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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery | Frequency Of Classes |
|---|---|---|---|
| English | Self Study | Video and Text Based | Weekends |
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 Availability | Certificate Providing Authority |
|---|---|
| yes | Great 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 | Fee |
Total Fee | Rs.2,95,000+GST |
Starting at ₹ 7,319/month
Eligibility Criteria
Educational Qualification
A bachelor’s degree in any discipline with a minimum of 50% marks is required.
What you will learn
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: https://www.mygreatlearning.com/pg-program-artificial-intelligence-course
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
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
Applied Statistics
- Descriptive Statistics
- Probability & Conditional Probability
- Hypothesis Testing
- Inferential Statistics Probability Distributions
Supervised learning
- Linear Regression
- Multiple Variable Linear Regression
- Logistic Regression
- Naive Bayes Classifiers
- k-NN Classification
- Support Vector Machines
Ensemble Techniques
- Decision Trees
- Bagging
- Random Forests
- Boosting
Unsupervised learning
- K-means Clustering
- Hierarchical Clustering
- Dimension Reduction-PCA
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
Introduction to SQL
- Introduction to Databases and SQL
- Fetching data in SQL
- Filtering data in SQL
- SQL In-Built Functions (Numeric, Date, String)
- Aggregating data in SQL
- Joins
- Window Functions
- Subqueries
- Normalization
Neural Networks and Deep Learning
- Introduction to Neural Networks
- Multi-Layer Perceptron
- Activation and Loss Functions
- Gradient Descent and Backpropagation
- Optimizers
- Weight Initialization
- Regularization (Dropout, Batch Normalization)
- Deep Neural Networks
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
- YOLO, R-CNN, SSD
- Semantic Segmentation
- U-Net
- Face Recognition using Siamese Networks
- Instance Segmentation
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
Introduction to Data Science and AI
- 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
ML Ops
- Introduction to Model Deployment
- Model Serialization - Pickling
- Batch Mode and Flask
- Docker and Kubernetes
Recommendation Systems
- Intro to Recommendation Systems
- Market Basket Analysis
- Popularity-Based and Content-Based Recommendation Systems
- Collaborative Filtering
- Hybrid Recommendation Systems
Visualization using Tensor board
- Tensorboard
- Visualizing weights, bias, and gradients
- Occlusion experiment
- Saliency maps
- Neural style transfer
GANs (Generative Adversarial Networks)
- Introduction to GANs
- Working of GANs
- KL & JS Divergence
- Types of GANs
- Evaluating GANs
Time Series Forecasting
- Introduction to Time Series
- Forecasting models
- Exponential Smoothing
- Stationarity
- Autoregressive Models (ARMA, ARIMA, SARIMA)
Reinforcement Learning
- Introduction to Reinforcement Learning
- Reinforcement Learning Framework
- Q-Learning
- Exploration vs Exploitation
- SARSA Algorithm
Demystifying ChatGPT, Overview & Applications of Generative AI
- Introduction to Generative AI
- Discriminative AI vs Generative AI
- Introduction to Large Language Models
- Generative AI Demonstrations (Bing Images, ChatGPT)
- Overview of ChatGPT
- ChatGPT - Applications and Business
- Breaking Down ChatGPT
- Limitations and Beyond ChatGPT
ChatGPT: The Development Stack
- Demystifying Generative AI
- Overview of Natural Language Processing
- RNNs and LSTMs for NLP
- Transformers in NLP
- Large Language Models for Next Word Prediction
- Evolution of OpenAI GPT Models
- OpenAI GPT Models Training Process
- Recipe for High-Quality Chat Assistants
- Prompt Engineering vs Retrieval-Augmented Generation vs Fine-Tuning
- Hands-on ChatGPT Prototype Creation
Instructors
Texas McCombs Frequently Asked Questions (FAQ's)
Yes, the programme includes 12+ hands-on projects that provide practical experience in implementing AI/ML solutions.
Yes, the programme provides career support with resume review, interview preparation, and access to a job board with job opportunities from 2800+ companies.
Yes, participants who successfully complete the programme will receive a certificate from the institute.
Yes, the programme offers financial aid and collaborations with financial partners for education loans at a 0% interest rate to eligible candidates.
Interested candidates can apply by filling out the online application form and providing the required information, including personal, educational, and professional details.