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Program Satisfaction

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4.81/5

Course Report

Thousands of Careers Transformed

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    Michael Wang

    Director of Business Development, ISSI

    By breaking down advanced concepts into understandable terms, the course gave me the confidence and skills to advance my career.

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    Joydeep Bhattacharjee

    Sr Advisor, Architecture

    Perfect for those who want to get started in this field with little or no prior knowledge.

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    Samantha Fong

    Manager

    This program helped me re-enter the industry without having any relevant background.

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    Travis L Stoner

    Principal Product Owner, Hexagon

    The program allowed me to introduce to my workplace the ways in which we can take advantage of AI concepts and technologies.

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    Gerald Zuniga

    Technical Safety Lead

    Concepts accessible for professionals without programming background and sufficiently challenging for those with advanced knowledge in related fields.

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    Kingshuk Banerjee

    Software Engineering Director

    Recommend this course to anyone who is overwhelmed by the ML information on the web and wants a clear direction to navigate this exciting technical space.

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    Sujoy Joy

    Module & Process Owner

    The course gave me a fair coverage in terms of both breadth and depth of AI ML in 6 months

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    Adarsh Kumar

    Sr Project Manager

    Excellent course for students and professionals starting to develop skills needed in the field.

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Get Industry ready with Career Support

Job Posting

1:1 Industry Interactions

Personalised Resume

Resume & Linkedin Profile Review

Mock interviews

Interview Preparations & Demos

Portfolio Assessment

Online Portfolio Assessment

Why Choose Our Post Graduate Program in AI & ML

Interactive Mentor-led Sessions by Industry Experts

Augment weekly learning experience and gain industry insights in in live and interactive mentor-led sessions.

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Global Learning Experience

Upskill with a diverse cohort of professionals from all over the world and grow your professional network.

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People Network

Learn Fundamentals of Python Programming

Learn coding without prior experience .Earn a Certificate in Python Foundations.

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Real-world Business Projects

Build industry-relevant AI and machine learning skills with 8 hands-on projects under the guidance of experts.

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Transform your career with Artificial Intelligence & Machine Learning

Certificate from the University of Texas at Austin

Artficial intelligence and machine learning certificate by university of Texas at Austin

* Image for illustration only. Certificate subject to change.

  • MS - Business Analytics

    MS - Business Analytics

    QS World University rankings, 2022

  • Executive Education

    Executive Education

    Custom Programs by Financial Times, 2022

For any feedback & queries regarding the program, please reach out to us at MSB-AIML@mccombs.utexas.edu

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

  • Learn Leadership Skills

  • 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

Unit 1

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.

Unit 2

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.

  • Logistic Regression

Logistic Regression is also one of the most popular supervised ML algorithms, like Linear Regression. It is a simple classification algorithm where you can predict the categorical dependent variables with independent variables’ assistance. You will cover all the concepts of Logistic 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 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.

  • Hierarchical Clustering

Hierarchical Clustering is another popular unsupervised ML technique or algorithm, which is used for building a hierarchy or tree-like structure of clusters. For example, you can combine a list of unlabeled datasets into a cluster in the hierarchical structure.

  • Principal Component Analysis (PCA)

PCA is a dimensionality reduction technique used to transform a high-dimensional dataset into a lower-dimensional space. This can help in choosing and retaining only the variable which capture the highest amount of variablity in the data as they will be the most important ones.

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.

  • Cross Validation

Cross-validation is a technique used to evaluate the performance of machine learning models, which helps in getting a clearer picture of an ML model's generalization ability. K-fold cross validation is one of the most common approaches.

  • Class Imbalance Handling

Class imbalance handling is a crucial task in machine learning when the distribution of classes in the dataset is uneven. There are different techniques like RandomUnderSampler, SMOTE, etc. used for this purpose.

  • Hyperparameter Tuning

Hyperparameter tuning is the process of finding the optimal values for hyperparameters in a machine learning algorithm. It involves exploring different combinations of hyperparameter values and evaluating their impact on the model's performance, often using techniques like grid search, random search, etc

Self-paced Module: Model Deployment

This last module of the machine learning online course will discuss the model deployment techniques and techniques around making your model scalable, robust, and reproducible.

