Get a 360° understanding of AI and Machine Learning

Masterclasses delivered by distinguished UT Austin faculty

  • Python

    Python

  • Deep Learning

    Deep Learning

  • Machine Learning

    Machine Learning

  • Computer Vision

    Computer Vision

  • Neural Networks

    Neural Networks

  • Hugging Face

    Hugging Face

  • Prompt Engineering

    Prompt Engineering

  • NLP with Generative AI

    NLP with Generative AI

Thousands of careers empowered

  • 50%

    Average salary hike

  • 3,300+

    Hiring Companies

*Across Great Learning programs

  • Awani - Learner

    Awani

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    Artificial Intelligence & Machine Learning

    Assistant Manager- Data Science Analytics

    Mazars India

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    Senior Data Scientist(Engineer)

    HERE Technologies

  • Archit Narang - Learner

    Archit Narang

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    Artificial Intelligence & Machine Learning

    Data Scientist

    FUTURE CONSUMER LIMITED

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    Senior Data Scientist

    Technokart Consultancy Services LLP

  • Asit Panigrahy - Learner

    Asit Panigrahy

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    Artificial Intelligence & Machine Learning

    Senior Data Scientist

    INNOVA SOLUTIONS USA

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    Data Scientist Specialist

    Brillio

  • Praneeth Pragallapati - Learner

    Praneeth Pragallapati

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    Artificial Intelligence & Machine Learning

    Senior Business Analyst

    Tredence Inc. Full-time

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    Data Scientist

    Fractal Full-time

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    Sai Kumar Yava

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    Artificial Intelligence & Machine Learning

    Machine Learning Engineer

    Innoclique Cognitive Technologies Pvt. Ltd

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    Senior ML Engineer - Data & Insights @ EXP+ Explore

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

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  • MS - Business Analytics

    MS - Business Analytics

    QS World University rankings, 2022

  • Executive Education

    Executive Education

    Custom Programs by Financial Times, 2022

Program Curriculum

The curriculum, designed by the faculty of UT Austin, Great Learning, and leading industry practitioners, is taught by best-in-class professors and practicing industry experts. The program's objective is to familiarize learners with the concepts of AI, ML, and Generative AI necessary to establish their career or transition to a career in the field of AI and ML.

225+ hrs

Learning content

20+

Languages & Tools

Unit 1

Learn at Your Own Pace

Data Science and AI: Introduction

  • 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

Generative AI: From Concepts to Applications
  • ChatGPT and Generative AI overview 
  • ChatGPT applications and business
  • Generative AI demonstrations

Pre-work: Building Your AI Foundation
  • Introduction to Python programming
  • AI application case study

Unit 2

Course 1: Python Foundations

Python Programming

  • Understand key concepts: Variables and data types
  • Explore data structures for effective data management
  • Implement conditional and looping statements
  • Develop functions for code efficiency and reuse

Python for Data Science
  • Work with NumPy: Arrays, functions, and file operations
  • Create and modify Pandas Series and DataFrames
  • Utilize Pandas functions for data manipulation
  • Save and load datasets seamlessly with Pandas

Exploratory Data Analysis
  • Gain insights through data overview techniques
  • Perform univariate analysis using histograms, boxplots, and bar graphs
  • Conduct bivariate/multivariate analysis with various plotting techniques
  • Customize plots for better data visualization
  • Address missing values and detect outliers

Analyzing Text Data
  • Master text processing techniques: Stopword removal, stemming, and cleaning
  • Explore text vectorization methods: Bag of words and n-grams

Unit 3

Course 2: Machine Learning

Linear Regression

  • Introduction to the concept of learning from data
  • Explore types of Machine Learning: Supervised and unsupervised
  • Understand business problem and solution space in regression
  • Analyze correlation and linear relationships
  • Build simple and multiple linear regression models
  • Incorporate categorical variables in linear regression
  • Evaluate performance using regression metrics

Decision Trees
  • Investigate business problem and solution space in classification
  • Introduction to decision trees and their applications
  • Learn about impurity measures and splitting criteria
  • Assess model effectiveness with classification metrics
  • Understand the concept of pruning to enhance model performance
  • Apply decision trees for regression task

