Executive PG Program in Machine Learning & Artificial Intelligence
In association with
With Industry Experts From
Format
OnlineLearn about ChatGPT and the latest trends in AI
Advanced AI Lecture SeriesLearner base
85+ CountriesRecommended 15 hrs/week
13 MonthsStart Date
Coming SoonProgram Overview
Key Highlights
Eligibility:
Bachelor’s Degree with 50% or equivalent passing marks. Minimum 1 year of work experience in a technical domain or a degree in mathematics or Statistics with programming experience.
Top Subjects You Will Learn
Who Is This Program For?
Job Opportunity
Minimum Eligibility
Syllabus
Experience 600+ hours of best-in-class content developed by leading faculty & industry leaders in the form of videos, case studies & projects
20 Programming Tools, Languages & Libraries Covered
- Gain IIITB Alumni status & connect with a global network of accomplished IIITB alumni
- Build a strong network for life with opportunities to connect to Machine Learning Industry experts & your experienced fellow learners
Instructors
Learn from India's leading ML & AI faculty and industry leaders
Chandrashekar Ramanathan
S. Anand
Ujjyaini Mitra
Prof. S. Sadagopan
Anshuman Gupta
Prof. G. Srinivasaraghavan
Dinesh Babu Jayagopi
Kalpana Subbaramappa
Srinath Srinivasa
Mirza Rahim Baig
Chandramouleeswaran
Georgios Ouzounis
Arihant Jain
Usha Rengaraju
Snehansu Sekhar Sahu
Chiranjoy Chowdhuri
Sajan Kedia
Ankit Jain
Rohit Ghosh
Gunjan Narulkar
Syllabus
Experience 600+ hours of best-in-class content developed by leading faculty & industry leaders in the form of videos, case studies & projects
Stats and EDA
- EDA + Welcome
- Intro to Git & Github
- Lending club case study
- Inferential Stats
- Hypothesis Testing
Machine Learning I
- Linear Regression Module
- Linear Regression Assignment:Bike sharing company(demand regression)
- Logistic Regression
- Naive Bayes
- Model Selection
- Revision week - Skill test
Machine Learning - II
- Advanced Regression
- Advance regression assignment + SVM (Optional)
- Tree Models
- Random Forests + Practical Considerations
- Boosting - 1
- Unsupervised Clustering
- PCA
- Telecom Churn Case Study
- Buffer - Skill test
Deep Learning
- Introduction to Neural Networks - Part1
- Introduction to Neural Networks - Part2
- Convolutional Neural Networks
- Assignment: Convolutional Neural Networks
- CNN : Practical Considerations
- Recurrent Neural Networks
- Gesture Recognition - Project
- Exam on Sunday End of the week
- Revision week - Skill test (DL)
Natural Language Processing
- NLP - Lexical Processing
- NLP - Syntactic Processing
- NLP Assignment - Syntactic Processing
- NLP - Semantic Processing
- Ticket classification Project
- Buffer - Skill test(NLP)
ELective : MLOps Elective
- Pre-Requisite Module
- Introduction to MLops
- Designing Machine Learning Systems
- Experimenting with Data and Model using MLflow
- Automating and Orchestrating piepelines with Airflow
- Building continuous learning infrastructure
- MLOps Project
- Advanced NLP - Introduction to Attention Mechanism
- Advanced NLP - Introduction to Transformers
- Advanced CV - Object Detection & Semantic Segmentation
- MLOps + Deployment: DL (Theory)
- MLOps + Deployment: DL (Case Study)
- Exam on Sunday End of the week
- Revision week - Skill test()
Elective : Generative AI Elective
- Advanced NLP - Introduction to Attention Mechanism
- Advanced NLP - Introduction to Transformers
- "Fundamentals of Generative AI, ChatGPT & Prompt Engineering using Non Reasoning, Chain of Thought & Advanced Techniques"
- Product Development using OpenAI APIs, Fine Tuning using STaR technique in Python
- Integrating speech using Whisper API and application deployment using Flask
- Fundamentals of Multimodal LLMS like Stable Diffusion, Mid Journey
- "Fundamentals of Design, Photography & Product Development using Stable Diffusion in Python & Applications of LLMs for Data Science problems like clustering, classification"
- GenAI Project
- Applications of LLMs to create Embeddings for Large Documents
- Applications of Vectorstores like Pinecone to store and index embeddings of large documents
- Understand the importance of LangChain and it's applications
- Initialise the agents, tools and vectorstore for storage and retrieval
- Connect the components using Chain and explore the power of tools in LangChain
- Scale and Deploy Generative AI Apps using Azure OpenAI services
- Future Developments in Generative AI
Capstone
- Capstone
AI Mastery Syllabus
Unleashing the Power of AI: Exploring the Latest Trends in Generative Modelling
- Introduction to ChatGPT: The world of AI is constantly evolving, and staying up to date with the latest advancements can be challenging. This session will introduce you to the power and potential of GPT-3.