  • Packaging Models
    Model Packaging helps you package all the necessary assets to host a model as a web service. It also enables you to download either a fully built Docker image or the files required to make one.
  • Rest APIs, Dockers
    RESTful API, also known as Representational State Transfer, is an API that uses HTTP requests like GET, PUT, POST, and DELETE to communicate with web services.
    Docker is one of the most popular tools, which is used to create, deploy, and run applications with the help of containers.
    This module will teach how to package up an application using containers.
  • ML Pipeline and Model Scalability
    In this module, you will learn everything you need to know about ML Pipeline and Model Scalability used for ML models.

Unit 3

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: Introduction to Computer Vision

  • Overview of Computer Vision (CV)

In this module, you will drive through all the business applications of computer vision and learn how it impacted several business industries.

  • Understanding images (Color Pixel Theory and Image Representation)

This module will teach you how to process the image and extract all the data from it, where you can use the data for image recognition in deep learning.

  • Convolution Operation and Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNN) are used for image processing, classification, segmentation, and numerous other applications. This module will give you a deep understanding of CNNs from scratch.

  • CNN Architectures

CNN architectures are specialized deep learning models designed for processing grid-like data, such as images or audio. Common architectures include VGG16, ResNet, InceptionNet, etc.

  • Transfer Learning

Transfer learning is a technique in neural networks where a pre-trained model, usually trained on a large dataset, is used as a starting point for solving a related task. It allows leveraging the knowledge and learned features from the pre-trained model, reducing the need for extensive training on limited data, and often leads to improved performance and faster convergence.

Module 6: Introduction to Natural Language Processing

Get introduced to the world of natural language processing, gain a practical understanding of text processing and vectorization methods, gain a practical understanding of the working of different transformer architectures that lie at the core of large language models (LLMs), and design and implement robust NLP solutions using open-source LLMs combined with prompt engineering techniques.

  • Overview of Natural Language Processing (NLP)

This module will get you comfortable with the introduction to NLP and, later, teach you all the essential business applications you need to know about NLP. Natural Language Processing (NLP) applies computational linguistics to build real-world applications, which work with languages consisting of varying structures. First, we teach the computer to learn languages and then expect it to understand them with relevant, efficient algorithms.

  • Attention Mechanism and Transformer Models

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 working of transformer models and how to use them to solve complex NLP tasks.

  • Word Embeddings

Word embeddings allow us to numerically represent complex textual data, thereby enabling us to perform a variety of operations on them. This module will cover 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.

  • 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.

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.

Unit 4

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.

Unit 5

Post Graduate Certificate from The University of Texas at Austin

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.

Unit 6

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

Languages and Tools covered

  • tools-icon

    Python

  • tools-icon

    NumPy

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    Keras

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    Tensorflow

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    Matplotlib

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    Skitlearn

Hands-on Projects

  • 1000+

    Projects completed

  • 22+

    Domains

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Supervised Learning

A campaign to sell personal loans

Build a model that helps to identify potential customers of a bank who have a higher probability of purchasing a loan.
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Ensemble Techniques

Predict Potential Customers

Build a model that will help the marketing team of a company to identify potential customers for a term deposit subscription.
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Feature Engineering & Model Tuning

Construction Material Strength

Perform Feature Engineering and Model Tuning on a model designed to predict the strength of construction material to enhance accuracy.
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Unsupervised Learning

Bank Customer Segmentation

Identify different segments from a bank’s existing customer pool based on their spending patterns as well as past interactions with the bank.
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Neural Networks

Identify Street View House Numbers

Build an Image Classification model to classify street view house numbers using Neural Networks.
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Natural Language Processing

Sarcastic News Detection

Detect sarcastic news headlines using a Recurrent Neural Network architecture on different news headlines.
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Recommendation Systems

e-Commerce Recommendation System

Build your own recommendation system for products on an e-commerce website.

Our Faculty and Mentors

Learn from prominent academicians in the field of Data Science and Business Analytics, as well as a number of highly skilled industry practitioners from top companies.