K-means Clustering
  • Define a business problem and solution space in clustering
  • Explore distance metrics for clustering analysis
  • Introduction to clustering techniques and their types
  • Build K-means clustering models for data segmentation
  • Utilize t-SNE for visualizing high-dimensional data

Unit 4

Course 3: Advanced Machine Learning

Bagging

  • Explore the concept of ensemble techniques for model improvement
  • Introduction to bagging and its benefits
  • Understand sampling with replacement methods
  • Dive into random forest and its application in ensemble learning

Boosting
  • Introduction to boosting and its significance in model performance
  • Learn about key boosting algorithms: Adaboost, Gradient Boosting, and XGBoost
  • Explore stacking techniques to combine model predictions

Model Tuning
  • Master feature engineering techniques for enhanced model input
  • Implement cross-validation for reliable performance evaluation
  • Explore oversampling and undersampling methods for balanced datasets
  • Focus on model tuning and performance optimization
  • Dive into hyperparameter tuning techniques: Grid search and random search
  • Understand regularization methods to prevent overfitting

Unit 5

Course 4: Introduction to Neural Networks

Introduction to Neural Networks

  • Explore Deep Learning and its historical context
  • Understand the multi-layer perceptron structure
  • Learn about dierent types of activation functions
  • Train a neural network and understand the backpropagation process

Optimizing Neural Networks
  • Discover various optimizers and their types
  • Implement weight initialization techniques
  • Explore regularization methods and their importance
  • Examine different types of neural networks

Unit 6

Course 5: Natural Language Processing with Generative AI

Word Embeddings

  • Introduction to Natural Language Processing (NLP) and its history
  • Conduct sentiment analysis and understand its applications
  • Learn about word embeddings: Word2Vec and GloVe
  • Explore concepts of semantic search

Attention Mechanism and Transformers
  • Understand the fundamentals of transformers
  • Explore the components of a transformer and their functions
  • Review different transformer architectures and their applications

Large Language Models and Prompt Engineering
  • Introduction to Large Language Models (LLMs) and their workings
  • Explore applications of LLMs across various domains
  • Understand Prompt Engineering and strategies for eective prompts

Retrieval Augmented Generation
  • Learn about embeddings and Tokenization techniques
  • Understand Byte-Pair Encoding (BPE) Tokenization
  • Compute and apply Sentence Embeddings
  • Explore Retrieval Augmented Generation (RAG) for improved responses

Unit 7

Course 6: Introduction to Computer Vision

Image Processing

  • Overview of computer vision and its applications
  • Understand color pixel and image representation
  • Explore edge detection and the use of Kernels
  • Learn about padding, strides, pooling, and flattening images to a 1D array

Convolutional Neural Networks
  • Compare ANN vs. CNN and their dierences
  • Explore CNN architecture and its components
  • Introduction to transfer learning and its applications
  • Review common CNN architectures

Unit 8

Course 7: Model Deployment

Introduction to Model Deployment

  • Understand the significance of model deployment in ML
  • Learn about serialization techniques
  • Deploy a model using Streamlit for practical applications

Containerization
  • Introduction to containerization and its benefits
  • Explore docker and its role in deployment
  • Deploy a model using Flask for web applications

Unit 9

Additional Modules: Learn at your Own Pace

Multimodal Generative AI

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

Statistical Learning

  • Probability Fundamentals
  • Probability Distributions
  • Sampling and Central Limit Theorem
  • Estimation Theory
  • Hypothesis Testing

Recommendation Systems

  • Introduction to Recommendation Systems
  • Market Basket Analysis
  • Popularity-based and Content-based Recommendation Systems
  • Collaborative Filtering and Hybrid Recommendation Systems

Introduction to SQL


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

Learn Generative AI with Hugging Face

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    Hugging Face is a platform where the machine learning community collaborates on models, datasets, and applications. It allows users to build, train, and deploy machine learning and AI models by leveraging the models and datasets that are shared by the community, and also test and share their work openly.

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    One of the key components of the Hugging Face platform is the Transformers library, an open-source Python library that provides access to thousands of pre-trained transformers and large language models (LLMs) for natural language processing (NLP) and computer vision (CV).

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    In this program, you’ll be using the Transformers library to access and use state-of-the-art LLMs for a variety of tasks like text classification, question-answering, summarization, text generation, sentence similarity, and more.

