Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based |
Course Overview
Post Graduate Program in Artificial Intelligence & Machine Learning which is widely recognised for being a centre of research and quality education in IT interdisciplinary areas. They shall receive proper guidance throughout their learning journey and avail themselves of dedicated career support. The programme has been designed to deliver a comprehensive learning experience.
Post Graduate Program in Artificial Intelligence & Machine Learning Syllabus holistically covers algorithms while creating a solid foundation in Computer Science. This online training program can be pursued by both students and working professionals to upskill their knowledge in this domain.
The Highlights
- 12 months programme
- Opportunity to attend GL Confluence
- Online learning live program
- Imparted in collaboration with Great Learning
Programme Offerings
- live online classes
- hackathons
- course material
- Q&A forums
- Networking events
- Capstone Project
- Programme manager
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | University of Texas, Austin |
Post Graduate Program in Artificial Intelligence & Machine Learning Fee information is specified below-
| Head | Amount |
| Program fees | Rs. 2,95,000 |
Eligibility Criteria
Work experience
Candidates of Post Graduate Program in Artificial Intelligence & Machine Learning must have experience with programming in at least one programming language.
Education
Interested participants of Post Graduate Program in Artificial Intelligence & Machine Learning Certification should have graduated (B.Sc/BCA in Mathematics/CS/Statistics/Electronics/IT, B.E/B.Tech in any discipline, or a related stream, BBA, Bachelors in Economics/Bioinformatics/CS/IT, MBBS or other areas with a quantitative component) with a minimum of 50% marks or equivalent CGPA. Postgraduates in the above categories with the requisite marks are also eligible to apply.
After fulfilling the above, learners need to appear for an online aptitude (quant) cum programming screening test and score 50% or more to get selected for this course.
Certification qualifying details
Post Graduate Program in Artificial Intelligence & Machine Learning will be issued to all successful learners who abide by the programme requirements.
What you will learn
Post Graduate Program in Artificial Intelligence & Machine Learning Online Course will highlight the following concepts-
- The course works on candidates’ ability to apply the knowledge of AI towards innovative practical applications
- They will be brushing up skills on understanding computer science foundations
- Learners shall be utilising their conceptual understanding to solve practical applications of CS
- IIIT-Delhi: Post Graduate Diploma in Computer Science and Artificial Intelligence Training will allow candidates to apply aspects of Artificial Intelligence into different domains
- The syllabus provides an introduction to the applied and foundational aspects of Artificial Intelligence
Who it is for
Post Graduate Program in Artificial Intelligence & Machine Learning will aid the following learners-
- Learners acquainted with the concepts of programming
- Learners seeking professional opportunities to leverage the demand for qualified AI professionals
- Fresh graduates willing to build a foundation in Computer Science
- Career professionals seeking a boost in a career in Artificial Intelligence
Admission Details
Learners can apply for Post Graduate Program in Artificial Intelligence & Machine Learning Online Course via the given guide-
Step 1: Browse https://www.mygreatlearning.com/pg-program-artificial-intelligence-course and tap “Apply Now.”
Step 2: Fill the application form and select “Submit Application.”
Step 3: The candidates have to fill the form with basic information and professional details. These details will help the officials in evaluating the profile.
Step 4: On selection, candidates will be required to make the payment and confirm their admission.
Application Details
The application form requires learners to enter their name, address, city, and email ID under basic information. They also have to specify work experience, last company, years of programming experience that they have, preferred programming language, industry, and job title. They further need to name their degree and area of specialisation, year of graduation, and percentage. Statement of Purpose will also be a part of the application.
The Syllabus
Introduction to Python
- Python Programming Fundamentals
- Data Manipulation and Analysis with NumPy and Pandas
- Data Visualization with Seaborn and Matplotlib
- Exploratory Data Analysis
- Data Preprocessing
Applied Statistics
- Introduction to Probability
- Probability Distributions (Binomial Distribution, Normal Distribution, Uniform Distribution)
- Sampling
- Central Limit Theorem
- Point Estimation and Confidence Intervals
- Introduction to Hypothesis Testing (Null and Alternative hypothesis, p-value, One-tailed and Two-tailed Tests)
- Common Statistical Tests (z-test, t-test, Chi-square Test of Independence, ANOVA)
Supervised Learning
- Simple Linear Regression
- Multiple Linear Regression
- Logistic Regression
- KNN Classifier
- Naive Bayes Classifier
- Support Vector Machines
Ensemble Techniques
- Decision Trees
- Random Forests
- Bagging
- Boosting (AdaBoost, Gradient Boosting, XGBoost)
Unsupervised Learning
- K-means Clustering
- Hierarchical Clustering
- Introduction to Dimensionality Reduction
- PCA
Featurization, Model Selection & Tuning
- Feature Engineering
- K-fold Cross-Validation
- Oversampling and Undersampling
- Regularization
- Data Leakage
- Hyperparameter Tuning
- GridSearchCV and RandomizedSearchCV
- ML Pipeline
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 Computer Vision
- Image Representation and Processing
- Convolutional Neural Networks
- Transfer Learning and Common CNN Architectures
- Regularization in CNNs
- Object Detection
- Image Segmentation
Natural Language Processing & Generative AI
- Introduction to Natural Language Processing
- Text Processing
- Sentiment Analysis
- Word Embeddings (Word2Vec, GloVe)
- Semantic Search
- Introduction to Attention Mechanism and Transformers
- Different Transformer Architectures
- Large Language Models
- Generative AI
- Prompt Engineering
- Broad Strategies for Prompt Design (Template-based prompts, Instructional prompts, Iterative prompts, Chain-of-thought prompts, Ethically-aware prompts)
- Hugging Face and Open-source LLMs
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
Student Reviews for Post Graduate Program in Artificial Intelligence & Machine Learning
IIIT delhi
Batch of 2024-01-01 | Post Graduate Program in Artificial Intelligence & Machine Learning
College Infrastructure
IIIt Delhi has a beautiful campus covered with lush green grounds with all the necessary facilities like sports grounds, gyms, hostels, mess, libraries, etc. Living spaces are also cleaned regularly to maintain hygiene.
Instructors
IIIT Delhi Frequently Asked Questions (FAQ's)
Candidates who pass the eligibility test will have to send their acceptance and pay the fee.
Candidates can avail of EMIs starting at Rs. 7,319. They can also avail education loans at a 0% interest rate.
Hiring partners include Microsoft, Amazon, Yahoo!, Google, Accenture, Infosys and Cognizant.
The certification will be in online (digital) format.