Dr. Kumar Muthuraman - Faculty Director

Dr. Kumar Muthuraman

Faculty Director - Centre for Research and Analytics 20+ Years Work Experience

Dr. Dan Mitchell - Faculty Director

Dr. Dan Mitchell

Assistant Professor, McCombs School of Business

Dr. Abhinanda Sarkar - Faculty Director

Dr. Abhinanda Sarkar

Faculty Director, Great Learning

Prof. Mukesh  Rao - Faculty Director

Prof. Mukesh Rao

Director, Academics, Great Learning

Dr. Bradford Tuckfield - Faculty Director

Dr. Bradford Tuckfield

Data Science Expert

Industry Mentors from Top Organisations

Idris Malik - Mentor

Idris Malik

Software Engineer, Machine Learning

Nimish Srivastava - Mentor

Nimish Srivastava

Senior Machine Learning Engineer

Franck Tchuente - Mentor

Franck Tchuente

Senior Data Scientist

Vybhav Reddy K C - Mentor

Vybhav Reddy K C

Senior Data Scientist

Dipjyoti Das - Mentor

Dipjyoti Das

Staff Data Scientist

Omid Badretale - Mentor

Omid Badretale

Senior Research Data Scientist | Alternative Data

Asghar Mohammadi - Mentor

Asghar Mohammadi

Senior Data Scientist

Rafat Mohammed - Mentor

Rafat Mohammed

Senior Data Scientist, Advanced Analytics

Mustakim Helal - Mentor

Mustakim Helal

Senior Data Engineer

Alisher Mansurov - Mentor

Alisher Mansurov

Assistant Professor

Shahzeb Shahid - Mentor

Shahzeb Shahid

Senior Data Scientist

Yusuf Baktir - Mentor

Yusuf Baktir

Senior Data Scientist

Shekhar Tanwar - Mentor

Shekhar Tanwar

Machine Learning Engineer

Mahmudul Hasan - Mentor

Mahmudul Hasan

Lead Data Scientist

Olha Kuzaka - Mentor

Olha Kuzaka

Senior Software Engineer 1 - Data, Tech Lead

Karlos Muradyan - Mentor

Karlos Muradyan

Data Scientist

Marcelo Guarido de Andrade - Mentor

Marcelo Guarido de Andrade

Senior Data Scientist and Head of the CREWES Data Science Initiative

Kandarp Patel - Mentor

Kandarp Patel

Staff Data Scientist, AI/ML

Ben Brock - Mentor

Ben Brock

Teaching Assistant to Professor Stuart Urban for Quantitative Financial Analysis course.

Learner Testimonials

  • "It has exceeded my expectations. I literally walked away feeling great and confident. I was intimidated by artificial intelligence. Now I'm not. That’s where I see the impact. "

    Alston Noah, CEO, Vincari (United States)

  • "The fact that each video can be watched during a lunch break or during downtime at work in a way that you can understand makes the learning journey more rewarding, satisfying, and manageable. "

    William Matthew Tyler, Sr. Associate Consultant, Infosys (United States)

  • "The support system was key, like having a mentor, coordination manager, those sorts of concepts, and I didn't find that in many of the other ones. If you're balancing your work, your family, and studying, then this sort of thing really helps you. "

    Tandeep Sandhu, Solutions Director, HCL (United States)

  • "The program is perfect for someone who has little to no experience in the field of Data Science. For me, the brochure and the information provided syllabus, requirements, and delivery schedule were the main selling points. I would wholeheartedly recommend this program to anyone who wants to jumpstart a career in Data science. "

    David Hickman, Director-Data Science & Analytics, PE Impact (United States)

  • "I liked the concept of learn and apply at Great Learning. The program gave me the confidence to be able to solve complex problems and figure out the tools that can help me do that. The mentor sessions were incredible, with all mentors always going above and beyond when it came to imparting knowledge. "

    Stephanie Nicole Baker, Research Associate, TACC-UT Austin (United States)

  • "The program helped me upgrade my skillsets to understand the concepts that emerging technologies are bringing . It helped me upskill exactly in the same technologies that my company was working into, and gave me the ability to work efficiently in this field. "

    Ana Alfaro, Senior Demand Management Systems Analyst, NXP (United States)

  • "The Mentor Learning sessions and the ability to network with a diverse cohort were the two things that made me take the course. The case studies in the program help us solve real world problems with much ease! "

    Dustin Lee, Junior Technical Consultant, ProLytX (United States)

  • "The content has been well thought out and the team has been very responsive. It has been a great experience for me, and I would recommend this program to my colleagues. "

    Deepa Chandrasekaran, Director-Strategic Development, IMI (UK)

