniques to build a model that identifies customers likely to subscribe to a term deposit, enhancing accuracy by combining multiple machine learning algorithms

Skills you will learn

  • Feature Engineering and Selection
  • Ensemble Methods
  • Model Performance Evaluation
  • AI-Driven Marketing Insights
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Unsupervised Learning

Bank Customer Segmentation

About the Project

Use unsupervised learning to analyze bank customer data, identify segments based on spending and interactions, and help tailor marketing strategies to boost engagement.

Skills you will learn

  • Clustering Techniques
  • Data Exploration and Feature Engineering
  • Dimensionality Reduction
  • Customer Insights and Business Strategy
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neural networks

Identify Street View House Numbers

About the Project

Build an image classification model using neural networks to identify house numbers from street-view images by preprocessing data, designing the architecture, and training the model for accurate digit recognition

Skills you will learn

  • Image Data Preprocessing
  • Neural Network Architecture Design
  • Computer Vision Applications
  • Model Evaluation and Fine-tuning
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Recommendation Systems

E-Commerce Recommendation System

About the Project

Design a recommendation system for an e-commerce platform to suggest products using user behavior and product data, enhancing the shopping experience and boosting sales

Skills you will learn

  • Understanding Recommendation Techniques
  • Data Analysis and Feature Engineering
  • Matrix Factorization and Similarity Measures
  • Building Scalable Solutions
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Natural Language Processing

Sarcastic News Detection

About the Project

Build a model to detect sarcastic news headlines using Recurrent Neural Networks (RNNs) by analyzing text data, understanding context, and applying advanced NLP techniques for classification.

Skills you will learn

  • Text Preprocessing and Feature Engineering
  • Deep Learning with RNNs
  • Natural Language Understanding (NLU)
  • Model Evaluation and Interpretation

Master in-demand AI & ML tools

Get AI training with 8+ tools to enhance your workflow, optimize models, and build AI solutions

  • tools-icon

    Python

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    NumPy

  • tools-icon

    Keras

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    Tensorflow

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    Matplotlib

  • tools-icon

    Skitlearn

  • And More...

Earn a Professional Certificate from UT Austin

Get a PG certificate from one of the top universities in USA and showcase it to your network

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* Image for illustration only. Certificate subject to change.

Meet your faculty

Learn from the top, world-renowned faculty at UT Austin

  • Dr. Kumar Muthuraman - Faculty Director

    Dr. Kumar Muthuraman

    Professor, McCombs School of Business, UT Austin

    Faculty Director, Center for Analytics and Transformative Technologies

    21+ years' experience in AI, ML, Deep Learning, and NLP.

    Know More
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  • Dr. Daniel A Mitchell - Faculty Director

    Dr. Daniel A Mitchell

    Clinical Assistant Professor, McCombs School of Business, UT Austin

    Research Director, Center for Analytics and Transformative Technologies

    15+ years of experience in financial engineering and quantitative finance.

    Know More
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  • Dr. Abhinanda Sarkar - Faculty Director

    Dr. Abhinanda Sarkar

    Academic Director - Data Science & Machine Learning

    30+ years of experience in data science, ML, and analytics.

    Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.

    Know More
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  • Prof. Mukesh  Rao - Faculty Director

    Prof. Mukesh Rao

    Director, Academics, Great Learning

    20+ years of expertise in AI, machine learning, and analytics

    Director - Academics at Great Learning

    Know More
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  • Dr. Bradford Tuckfield - Faculty Director

    Dr. Bradford Tuckfield

    Founder - Kmbara & Data Science Consultant

    10+ years of expertise in statistics, programming, and machine learning.

    PhD. from the Wharton School, University of Pennsylvania

    Know More
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Interact with our mentors

Interact with dedicated AI and Machine Learning experts who will guide you in your earning and career journey

  •  Idris Malik - Mentor

    Idris Malik linkin icon

    Software Engineer, Machine Learning Meta
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  •  Nimish Srivastava - Mentor

    Nimish Srivastava linkin icon

    Senior Machine Learning Engineer Adobe
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  •  Franck Tchuente - Mentor

    Franck Tchuente linkin icon

    Senior Data Scientist Paper
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  •  Vybhav Reddy K C - Mentor

    Vybhav Reddy K C linkin icon

    Senior Data Scientist Socure
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  •  Dipjyoti Das - Mentor

    Dipjyoti Das

    Staff Data Scientist One Concern
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  •  Omid Badretale - Mentor

    Omid Badretale linkin icon

    Senior Research Data Scientist | Alternative Data RBC Capital Markets
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  •  Asghar Mohammadi - Mentor

