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Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
EnglishSelf StudyVideo and Text Based

Course Overview

The Post Graduate Program in Artificial Intelligence & Machine Learning is a comprehensive and hands-on course. The course is 12 months and is provided in a fully online mode. It covers cutting-edge technologies like Deep Learning, Computer Vision, NLP, and Reinforcement Learning, along with practical projects and industry insights. 

The Post Graduate Program in Artificial Intelligence and Machine Learning certification by Great Learning and the University of Texas at Austin is designed by expert faculty to ensure a great learning experience. Through this programme, learners gain valuable skills and knowledge to excel in AI and ML roles. 

In the Post Graduate Program in Artificial Intelligence and Machine Learning training, candidates will receive career support, resume reviews, and interview preparation. You will also get access to a vast job board with opportunities from 2800+ companies. Whether you are a beginner or an experienced professional, this programme can open doors to promising career prospects in the dynamic field of Artificial Intelligence and Machine Learning.

Also Read:  Artificial Intelligence And Machine Learning Certification Courses

The Highlights

  • Hands-On Experience
  • Case Studies
  • Offered by the University of Texas at Austin
  • 12 Months Duration
  • Online Bootcamp
  • Flexible Learning

Programme Offerings

  • Mentorship Session
  • assignments
  • Readings
  • LIVE Webinar
  • discussion forum
  • Capstone Project
  • Experiential Learning

Courses and Certificate Fees

Certificate AvailabilityCertificate Providing Authority
yesGreat LearningTexas McCombs

The Post Graduate Program in Artificial Intelligence and Machine Learning certification fees can be paid through net banking, or credit/debit card. You can also apply for an educational loan at 0% interest.

Post Graduate Program in Artificial Intelligence and Machine Learning  Fee

Mode of Enrolment


Total Fee


Eligibility Criteria

Educational Qualification

A bachelor’s degree in any discipline with a minimum of 50% marks is required. 

What you will learn

Machine learningKnowledge of PythonKnowledge of Artificial IntelligenceKnowledge of deep learning

By the end of the Post Graduate Program in Artificial Intelligence and Machine Learning certification syllabus, candidates will be able to have a comprehensive understanding of AI and ML concepts, algorithms, and technologies. He/She will also learn concepts of Deep Learning, Computer Vision, NLP, and Reinforcement Learning. Candidates will become job-ready with resume preparation, interview skills, and access to job opportunities from top companies. Candidates will get hands-on experience in implementing AI and ML projects, working with real-world datasets, and using popular tools and frameworks like TensorFlow, Keras, and OpenCV.

Who it is for

Post Graduate Program in Artificial Intelligence and Machine Learning classes are designed for individuals who are in the following fields:

Admission Details

Enrol in the Post Graduate Program in Artificial Intelligence and Machine Learning online course by following these simple steps:

Step 1: Access the course via: 

Step 2: Click on the “Apply Now” button.

Step 3: Fill up an application form.

Step 4: Submit the application and start by attending the demo class.

Step 5: You will receive an interview call for further processing. 

Step 6: An offer letter will be provided to the selected candidate.

Application Details

In this application form, the applicant needs to provide their basic information, including their name, mobile number, and email address. They need to specify their work experience and any programming experience. The applicant needs to agree to the Terms and Conditions and the Privacy Policy before proceeding.

After that, they need to share their education details for their undergraduate programme. The applicant is required to select their degree type from the available options and provide information about their college/university name, year of graduation, and their CGPA or percentage. These details are necessary to determine if they meet the eligibility criteria, which require a minimum of 50% in any undergraduate programme. Once completed, the applicant can submit the form.