Languages and Tools covered

  • tools-icon

    Python

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    Pandas

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    NumPy

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    Seaborn

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    Matplotlib

  • tools-icon

    scikit-learn

  • tools-icon

    Hugging Face

  • tools-icon

    LangChain

  • tools-icon

    ChatGPT

  • tools-icon

    Tensorflow

  • tools-icon

    Keras

  • tools-icon

    OpenCV

  • tools-icon

    Gemini

  • tools-icon

    SpaCy

  • tools-icon

    Docker

  • tools-icon

    Flask

  • tools-icon

    Transformers

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    Dall.E

  • tools-icon

    Whisper

  • tools-icon

    MySQL

  • And More...

 Hands-On Case Studies

Data sets from the industry
UBER · NETFLIX · AMAZON

  • 7

    Hands-on projects

  • 40+

    Case studies

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BFSI

Company Annual Financial Report Analysis

Aid financial analysts at Apple to extract key information from long financial documents like annual reports very quickly using RAG and thereby increasing efficiency in making key financial decisions.

Tools & Concepts: Generative AI, Large Language Models, Prompt Engineering, Hugging Face, Retrieval Augmented Generation, Vector Databases, Langchain
project icon

Food and Beverages

Restaurant Review Analysis

Analyze the customer reviews for different restaurants for a leading global food aggregator and use generative AI models to analyze the reviews and tag them, thereby enhancing the company's ability to understand customer sentiments at scale, enabling data-driven decision-making, and improving overall customer satisfaction.

Tools & Concepts: Generative AI, Large Language Models, Prompt Engineering, Hugging Face
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News and Media

E-news Platform News Categorization

Efficiently categorize and tag news articles for an e-news platform for improved content organization and user engagement

Tools & Concepts: Generative AI, Sentence Transformers, Hugging Face, Sentence Similarity, Text Classification
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Hospitality

Hotel Booking Cancellation Prediction

Build a Data Science solution for a chain of hotels that will help them predict the likelihood of a booking getting canceled so that they can take measures to fill in potential vacancies and reduce revenue loss

Tools & Concepts: Exploratory Data Analysis, Decision Trees, Random Forest, Scikit Learn, Pandas
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Manufacturing

Machine Predictive Maintenance

Analyze the data of an auto component manufacturing company and develop a predictive model to detect potential machine failures, determine the most influencing factors on machine health, and provide recommendations for cost optimization to the management

Tools & Concepts: Exploratory Data Analysis, Data Visualization, Decision Trees, Pruning, Scikit-Learn
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Healthcare

COVID Detection

Build an AI solution for a renowned chain of hospitals that will help them predict the likelihood of a patient being infected by COVID by analyzing a chest X-ray scan of the patient to segregate the patients who are less likely to have COVID and prioritize critical cases

Tools & Concepts: Image Processing, OpenCV, TensorFlow, Keras, Image Classification
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BFSI

Credit Card Fraud Detection

Analyze credit card transaction data and build a neural network model to capture the complexities in the data and predict the probability of a transaction being fraudulent to help minimize financial losses incurred by the financial institution and the cardholders

Tools & Concepts: Exploratory Data Analysis, Neural Networks, TensorFlow, Keras
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BFSI

Bank Customer Segmentation

Identify different segments in the existing customers, based on their spending patterns as well as past interaction with the bank, using clustering algorithms and provide recommendations to the bank on how to better market to and service these customers.

Tools & Concepts: Exploratory Data Analysis, K-means Clustering, t-SNE, Scikit-Learn
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Retail

Used Car Price Prediction

Explore and visualize the data, build a linear regression model to predict the prices of used cars, and generate a set of insights and recommendations that will help the business

Tools & Concepts: Exploratory Data Analysis, Missing Value Treatment, Data Visualization, Linear Regression, Scikit-Learn
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BFSI

CredPay

Analyze the data provided by a consultation firm that partners with banks, answer key questions provided, draw actionable insights, and help the company to improve the business by identifying the attributes of customers eligible for a credit card

Tools & Concepts: Exploratory Data Analysis, Data Visualization, Pandas, Seaborn

Faculty and Mentors

Learn from leading academicians in the field of Artificial Intelligence and Machine Learning and several experienced industry practitioners from top organisations.