    Asghar Mohammadi linkin icon

    Senior Data Scientist Cvent
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  •  Rafat Mohammed - Mentor

    Rafat Mohammed linkin icon

    Senior Data Scientist, Advanced Analytics Gordon Food Service
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  •  Mustakim Helal - Mentor

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    Senior Data Engineer CGI
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  •  Alisher Mansurov - Mentor

    Alisher Mansurov

    Assistant Professor Nipissing University
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  •  Shahzeb Shahid - Mentor

    Shahzeb Shahid linkin icon

    Senior Data Scientist Kroll
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  •  Yusuf Baktir - Mentor

    Yusuf Baktir

    Senior Data Scientist Wider Circle
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  •  Shekhar Tanwar - Mentor

    Shekhar Tanwar

    Machine Learning Engineer Highmark Inc.
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  •  Mahmudul Hasan - Mentor

    Mahmudul Hasan linkin icon

    Lead Data Scientist TELUS Communications
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  •  Olha Kuzaka - Mentor

    Olha Kuzaka linkin icon

    Senior Software Engineer 1 - Data, Tech Lead BenchSci
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  •  Karlos Muradyan - Mentor

    Karlos Muradyan linkin icon

    Data Scientist Teck Resources Limited
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  •  Marcelo Guarido de Andrade - Mentor

    Marcelo Guarido de Andrade linkin icon

    Senior Data Scientist and Head of the CREWES Data Science Initiative University of Calgary
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  •  Kandarp Patel - Mentor

    Kandarp Patel linkin icon

    Staff Data Scientist, AI/ML Walmart
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  •  Ben Brock - Mentor

    Ben Brock linkin icon

    Teaching Assistant to Professor Stuart Urban for Quantitative Financial Analysis course. Johns Hopkins University Carey School of Business
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Watch inspiring success stories

  • learner image
    Watch story

    "Flexible learning and real-world projects made me confident in AI/ML"

    The course's flexible schedule and hands-on projects helped me master Python and AI/ML concepts. Supportive instructors ensured doubts were addressed, giving me confidence to solve real-world problems.

    Animesh Bannerjee

    Director , Visa

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    "Mentoring sessions helped me learn AI from industry experts and build models."

    The program's mentoring sessions were exceptional, offering industry insights and clearing doubts. I successfully built AI and ML models, gaining skills that make me feel ahead of the curve.

    Aron Feseha

    Sr. Database Engineer , Lowes Pro

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    "Mentor-led sessions and hands-on projects made AI learning exceptional."

    The program’s balanced curriculum, engaging projects, and weekly mentor sessions were invaluable. It strengthened my Python skills, deepened my AI expertise, and provided an impressive deep dive into NLP concepts.

    James C McGrath

    Head of Investment Strategy and Advisor Consulting , AlphaTrAI

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    Interview preparation

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    Resume & Profile review

    Get your resume and LinkedIn profile reviewed by our experts to highlight your AI & ML skills & projects

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    E-portfolio

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

Course fees

The AI & ML course fee is 4,200 USD

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    Lead AI innovation by mastering core AI & ML concepts & technologies

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    Build AI applications with GenAI, NLP, computer vision, predictive analytics, and recommendation systems

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    Build an impressive, industry-ready portfolio with hands-on projects.

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    Earn a bonus certificate in Python Foundations to strengthen your skills

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    Upto 18 months Installment plans

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  • discount available

    Upfront discount:4,200 USD 4,000 USD

    Referral discount:4,200 USD 4,050 USD

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Application Closes: 27th Feb 2025

Application Closes: 27th Feb 2025

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Admission Process

Admissions close once the required number of participants enroll. Apply early to secure your spot

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    1. Fill application form

    Apply by filling a simple online application form.

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    2. Interview Process

    Go through a screening call with the Admission Director’s office.

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    3. Join program

    Selected candidates will receive an offer letter. Secure your seat by paying the admission fee.

Course Eligibility

  • Applicants should have a Bachelor's degree with a minimum of 50% aggregate marks or equivalent
  • For candidates who do not know Python, we offer a free pre-program tutorial

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career guidance

Post Graduate Program in AI and Machine Learning: Business Applications

Artificial Intelligence (AI) and Machine Learning (ML) are two key developments in computer science and data processing that are disrupting a wide range of sectors. Machine Learning is a subset of Artificial Intelligence that enables a system to automatically learn from prior data without explicit programming. AI aims to create intelligent computer systems that can address human challenges, processes, and technology issues.

The potential of Artificial Intelligence is expected to significantly increase workplace productivity and expand the range of tasks people can perform. As AI replaces tedious and monotonous tasks, the human workforce can focus on projects that, among other things, require creativity and empathy.