  • "My experience with my program advisor has been great. He is very receptive and solves all doubts I have. The program advisor pushes us to achieve our goals consistently, which makes this program better than others. "

    Everth Hernandez, Sales Director, Aruba -HPE (Mexico)

  • "I speak the language now when I get talking to my clients or when I go for business development activities. The course has offered me that edge and confidence to understand the field of AI and ML better. "

    Kokila Narayanan, Senior Consultant, CGI (United States)

  • "The program is helping me in my current job, where I am going to incorporate my learnings. With the transition happening in the industry, this program is a great stepping stone. "

    Afshan Parkar, Instructor, Zayedh University (UAE)

  • "I am very greatful to the program office, as they helped me throughout the learning journey. Anytime I had a request, the program advisor would respond very quickly and effectively. "

    Dimitrios Zografos, Director-Asset Management, IPTO (Greece)

  • "My learnings through projects allowed me to solve problems at my job, especially problems related to computer vision and robotic processes. I am also greatful to the program advisor, who helped us every time we faced a problem. "

    Endri Hoxha, Automation Engineer, Alten (Switzerland)

  • "The faculty and videos have been fantastic. At the end of each and every session, there were practice modules that were provided to us. We also had a project discussion forum where anybody in the team who was working on the project could raise a question and the team would answer it. "

    Gaurang Laxmanbhai Patel, IT Project Manager, L&T Infotech (United States)

  • "The way it was structured, the timings, and how it was broken down were really good. I started noticing that I had pretty much touched all the important areas or fundamental areas that would actually help me take this subject or my learning to the next level. "

    Shadab Syed, Specialist - Information Security, QIB (Qatar)

Program Fees

Starting at 233 USD/month*

Program Fee: 4,200 USD

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Pay in Intsallments

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Upfront Payment & Referral

Upfront Discount:
4,200 USD

4,000 USD

Referral Discount:
4,200 USD

4,050 USD

Payment Partners

affirm - Payment Partner uplift Climb Credit - Payment Partner d-local - Payment Partner

*Subject to partner approval based on regions & eligibility. dLocal for Brazil, Colombia & Mexico learners. Other partners for U.S. learners only.

Benefits of learning from us

  • High-quality content
  • 8+ hands-on projects
  • Live mentored learning in micro classes
  • Doubt solving by industry experts
  • Live webinars by UT Austin faculty
  • Career support services
  • Additional Certificate in Python Foundations

This program helped me gain hands-on skills with guidance from industry practitioners. And this is just what employers require.

Bernard Tumanjong

Bernard Tumanjong

Information Systems Engineer U.S. Army

Admission Process

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

  • steps icon

    1. Fill application form

    Apply by filling a simple online application form.

  • steps icon

    2. Interview Process

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

  • steps icon

    3. Join program

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

Batch Start Date

Frequently asked questions

Program Details

Who are the mentors as part of this program?

You can find the details of the mentors in the program page. Suffice to say, the mentors are industry practitioners with leading organizations and come with extensive experience in their fields.

Will the content be available after the program is completed?

We believe that learning is continuous and hence all learning material – lecture notes, online content and supporting material – will be available through the online platform for 3 years after completion of the program.

How will I be evaluated during the program?

In this holistic and rigorous program, you will be evaluated continuously. All quizzes, assignments, attendance and projects are used to evaluate and monitor your progress towards the desired learning outcomes.

What kind of career support can I expect from this program?

The PG Program in Artificial Intelligence & Machine Learning: Business Applications offers career support to ensure you derive not just positive learning outcomes, but also the career outcomes you desire for your professional journey. You can expect career guidance through 1:1 career coaching sessions with industry practitioners, resume and LinkedIn profile review, interview preparation sessions, and webinars with UT Austin faculty.

Do candidates need to bring their own laptops?

The candidates need to bring their own laptops; the technology requirement shall be shared at the time of enrolment.

How does this AIML online certificate course fit my schedule?

This online program is designed and scheduled to be delivered online in 7 months and includes weekend mentorship. Learners willing to take a full-time course or working professionals aspiring to learn from an online AIML course can benefit from a UT Austin AIML certificate program. This program understands the demands of a full-time job, offering flexible online learning that allows you to fit skill development around your existing commitments. Master in-demand Artificial Intelligence and Machine Learning concepts at your own pace, without putting your career on hold.