- Objective: To comprehensively understand GPT-3, its capabilities like Natural Language Understanding(NLU) & Context Recognition, and how it can be used in their work or business.
- Prompt engineering is a powerful technique that can help content creators, freelancers, and businesses generate high-quality text quickly and efficiently. This session will introduce you to the concept of prompt engineering and its applications.
- Objective: The objective of this session is to provide learners with a comprehensive understanding of prompt engineering and how it can be used with different AI tools like ChatGPT3, Midjourney & Stable Diffusion.
- By the end of the session, learners will be able to use prompt engineering techniques to generate high-quality text for a variety of applications.
Crack the Code of AI: Demystifying Model Explainability and Interpretability
- With the increasing adoption of AI, there is a growing need to ensure that AI models can be understood and trusted. This session will introduce you to the concept of Explainable AI and Model Interpretability.
- Objective: To gain a comprehensive understanding of the importance of XAI (Explainable Aritificial Intelligence) and model interpretability, and learn how to build and interpret an interpretable machine learning model using LIME & SHAP techniques
- As AI is increasingly used in various domains, it is important to consider the ethical implications and potential biases that can arise. This session will introduce learners to the concepts of ethics and bias in AI and provide real-world examples.
- Objective: To understand the ethical and bias considerations in AI and how to identify and mitigate potential issues. Learn through case studies in healthcare, finance, autonomous vehicles, recruitment among others.
Building Innovative Products with AI: Leveraging the Power of Generative Modelling
- This session will provide an overview of GPT models and their architecture, along with advanced techniques like RLHF and InstructGPT.
- Objective: The objective of this session is to introduce learners to one of the variant of the Transformer - the GPT model, specifically the Davinci variant. They will gain knowledge about the architecture of GPT models and advanced techniques such as Reinforcement Learning from Human Feedback (RLHF), one-shot & few shot learning. They will also learn how to fine-tune GPT models with prompts and evaluate their performance through hands-on exercises.
- Using transfer learning with GPT-3 eliminates the need to train NLP models from scratch, and provides access to state-of-the-art language models on various NLP tasks. This session will help you perform 'Ticket Classification Using ChatGPT'
- Objective: To build a product that can automatically classify support tickets into different categories based on their topics using the fine-tuned ChatGPT model. This will help companies and organizations to improve their support ticket handling efficiency and reduce the workload of their support teams.
- Using transfer learning with GPT-3 eliminates the need to train NLP models from scratch, and provides access to state-of-the-art language models on various NLP tasks. This session will help you 'Build an image generator using stable diffusion'
- Objective: The objective of this session is to teach learners how to build an image generator using Stable Diffusion and the Hugging Face library. They will learn how to prepare data, build and train a Stable Diffusion model, evaluate its performance, and build a Streamlit dashboard to showcase the model's output.
Innovations in Graph Networks, Computer Vision and Generative Modelling
- This session aims to simplify the process and provide learners with a comprehensive understanding of Graph Neural Networks (GNNs) and how to build a simple GNN model.
- Objective: The objective of this session is to introduce learners to the components of a GNN, its working mechanism, and how to build a simple GNN model for a given problem statement. By the end of the session, learners will be able to apply their knowledge to solve problems related to graph data.
- With the limitations of simple GNNs, there is a need to explore advanced GNN architectures. This session aims to provide an in-depth understanding of advanced graph networks.
- Objective: By the end of this session, learners should be able to comprehend the drawbacks of simple GNNs. Additionally, learners should be able to compare and evaluate the performance of advanced GNNs like Graph Convolutional Networks(GATs) & Graph Attention Networks(GANs) to simple GNNs.