Though many online AI ML courses provide a conceptual understanding, they fall short in preparing learners to know what skills organizations look for and the opportunities to tap. To address this, faculties at the University of Texas at Austin and Great Learning have designed the Post Graduate program in AIML with the flexibility of an online program while maintaining the academic rigor, hands-on learning, program assistance, and peer interaction of a full-time course.

Why learn Artificial Intelligence and Machine Learning?

  • The AI market will be valued at $15 trillion or more by 2030.
  • Artificial Intelligence will generate 58 million jobs by 2022.
  • AI in business will bring in $118.6 billion annually by 2025.
  • 86% of rapidly expanding organizations believe AI is critical to their success.

Key Highlights Of the Program

  • Learning Format

An online classroom that delivers lectures in recorded and interactive mentored learning environments with hands-on practice alongside.

  • Teaching Academia

Top-rated academia from UT Austin and experts in the field of Artificial Intelligence and Machine Learning have designed the program to adopt the latest practices.

  • Career Support

Practical learning helps you prepare to make data-driven decisions on business problems and solid theoretical foundations. Receive personalized mentoring every weekend from professionals and industry experts in the Artificial Intelligence and Machine Learning fields as you solve real-world business problems.

  • Portfolio Building Projects

Create a portfolio comprising assignments, exams, case studies, and industry-oriented projects relevant to your field to demonstrate your skills to potential employers.

Program Design: Post Graduate Program in AIML - McCombs Business School

Great Learning offers the 7-month Post Graduate Artificial Intelligence and Machine Learning certificate course in collaboration with McCombs University at UT Austin. This program is designed to impart comprehensive knowledge in implementing AI applications and successfully build a career in Artificial Intelligence and Machine Learning. This program includes 12 modules and comprises 8+ projects.

This Post Graduate Program in AIML includes a Programming Bootcamp to help learners with no prior coding experience to acquire foundational programming skills. It covers the fundamentals of AIML along with in-depth understanding of Supervised Learning, Ensemble Techniques, Feature Engineering, Model Section and Tuning, and Unsupervised Learning.

Learners will also delve deep into various machine learning modules such as Neural Networks, Computer Vision, Natural Language Processing, and Statistical Learning and enhance their ability to develop real-time solutions for industry-specific problems. This program also includes the Recommendation System module to cover the Business Analytics application of AIML and the Model Deployment chapter to comprehend learners’ knowledge on developing scalable, robust, and future-friendly solutions.

Learning Outcomes

  • Gain in-depth knowledge of the most popular AI and ML tools and technologies.
  • Proficiency in using AI and Machine Learning to solve business problems on your own.
  • Gain practical experience required to create Deep Learning and Machine Learning models.
  • Recognize the potential and effects of Artificial Intelligence in various businesses.
  • Develop an exceptional work record and an industry-ready AI and ML portfolio.
  • Lead the implementation of artificial intelligence in your existing role within the organization.
  • Set a successful career in Artificial Intelligence and Machine Learning

Who is this UT Austin Machine Learning and Artificial Intelligence Program for?

  • Professionals and students who prefer to approach complex business problems by adopting modern technology.
  • Learners that are at ease working with sophisticated algorithms.
  • Learners with little or no prior programming knowledge
  • Learners interested in developing AI and ML applications integrated with technical innovations.

Reviews: PGP AI ML UT Austin (Program Reviews)

This Artificial Intelligence and Machine Learning course has continued to transform thousands of careers and stood testimonial in the past few years. The learners for this course are from varied backgrounds and professions.

About the University of Texas at Austin

The University of Texas at Austin is perhaps one of the leading public universities in the globe, hosting 51,000+ students and 3000+ world-class faculties. UT Austin is known across the world as a pioneer in the fields of social science, business, technology, and science. With an established track record of success, cutting-edge research, and teaching methodologies, you can be confident that you are learning from the finest academicians and researchers. UT Austin also offers an AI for Business Leaders program to those seeking to lead in this ever-evolving domain. (Explore AIFL Program)

AI ML Courses - Great Learning (Explore AI ML courses)

Artificial Intelligence and Machine Learning have, since the beginning, been the most promising and rapidly expanding subfields in computer science. The best Artificial Intelligence courses from Great Learning will equip you with the knowledge and skills needed to continue to be a pioneer in this quickly evolving discipline. You will acquire hands-on experience with cutting-edge tools and techniques while learning about the most recent AI research, algorithms, and applications from world-class universities, top-rated academia, and research experts. These programs are an excellent approach to get started if you want to start a career in AI or stay up to date on the newest advances.

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