What is the eligibility to learn this AI certification course online?

UT Austin Artificial Intelligence and Machine Learning program aspirants must hold a bachelor's degree with a minimum aggregate of 50% or equivalent scores with programming experience. Participants can learn Python programming as a precourse work and do not need to have expertise in Python.

What is this AI and Machine Learning course?


The PG program in Artificial Intelligence and Machine Learning is meticulously designed to equip learners with the skills and knowledge to reshape and empower them, ensuring they are well-positioned to advance in rapidly evolving technology. The program offers unparalleled flexibility through a blend of academic rigor, comprehensive learning resources, and collaborative peer engagement.

What is the required weekly time commitment?

Each week involves around 2-3 hours of recorded lectures and an additional 2-hour mentored learning session each weekend, which includes hands-on practical applications and problem-solving. The program also involves around an hour of practice exercises or assessments each week. Additionally, based on your background, you should expect to invest 2 to 4 hours every week in self-study and practice. So, that amounts to a time commitment of 8-10 hours per week.

Will I receive a transcript or grade sheet after completion of the program?

The Post Graduate Program in Data Science and Business Analytics is an online professional certificate program offered by the McCombs School of Business in collaboration with Great Learning. You will receive the grade sheet post-completion; however, the program does not carry any credits. Also, your performance will be assessed through individual assessments and module completion to determine your eligibility for the certificate.


Upon completing all the modules in accordance with the qualifying requirements for the program, you'll receive a certificate from the University of Texas at Austin.

What AIML techniques can I learn to apply from this course?

You can learn and apply various Artificial Intelligence Markup Language techniques from this course, though it primarily focuses on machine learning concepts. 
 

Here's a breakdown of relevant techniques:
 

  • Natural Language Processing Fundamentals: While AIML and NLP are distinct fields, this course covers foundational NLP concepts applicable to AIML. You will learn about text processing, sentiment analysis, and word embeddings, which can be used to build chatbots or virtual assistants that understand and respond to user input.

 

  • Prompt Engineering for Generative AI: The course covers prompt engineering for guiding large language models. These techniques can be applied to craft effective prompts for chatbots or virtual assistants powered by AIML engines.

 

  • Building Chatbots with Scripting and Limited Machine Learning: While the course emphasizes machine learning, the underlying concepts of data structures, variables, and conditional logic apply to building basic chatbots using scripting languages.

 

  • Entity and Relationship Recognition: The course covers Entity-Relationship (ER) diagrams, a fundamental concept used in AIML for representing entities (objects) and the relationships between them. This knowledge can be applied to design chatbots that can understand and respond to queries involving multiple entities. For instance, a restaurant reservation chatbot could leverage ER concepts to recognize entities like "date," "time," and "party size" within a user's request.

 

  • Simple Pattern Matching and Rule-Based Chatbots: While the course emphasizes machine learning, the basics of pattern matching and conditional logic covered in the Python programming section can be applied to build simple rule-based chatbots. These chatbots can be programmed with specific keywords or patterns and corresponding responses, allowing for a limited conversation with users.

 

  • Data Preprocessing for AIML Chatbots: The course covers data cleaning techniques like handling missing values and outliers.  While the focus is on preparing data for machine learning models, these techniques also apply to cleaning and preparing chatbot training data. Clean training data can improve the accuracy and effectiveness of AIML chatbots by ensuring they understand and respond to user queries consistently.

 

This course focuses on AI and machine learning algorithms and techniques that can be used to create more intelligent and interactive applications.

Why Artificial Intelligence and Machine Learning

What is Artificial Intelligence?

Artificial Intelligence is the technology used to build intelligent machines that act as humans do. The AI enabled systems to mimic human behavior and perform tasks as we do. This intelligence is built using complex algorithms and mathematical functions. 

Artificial Intelligence is the technology that is being applied in almost every industry and business. AI is literally everywhere. We are witnessing the presence of Artificial Intelligence every single day of our lives. Artificial Intelligence is applied in smartphones, smart window treatments, banking, self-driving cars, healthcare, social media, video games, surveillance, and many other aspects of our daily life. 

 

What is Machine Learning?

Machine Learning is an important subset of Artificial Intelligence. Machine learning is one of the most interesting careers that you could choose. Machine learning is perceived as one of the fastest-growing technologies. 