- In this session, we will introduce learners to the concept of landmark and feature detection, and enable them to build a simple landmark detection system.
- Objective: By the end of the session, learners will be able to understand the fundamentals of landmark detection, popular feature detection algorithms such as SIFT and SURF, and build a landmark detection system using feature detection algorithms.
- Keeping up with the latest advancements in generative modeling can be challenging. This session aims to provide an overview of the state-of-the-art (STOA) architectures in generative modeling and their applications.
- Objective: By the end of the session, learners should have an understanding of the STOA architectures in generative modeling and be able to read research papers on topics such as image-to-text models (Vision Transformers), text-to-image models(CoCA, CLIP), and multimodality(Pathways).
Industry Projects
Telecom Churn Analysis
Solve the most crucial business problem for a leading telecom operator in India and southeast Asia - predicting customer churn.
Train an agent to play Tic Tac Toe
Learners will apply Q-Learning to train an RL agent to play the game of numerical Tic Tac Toe.
Classification of customer complaints
Create a solution that will help in identifying the type of complaint ticket raised by the customers of a multinational bank
Credit Card Fraud Detection
Build a machine learning model capable of detecting fraudulent transactions. Here you have to predict fraudulent credit card transactions with the help of machine learning models.
Melanoma Detection Assignment
Build a neural network from scratch in Tensorflow to identify the type of skin cancer from image.
Gesture Recognition
Make a Smart TV system which can control the TV with user’s hand gestures as the remote control.
News Recommender System
Build a model to using the concepts of natural language processing and recommender systems to recommend news stories to users on a popular news platform.
Maximizing profit of cab driver using RL
Learners will use the Markov Decision Process & Q-Learning to build an RL agent that learns to choose the best request so as to maximize the total profit earned by the agent that day.
Custom Entity detection in Healthcare data
You will build a custom NER to get the list of diseases and their treatment from a medical healthcare dataset.
Eye for the Blind
Build a model that can help any visually impaired person in understanding image present before them. It is a deep learning model which can explain the content of an image in the form of speech.You will build a custom NER to get the list of diseases and their treatment from a medical healthcare dataset.
Sentiment analysis based Product Recommender system
Build a sentiment analysis based product recommendation system to recommend the similar products to the users. Sentiment analysis is used to fine tune the product recommendation system.
Sales Forecasting
Predict the sales for a european pharma giant using a host of different types of variables. Apply VAR and VARMAX models to build the appropriate model.
Style Transfer using GAN's
Build a Model for converting MRI images from one type (T1) into other (T2) and vice versa. CycleGAN model is used for producing T2 type MRI images given T1 type input MRI images.
Machine Translation System
To build an attention based Encoder-Decoder architecture for translating between English to Hindi & vice versa.
Face Mask Detection
Create a custom object detector using the YOLO algorithm to detect the presence of face masks in the images of different people.
Customer Churn prediction
To create an ML system that can predict the propensity of customers cancelling the subscription plan.
Data Strategy project
Help in creating a Data / AI Strategy for an ecommerce company by creating their roadmaps, solutions for use cases & prioritization frameworks.
Lead Scoring project
To build a lead scoring ML system that can remove junk calls by categorising leads on the basis of propensity to purchase. The system built should be able to reduce the customer acquisition costs in the long run.
Benefits with upGrad
Learning Support
- Student Support available all day for your convenience (24*7)
- For urgent queries, use the Call Back option on the platform
Practical Learning and Networking
- Live Discussion forum for peer to peer doubt resolution monitored by technical experts
- Peer to peer networking opportunities with a alumni pool of 10000+
- Lab walkthroughs of industry-driven projects
- Weekly real-time doubt clearing sessions
Career Assistance
- Get a dedicated career coach after the program to help track your career goals, coach you on your profile, and support you during your career transition journey
- Obtain specific, AI powered inputs on your resume and Linkedin structure along with content on real time basis
- Get access to Industry Experts and discuss any queries before your interview
- Career bootcamps to refresh your technical concepts and improve your soft skills
Our Learners Work At
Student Reviews
Saurav Kant Kumar
Amarpal Singh
Shashank Sane
Gaurav Singh
Admission Process
Step 1
Online Eligibility Test
Fill out an application and take a quick 40-minute online test with 18 questions to assess your aptitude.10 questions from mathematics, aptitude & reasoning & 8 questions from programming.