The Syllabus

Module 1 - Introduction to Python

Python Basics 

  • Python Functions and Packages 
  • Working with Data Structures, Arrays, Vectors & Data Frames 
  • Jupyter Notebook – Installation & Function 
  • Pandas, NumPy, Matplotlib, Seaborn
Self Paced Module - EDA and Data Processing
  • Data Types
  • Dispersion & Skewness
  • Uni & Multivariate Analysis
  • Data Imputation
  • Identifying and Normalizing Outliers
Module 2 - Applied Statistics
  • Descriptive Statistics
  • Probability & Conditional Probability
  • Hypothesis Testing  
  • Inferential Statistics Probability Distributions

Module 1 - Supervised learning
  • Linear Regression
  • Multiple Variable Linear Regression
  • Logistic Regression
  • Naive Bayes Classifiers
  • k-NN Classification
  • Support Vector Machines
Module 2 - Ensemble Techniques
  • Decision Trees
  • Bagging
  • Random Forests
  • Boosting
Module 3 - Unsupervised learning
  • K-means Clustering
  • Hierarchical Clustering
  • Dimension Reduction-PCA
Module 4 - Featurisation, Model Selection & Tuning
  • Feature engineering
  • Model selection and tuning 
  • Model performance measures
  • Regularising Linear models
  • ML pipeline
  • Bootstrap sampling
  • Grid search CV
  • Randomized search CV
  • K fold cross-validation
Module 5 - Recommendation Systems
  • Introduction to Recommendation Systems
  • Popularity based model
  • Content based Recommendation System
  • Collaborative Filtering (User similarity & Item similarity)
  • Hybrid Models

Module 1 - Introduction to Neural Networks and Deep Learning
  • Introduction to Perceptron &
  • Neural Networks
  • Activation and Loss functions
  • Gradient Descent
  • Batch Normalization
  • TensorFlow & Keras for Neural Networks
  • Hyper Parameter Tuning
Module 2 - Computer Vision
  • Introduction to Convolutional Neural Networks 
  • Introduction to Images 
  • Convolution, Pooling, Padding & its Mechanisms 
  • Forward Propagation & Backpropagation for CNNs 
  • CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet 
  • Transfer Learning 
  • Object Detection 
  • Semantic Segmentation 
  • U-Net 
  • Face Recognition using Siamese Networks 
  • Instance Segmentation
Module 3 - NLP (Natural Language Processing)
  • Introduction to NLP • 
  • Stop Words • 
  • Tokenization • 
  • Stemming and Lemmatization 
  • Bag of Words Model 
  • Word Vectorizer 
  • TF-IDF  
  • POS Tagging  
  • Named Entity Recognition 
  • Introduction to Sequential data
  • RNNs and its Mechanisms 
  • Vanishing & Exploding gradients in RNNs 
  • LSTMs - Long short-term memory 
  • GRUs - Gated Recurrent Unit 
  • LSTMs Applications 
  • Time Series Analysis 
  • LSTMs with Attention Mechanism 
  • Neural Machine Translation 
  • Advanced Language Models: Transformers, BERT, XLNet
Self Paced Module - Introduction to Reinforcement Learning (RL)
  • RL Framework
  • Component of RL Framework
  • Examples of RL Systems
  • Types of RL Systems
  • Q-learning
Self Paced Module - Introduction to GANs (Generative Adversarial Networks)
  • Introduction to GANs
  • Generative Networks
  • Adversarial Networks
  • How do GANs work?
  • DCGANs - Deep Convolution GANs
  • Applications of GANs
Additional Module
  • Power BI
  • Cloud Computing 
  • Block Chain


Texas McCombs Frequently Asked Questions (FAQ's)

1: Are there any real-world projects included in the Post Graduate Program in Artificial Intelligence and Machine Learning online programme?

Yes, the programme includes 12+ hands-on projects that provide practical experience in implementing AI/ML solutions. 

2: Does the programme offer career support?

Yes, the programme provides career support with resume review, interview preparation, and access to a job board with job opportunities from 2800+ companies.

3: Will I receive a certificate upon completion of the programme?

Yes, participants who successfully complete the programme will receive a certificate from the institute.

4: Can I apply for financial aid or education loans?

Yes, the programme offers financial aid and collaborations with financial partners for education loans at a 0% interest rate to eligible candidates.

5: How do I apply for the Post Graduate Program in Artificial Intelligence and Machine Learning online programme?

Interested candidates can apply by filling out the online application form and providing the required information, including personal, educational, and professional details.

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