Machine learning is a subset of artificial intelligence that provides systems the ability to automatically learn and progress from experience without being specifically instructed. By employing Machine Learning techniques, businesses can automate routine tasks and maximize profits. Hence, pursuing a PG in achine learning and artificial intelligence would fetch you the best career opportunities. 

 

Is learning Artificial Intelligence worth it?

 

Artificial Intelligence is one of the most latest trending technologies. Artificial Intelligence is not just about creating robots or building computer systems that can think as humans do. Artificial Intelligence is a technology that understands humans and makes their lives easy. From Apple's Siri to Google's voice assistant, from facebook friend recommendations to Netflix's movie recommendations, Artificial Intelligence is playing the most pivotal role in making our lives easy. AI in simple words can be defined as an interface to us and the computer devices, it is the technology that makes the systems understand humans so well. The technology of AI is just growing at a rapid pace and the number of industries and businesses adapting this technology is reaching the skies. There is a huge demand for AI professionals across the globe. Hence, taking up the best Artificial Intelligence course and pursuing a career in this domain stands as the best choice you could make for yourself.

 

What is the pay scale of Artificial Intelligence and Machine Learning professionals across the world?

The pay scale offered in the domain of Artificial Intelligence is one of the major factors that is motivating many to pursue a career in this domain. The job roles offered in this domain are considered to be one of the highest-paid across the globe. In the United States, the pay scale of Artificial Intelligence and Machine Learning professionals ranges from $90k to $305k per annum. The average pay scale is expected to be $164,769 per annum. While in India it ranges from 6 to 35 lakh per annum and the average pay scale is estimated as 21,86,857 per annum. Hence, the demand for Artificial Intelligence and Machine Learning courses is at its peak across the world. 

 

Who can pursue an Artificial Intelligence Course?

The Artificial Intelligence Courses designed by Great Learning are suitable for someone who is:

  • As computer science with artificial intelligence is an exciting combination, a developer who wants to become a Machine Learning Engineer or Artificial Intelligence Scientist would take up an AI learning course. 
  • Analytics Managers that drive a team composed of Analysts could learn AI. 
  • Analytics professionals that desire to work in AI or Machine Learning
  • Fresh graduates who want to secure a career in Machine Learning or AI could take up the pg in artificial intelligence courses.
  • Managers or Business owners who desire to become AI-enabled professionals can opt for the AI for leaders course.
  • Experienced working professionals that want to employ AI in their existing work field.

What are the various benefits of Artificial Intelligence and Machine Learning?

The technology of Artificial Intelligence has a lot more to contribute to any industry than individuals do. Hence many businesses are applying advanced artificial intelligence to draw the best outcomes.

Let us understand a few of the benefits.

  • Building better business strategyBy employing Artificial Intelligence, organizations can develop the best business plan. Artificial Intelligence renders solutions to come up with the best business plan that supports companies' flourish. Today, most of the top-notch companies are applying Artificial Intelligence in project and operation management to obtain better outcomes.
  • Better Research and InventionsOrganizations must be conscious of the latest trends in their market. An AI-enabled business team would shape their business in the best way that suits the requirements of end customers. An AI-enabled organization would learn current technological trends, plan a business strategy that delivers the best services. Businesses with a good vision and well versed with AI can compose a groundbreaking solution. AI assists businesses to add value to their products by adapting themselves to the latest trends in the market, technology.  
  • Cost ReductionCost reduction is one of the major benefits that AI contributes to any business. Small and medium scale certainly strive for their endurance considering their limited budget and resources. With a substantial demand for AI professionals, these companies may not be able to afford such resources to meet their needs. Hence, businesses need to adopt AI so that they can reduce costs to the company. AI in business draws more customers that explore solutions for their problems. Therefore, taking up an AI certification course would fetch you with the best career opportunities in several industries in the market. 

 

What are the Applications of AI in different industries?

Many believe that Artificial Intelligence and Machine Learning are limited to the IT industry. AI is being applied everywhere in every industry across the world.

Let us understand how AI is being employed in several industries today.