Dr. Kumar Muthuraman - Faculty Director

Dr. Kumar Muthuraman

Faculty Director, McCombs School of Business, The University of Texas at Austin

Dr. Abhinanda Sarkar - Faculty Director

Dr. Abhinanda Sarkar

Senior Faculty & Director Academics, Great Learning

Dr. Pavankumar Gurazada - Faculty Director

Dr. Pavankumar Gurazada

Senior Faculty, Academics, Great Learning

Prof. Mukesh Rao - Faculty Director

Prof. Mukesh Rao

Senior Faculty, Academics, Great Learning

Dr. Bradford Tuckfield - Faculty Director

Dr. Bradford Tuckfield

Co-Founder & Director, Wilson Consulting

Mr. Udit Mehrotra - Faculty Director

Mr. Udit Mehrotra

Data Scientist, Stripe

Dr. Daniel A Mitchell - Faculty Director

Dr. Daniel A Mitchell

Clinical Assistant Professor, McCombs School of Business, The University of Texas at Austin

Industry Mentors from Top Organisations

Sandeep Raghuwanshi - Mentor

Sandeep Raghuwanshi

Senior Specialist - Technology, Synechron

Bhaskar Mothali - Mentor

Bhaskar Mothali

Director - QMS Product Management, Novartis

Murali Balasubramanian - Mentor

Murali Balasubramanian

Regional Head for Technologies, Training and Skills, Stellantis

Ketan Bhatt - Mentor

Ketan Bhatt

Chief Operating Officer, Dharohar

Saurabh Bagchi - Mentor

Saurabh Bagchi

Vice President, JPMorgan Chase & Co

Naga Pavan Kumar Kalepu - Mentor

Naga Pavan Kumar Kalepu

Ind & Func AI Decision Science Manager, Accenture

Shajin Majeed - Mentor

Shajin Majeed

Assistant Manager-CAE, Tata Technologies

Jyant Mahara - Mentor

Jyant Mahara

Data Science Lead, Zscaler

Learner Testimonials

  • "You have no reason to worry even if you don’t have any programming background because that will be taken care of during the course."

    Siva Sai Kumar, Staff Design Engineer

  • "I found the curriculum to be extremely precise and structured to the topics discussed."

    Senthil Mohan, Designer Project Lead

  • "With the course and the projects that we undertook, it brought in a lot of discipline in me to continue working and upskilling at the same time."

    Jai Arora, Director of Engineering

Program Fees

Starting at ₹ 5,023/month

Program Fee: ₹ 2,10,000 + GST

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Pay in Intsallments

Pay in Installments

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As low as ₹ 5,023/month

for 60 months

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Third Party Credit Facilitators

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Benefits of learning from us

  • 7 months online
  • 20 online mentorship sessions
  • 7 hands-on projects & 40+ case studies
  • Work on 20+ tools and Languages
  • Dedicated Program Manager
  • Academic Learning Support
  • Detailed performance review
  • Certificate of Completion from UT Austin
  • Shareable e-Portfolio

“Great Learning had been the ideal choice when I was in a fix to change track in my IT career.”

Nishitha R

Nishitha R

Senior Business Analyst, Tredence

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.

Limited seats available

Apply today and become a AIML expert

Apply Now

Batch Start Date

Frequently asked questions

Program Details

What is the duration and format of the Post Graduate Program in Artificial Intelligence and Machine Learning (Executive)?

The PGP in Artificial Intelligence and Machine Learning (Executive) is a 7-month online program.

What are the main highlights of this AIML Program?

The main highlights of this PGP AIML Program for Executives are:

  • 20+ tools and languages are covered in the curriculum. 
  • Detailed performance reviews are conducted throughout the program. 
  • 20 online mentorship sessions that provide expert guidance. 
  • 7 hands-on projects which offer practical, real-world experience. 
  • 40+ case studies that expose learners to diverse business problems. 
  • Each participant is assigned a dedicated program manager. 
  • Academic learning support is available for coursework and projects. 
  • Certificate of completion awarded from the McCombs School of Business at The University of Texas at Austin. 
  • Graduates receive a shareable e-portfolio to showcase their skills and projects.

Who should consider enrolling in this AI and ML executive program?

