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. 2,75,000

*EMI starting at ₹ 6,776/month only

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

Programming skillsKnowledge of Python

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

4.8/5(1 Reviews)

College Infrastructure: 5/5

Academics: 5/5

Placements: 5/5

Value for Money: 4/5

Campus Life: 5/5

IIIT delhi

Posted on 7 Mar'24 by Tushar Tanwani
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.

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