  • Customer Support: The domain of AI is observed to replace many customer support job roles. Today, most websites are using chatbots to assist customers. The AI-enabled chatbot systems are capable of addressing customer's problems and provide the user with the most meaningful product recommendations at a faster pace.
  • E-commerce: With the employment of an AI recommendation system, E-commerce websites are offering personalized shopping experiences to their users. The systems study the user's past purchase records and recommend the most suitable products. The system learns the customer's choice and presents the most meaningful recommendations. This makes the user experience a personalized shopping experience. In this way, AI is benefitting the E-commerce industry by enhancing the customer experience. Today, a lot of e commerce giants such as Amazon employ AI to drive their businesses. 

 

Artificial Intelligence in Social Media

Social Media has become an indispensable part of our daily lives. We spend most of our time on Social media platforms such as Facebook, Twitter, Instagram, and more. There is a huge amount of data being generated through social media websites in the form of messages, tweets, posts, and more. In social media platforms like Facebook, Artificial Intelligence is used for face recognition while Machine Learning and Deep Learning concepts are used to recognize the facial features of people and automatically suggest you tag them. Twitter's AI is being used to identify hate speech and terroristic language in tweets by employing Natural Language Processing.

Hence, check out the best courses in Artificial Intelligence, learn AI today, and get into the most in-demand job roles of the 21st century.


 

What are the cutting-edge Artificial Intelligence applications?

AI is rapidly evolving and making significant strides across various industries. 
 

Here are some of the most cutting-edge applications currently transforming digital ad technological settings:
 

  • Generative AI: This subfield focuses on creating new data, such as images, text, or music. Applications include generating realistic product mockups, composing creative content, and even personalizing educational materials.

 

  • Large Language Models: These powerful AI models are trained on huge amount of text data, enabling them to communicate and generate human-like text to respond to a variety of prompts and questions. LLMs are being used for tasks like writing different kinds of creative content, translating languages, and powering intelligent chatbots.

 

  • Computer Vision with Deep Learning: Advancements in convolutional neural networks are enabling AI to process and analyze visual information with exceptional accuracy. Applications include self-driving cars, object detection and recognition in videos and images, and automated visual inspection in manufacturing.

 

  • Natural Language Processing: AI can now understand and respond to human language with increasing sophistication. NLP is used in sentiment analysis, machine translation, voice assistants like Siri and Alexa, and chatbots that can hold more nuanced conversations.

 

  • Reinforcement Learning: This type of AI learns through trial and error, making it ideal for complex tasks requiring strategic decision-making. RL is being explored in areas like robotics, game playing, and even optimizing traffic flow in smart cities.

Is AI and Machine Learning in demand?

Yes, AI and ML are in high demand across various industries. 
 

Here's why:
 

  • Increased Data Availability: The exponential growth of data has fueled the development of AI and machine learning models. Businesses are increasingly seeking professionals who can extract valuable insights from this data.
     

  • Automating Tasks: AI can automate repetitive tasks, freeing up human resources for more strategic and creative endeavors. This improves efficiency and productivity across various sectors.
     

  • Solving Complex Problems: AI can tackle complex problems that were previously beyond human capabilities. This allows businesses to optimize operations, develop innovative products, and gain a competitive edge.
     

  • Personalization and Customization: AI can personalize user experiences and design products and services to individual needs. This enhances customer satisfaction and loyalty.

 

The demand for AI and machine learning skills is expected to continue growing in the foreseeable future. As AI continues to evolve, it will significantly shape digital and technological sectors.

Fee & Payment

What is the refund policy?

Please note that submitting the admission fee does constitute enrolling in the program and the below cancellation penalties will be applied:

1) Full refund can only be issued within 48 hours of enrollment
2) Admission Fee - If cancellation is requested after 48 hours of enrollment, the admission fee will not be refunded.
3) Fee paid in excess of the admission fee: 

1. Refund or dropout requests requested more than 4 weeks before the Commencement Date are eligible for a full refund of the amount paid in excess of the admission fee

2. Refund or dropout requests requested more than 2 weeks before the Commencement Date are eligible for a 75% refund of the amount paid in excess of the admission fee

3. Refund or dropout requests requested more than 24 hours before the Commencement Date are eligible for a 50% refund of the amount paid in excess of the admission fee

4. Requests received after the Commencement Date are not eligible for a refund. 

Cancellation must be requested in writing to the program office.

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Get in touch with our Program Advisors & get your queries clarified.

Speak with our expert +1 512 861 6570 or email to aiml.utaustin@mygreatlearning.com

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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