This Postgraduate Program in AI and Machine Learning is for: 


  • Mid to senior professionals aiming to integrate AI/ML into tech infrastructures 
  • AI implementation leaders 
  • Aspiring AI practitioners 
  • Advanced AI learners wishing to master technical applications

How is this AIML Program for Executives different from other providers?

This Texas McCombs AIML program is based on the core principle of 'learning by doing.' It equips learners with practical, hands-on skills through case studies, projects, and a portfolio of AI, ML, and Generative Al work. This approach prepares them to transition into or advance within one of the most lucrative and fast-growing careers today

Will I receive a certificate after completing the program?

After completing the Post Graduate Program in Artificial Intelligence and Machine Learning (Executive), you will receive a certificate of completion from The McCombs School of Business at the University of Texas at Austin.

What support does this program provide to its learners?

This AI and Machine Learning program by Texas McCombs offers several layers of support to ensure a practical learning experience for participants:


Dedicated Program Manager: Every learner has access to a dedicated program manager who guides them through the program, coordinates schedules, and resolves administrative queries. 

Academic Learning Support: Learners receive continual academic assistance for their coursework, concepts, and project work. 

20 Online Mentorship Sessions : These sessions are led by industry mentors and academic faculty, providing personalized guidance, career insights, and answers to technical or industry questions. 

Hands-on Projects and Case Studies: With 7 hands-on projects and 40+ case studies, learners get practical exposure and the opportunity to build a robust portfolio. 

Detailed Performance Review: The program includes regular reviews, feedback, and performance tracking to help learners stay on track and improve continuously. 

Shareable E-portfolio: Graduates can showcase their learning and projects through a shareable e-portfolio, useful for job applications and professional visibility.

What skills and competencies will I gain from this Texas McCombs AI ML program?

This AI and Machine Learning Program for Executives by Texas McCombs offers the following skills and competencies: 


  • You will develop the ability to solve business problems with machine learning, deep learning, and generative AI using Python. 

  • You will gain expertise in widely used AI and ML tools and technologies for building, evaluating, and deploying models. 

  • You will learn to deliver actionable insights, manipulate and visualize data, and build robust predictive solutions. 

  • You will acquire practical knowledge in deploying AI solutions, performing data analysis, implementing recommendation systems, and leveraging SQL for data handling. 

  • You will master the end-to-end process of developing, optimizing, and deploying scalable AI/ML systems in real-world business contexts.

Can I complete this program in an accelerated manner by taking multiple courses per term?

This program follows a cohort-based approach. So, you must finish these courses in a specified order and time period.

What is the deadline to enrol in this program?

Admissions for the upcoming batch will close once we reach the required number of participants. Seats are limited and given out on a first-come, first-served basis. We encourage you to apply early to secure your spot in the program.

Faculty, Curriculum and Projects

Who is the faculty for this executive AI and ML program?

Name

Position / Affiliation

Expertise / Background

Dr. Kumar Muthuraman

H. Timothy Harkins Centennial Professor, UT Austin

Ph.D., Stanford; Info, Risk & Ops Mgmt & Finance; 21+ yrs experience

Dr. Abhinanda Sarkar

Academy Director, Great Learning

Ph.D., Stanford; ex-MIT Prof.; 30+ yrs; Data Science, ML, Academia

Dr. Daniel Mitchell

Clinical Asst. Prof., UT Austin

Ph.D., UT Austin; Analytics, ML; ex-Minnesota/Singapore faculty; 20+ yrs

Dr. Pavankumar Gurazada

Senior Faculty, Great Learning

Ph.D., IIM Lucknow; Biz & AI Programs; Data Science Advisor; 11+ yrs

Prof. Mukesh Rao

Senior Faculty, Great Learning

PGD in Biz Administration; Data Science, Big Data Teams head; 20+ yrs

Dr. Bradford Tuckfield

Co-Founder, Wilson Consulting; Data Science Consultant

Ph.D., Wharton; Statistics, Programming, ML

Mr. Udit Mehrotra

Data Specialist, Stripe; ex-McKinsey

UT Austin Certified; Dell, Modal, Great Learning; Data Science Specialist

Does this program cover Generative AI and Large Language Models (LLMs)?

Yes, this AI certificate program covers Generative AI and Large Language Models (LLMs), including modules on concepts, applications, transformer architectures, and prompt engineering.

What are the tools and languages covered in this program?