Step 2
Review & Shortlisting of Suitable Candidates
Our faculty will review all applications, considering the educational & professional background of an applicant, review the test scores where applicable. Following this, Offer Letters will be rolled out so you are assured a great peer group to learn & network with.
Step 3
Enrollment for Access to Prep Content
Make a quick block payment with assistance from our loan partners where required, receive immediate access to the prep content and begin your upGrad journey.
Program Fees
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Frequently Asked Questions
Program Details
How do I know if this program is for me?
This program is for you if you are a:
Data Scientist or Senior Data Analyst: If you are comfortable with data wrangling, have implemented statistical or machine learning models in the past, or if you have any prior experience as a working professional. You should also have some experience with R/Python/Scala.
Statistician: If you received formal education in statistics or mathematics or if you have any prior working experience.
Data Engineer/Big Data Engineer: If you have any prior experience in creating data pipelines/handling data warehouses and you have created ETL procedures. In addition, you should be familiar with various frameworks and tools like Hadoop and Spark.
Software Developer: If you have prior experience in creating and deploying software/apps, you are comfortable with object-oriented programming and know C/C++/Java/Python or a similar language.
How will this program benefit me?
The program will benefit you in different ways depending on your prior experiences:
Data Scientist or Senior Data Analyst: The program will familiarise you with the advancements in ML and AI. It will also help you understand the mathematics behind algorithms and how you can modify them to suit your needs so that you can transition to a Senior Data Science or Machine Learning role.
Statistician/Mathematician: Apart from familiarising you with the advancements in ML and AI, the program will help you understand how to define a structured approach to solve a business problem, and equip you with the right tools so that you can transition to a Machine Learning or Data Science role.
Data Engineer/Big Data Engineer: The program will set up a solid foundation of Statistics, Machine Learning, and AI along with problem-solving skills so that you can solve enterprise-level problems. It will build upon your existing knowledge of various tools to make you a Full-Stack Machine Learning or Data Science professional.
Software Developer: The program will help you create a strong foundation of Statistics, Machine Learning, and Business Understanding. It will leverage your existing knowledge of programming and expand the technologies you are familiar with so that you can become a well-rounded Machine Learning professional.
What is this program intended to do?
This program intends to produce extremely well-rounded data scientists and AI professionals with deep knowledge of mathematics, expertise in relevant tools/languages, and understanding of cutting-edge algorithms and applications.
What can I expect out of this program?
This programme is designed for working professionals looking to pick up skills in advanced concepts like Reinforcement Learning, Graphical Models, NLP, Deep Learning along with a solid foundation of Statistics. This programme demands consistent work and time commitment over the entire duration of 13 months.
What should I NOT expect from this program?
This programme is NOT intended to serve as an entry point to the field of Data Science. It is aimed at developing professionals who can be absorbed into Senior Data Scientist and Machine Learning/AI roles. Our Executive PG Programme in Machine Learning and AI is aimed at facilitating the transition of professionals to the Machine Learning field.
Is there any certification at the end of the program?
Post completion of the program, a Executive PG Program in Machine Learning & Artificial Intelligence will be granted from IIIT Bangalore.
Support
What type of learning should I expect?
Refund Policy
Is there any deferral or refund policy for this program?
Selection Criteria
What is the selection process for this program?
STEP-1: Online Eligibility Test
Fill out an application and take a quick 40-minute online test with 18 questions to assess your aptitude.10 questions from mathematics, aptitude & reasoning & 8 questions from programming.
STEP-2: Review and Shortlisting of Suitable Candidates
Our faculty will review all applications, considering the educational and professional background of an applicant and review the test scores where applicable. Following this, Offer Letters will be rolled out so you are assured a great peer group to learn and network with.
STEP-3: Enrollment for Access to Prep Content
Make a quick block payment with assistance from our loan partners where required, receive immediate access to the prep content and begin your upGrad journey.
Disclaimer
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**If we are unavailable to attend to your call, it is deemed that we have your consent to contact you in response.
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Write to us at globaladmissions@upgrad.com
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*All telephone calls will be recorded for training and quality purposes.
*If we are unavailable to attend to your call, it is deemed that we have your consent to contact you in response.