This program covers a comprehensive set of industry-standard tools and languages essential for AI, machine learning, and data science work. These include: 


  • Python 
  • Pandas 
  • NumPy 
  • Seaborn 
  • Matplotlib 
  • Scikit-learn 
  • Hugging Face Transformers 
  • LangChain 
  • ChatGPT 
  • TensorFlow 
  • Keras 
  • OpenCV 
  • Spacy 
  • Docker 
  • Flask 
  • Gemini 
  • DALL-E 
  • Whisper 
  • MySQL

Who are the mentors for this executive AI and ML program?

The Texas McCombs AIML program features industry mentors who are leading data scientists and analytics managers at top global organizations such as Apple, Amazon, Meta, Course Hero, and more.

What topics are covered in the curriculum of this AI course?

This AI and Machine Learning Program’s curriculum covers: 


  • Python foundations 
  • Machine learning 
  • Advanced ML 
  • Neural networks 
  • NLP with generative AI 
  • Computer vision 
  • Model deployment and additional modules in multimodal generative AI 
  • Statistical learning 
  • Recommendation systems 
  • SQL.

What kind of projects and case studies are included in this artificial intelligence program?

Sample projects include: 


  • Foodhub Order Analysis 
  • Personal Loan Campaign 
  • Purchase Prediction EasyVisa 
  • Renewal Prediction RenewWind Equipment Health Prediction and Plant Seedling Classification. 

Case studies encompass: 


  • Restaurant Review Analysis
  • E-News Platform 
  • News Categorization 
  • Hotel Booking Cancellation Prediction 
  • COVID Detection 
  • Bank Customer Segmentation.

Eligibility, Admissions, and Fees

What are the eligibility criteria for the AI and ML executive program?

The program is suitable for professionals at mid to senior levels, implementation leaders, aspirants building a technical career in AI, and advanced learners in AI/ML.

What is the admission process for the program?

Step 1 : Fill up an online Application Form 

Step 2: Eligible applications will go through a screening call 

Step 3: Review of the Application Form 

Step 4: Release of the Offer Letter

What is the fee for this AIML program for Executives?

For the most up-to-date information on the course fee, please refer to the official program page here.

Is there any financial assistance provided to candidates to pursue the Post Graduate Program AI and Machine Learning?

Candidates interested in financial assistance or options such as offers and payment plans are advised to contact a program advisor for detailed information and support related to financial aid and scholarships.

Career-Related Queries

What kind of career support or benefits does this AI and ML program offer?

The program prepares learners for careers in AI and ML by providing hands-on skills, project experience, and a portfolio. 


Alumni work at leading companies including Microsoft, Amazon, Google, Deloitte, and McKinsey & Company.

What career opportunities are available after completing this certificate program in AIML?

After completing Texas Mcombs’ AIML Program, graduates are equipped for roles such as: 


AI/ML Engineer: Developing algorithms, building, and deploying machine learning or deep learning models. 


Data Scientist: Analyzing and interpreting complex data, delivering actionable insights using statistical and AI/ML tools. 


Business/Data Analyst: Building predictive models, optimizing business processes, and generating reports using AI. 


AI Implementation Leader: Leading AI adoption and integration in organizations and their technology infrastructures. 


NLP Engineer: Working on natural language processing projects, such as sentiment analysis, language modeling, and chatbots. 


Computer Vision Specialist: Designing solutions for image and video data using deep learning and computer vision frameworks. 


Recommendation Systems Engineer: Creating and optimizing personalized recommendation engines for products and services. 


AI Product Manager: Managing the development, deployment, and scaling of AI-powered products and solutions. 


Graduates of this program work at leading technology and consulting companies such as Microsoft, Amazon, Google, Deloitte, and McKinsey & Company, leveraging their practical skills and portfolio built during the program to advance or transition into high-growth roles within the AI/ML industry. 


The program’s shareable e-portfolio and certificate further strengthen your career prospects in AI and data-driven domains

I have 10+ years of experience in a non-tech role. Can I transition into an AIML role?

Yes. Many of our learners from non-technical backgrounds have transitioned successfully into AIML roles. However, salary hikes depend on your domain, interview performance, and target companies.

Still have queries? Let’s Connect

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

Speak with our expert 080-4718-9252 or email to aiml@greatlearning.in

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