Certificate Programme in Machine Learning and Deep Learning (Batch 6)
Features
Programme Overview
Machine Learning (ML) and Deep Learning (DL) are groundbreaking branches of Artificial Intelligence (AI) that enable computers to learn and make decisions autonomously, without the need for explicit programming. These technologies are revolutionising a wide range of fields, from image and speech recognition to data analysis and pattern identification. Equip yourself with the skills to excel through our Certificate Programme in Machine Learning and Deep Learning (CPMLDL) and embrace the opportunities of tomorrow.
Machine Learning (ML) and Deep Learning (DL) are foundational components of Data Science and key subsets of Artificial Intelligence (AI). ML combines elements of computer science and statistics to identify patterns and make predictions from data, enabling systems to perform specific tasks without explicit programming. DL, a more advanced subset of ML, leverages Artificial Neural Networks (ANNs) that mimic human cognitive processes to analyse data with complex logical structures. These algorithms can learn in either a supervised or unsupervised manner, depending on the nature of the data provided. Reflecting their growing importance, the global market for ML is expected to surge from $17.1 billion in 2021 to $90.1 billion by 2026, while the DL market is forecasted to increase from $12.3 billion in 2021 to $60.5 billion by 2025.
Registration Closed
Class Schedule
Every Saturday and Sunday: 10 a.m. to 12 p.m.
6 months
80 hours live online sessions
20-30 hours assignments
20 hours (3 weeks) capstone projects
6 hours (1 day) optional campus immersion
4-6 hours masterclass on ChatGPT
Eligibility Criteria
Graduates/Post-graduates in Science, Technology, Engineering, or Mathematical Sciences
Programme Highlights
Covers ML/DL From Fundamentals To Advanced Concepts
Build And Train Neural Networks With Keras & Tensor Flow
Exclusive Masterclasses On Chat GPT
Optional 1 -day Immersive Campus Visit At IIT Delhi
80 Hours Of Live Online Learning
Expert-led Sessions From Global Faculty
Hands-on Learning With 9 Industry-focused ML/DL Tools
Assured Career Support And Guidance
Unique Bring Your Own Project (BYOP) Capstone
Programme Modules
Foundations of Python Programming
Functional Programming in Python
Data Structures, Loops, and Control Structures
Numerical Computations and Linear Algebra using NumPy
Data Pre-processing using Pandas
Data Visualisation using Matplotlib
Introduction to Scikit-learn
Linear Algebra: Vectors, Matrices, Norms, Subspaces, Projections, SVD, EVD, Derivatives of Matrices, Vector Derivative Identities, Least Squares
Optimisation: Constrained and Unconstrained Optimisation, Maxima and Minima, Convex and Non-Convex, Gradient and Hessian, Positive Definite and Semi-Definite, Second Derivative Test, Steepest Descent, Adam, AdaGrad, RMSProp, and KKT
Probability Theory: Discrete and Continuous Random Variables, Conditional Probability, Joint Probability Distribution, Multivariate, MAP Criterion, and ML Criterion
Differences Between Artificial Intelligence, Machine Learning, and Deep Learning
Differences Between Statistical Approach, Shallow Learning, and Deep Learning
Data Types and their properties
Attribute Types
General characteristics of datasets
Data Measurement Criteria: Precision, Bias, and Accuracy
Data Pre-processing Techniques
Distance-based Dissimilarities between Datasets
Machine Learning Problems: Classification, Regression, Interpolation, and Density Estimation
Linear Regression Model, Classification Model, and Classification Evaluation
Learning Algorithms: Supervised and Unsupervised
Bayesian Decision Theory: Bayesian Classifier, Discriminant Functions, and Minimum Error Rate Classification
Naïve Bayes Classifier
Logistic Regression Model and Parameter Estimation (Maximum-Likelihood)
Dimensionality Reduction Technique: Principal Component Analysis (PCA)
Non-parametric Techniques: K-Nearest Neighbour (KNN) and Density Estimation
K-means Clustering
Decision Tree (Entropy, Gini Impurity Index)
Support Vector Machine (SVM)
Random Forest, Ensemble Learning, Bagging, Boosting
Neurons, Perceptron Convergence Theorem, Relation Between the Perceptron and Bayes' Classifier, Batch Perceptron Algorithm, Adaptive Filtering Algorithm, Least Mean Square (LMS) Algorithm, Multilayer Perceptron, Feedforward Operation, Batch and On-line Learning, Activation Function, Backpropagation Algorithm, Rate of Learning, Stopping Criteria, XOR Problem, Loss Function, Bias and Variance, Regularisation, Cross-Validation, and Early-Stopping Criteria
Demonstration of All Machine Learning Algorithms for Classification and Regression Applications
Basics of Deep Learning
Convolutional Neural Network (CNN)
Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM)
Autoencoder (AE)
Generative Modelling
Attention and Transformer
Industry Use Cases and Applications of Computer Vision
Case Studies in Computer Vision
Latest Trends in Computer Vision
Latest Industry Use Cases and Applications of Speech Recognition
Case Studies in Speech Recognition
Latest Trends in Speech Recognition
Latest Industry Use Cases and Applications of NLP
Case Studies in NLP
Latest Trends in NLP
Gain hands-on expertise on ChatGPT
Bring Your Own Project
Learning Outcomes
Upon successful completion of this IIT Delhi Machine Learning and Deep Learning programme, students would:
Gain an understanding of efficient Python programming, including developing the skills to load and pre-process data from online and offline databases using pandas.
Develop a thorough understanding of the fundamental aspects and challenges of machine learning, such as data, model selection, and model complexity.
Identify the strengths and weaknesses of popular machine learning approaches.
Learn to design and train your own neural networks using Keras and TensorFlow modules.
Acquire the ability to design and implement various machine learning and deep learning techniques in a range of real-world applications
Tools
Python
Matplotlib
Pandas
Num Py
Tensor Flow
Scikit-learn
Spa Cy
Programme Coordinator
Prof. Manav Bhatnagar
Professor
Department of Electrical Engineering
Indian Institute of Technology Delhi
Prof. Manav Bhatnagar is currently a Professor with the Department of Electrical Engineering, IIT Delhi, New Delhi, India, where he is also a Brigadier, Bhopinder Singh Chair Professor. He holds a global rank of 517 in the area of Networking and Telecommunications and features are among the top 2% of scientists in a global list compiled by the prestigious Stanford University. He is a Fellow of IET, INAE, NASI, IETE, and OSI. He has received the prestigious NASI-Scopus Young Scientist Award, the Shri Om Prakash Bhasin Award, and the Dr. Vikram Sarabhai Research Award. He has been an Editor of the IEEE Transactions on Wireless Communications from 2011 to 2014. Currently, he is an Editor of the IEEE Transactions on Communications. He has published more than 100 high-quality IEEE journal papers, of which 10 are single-authored. His research interests include signal processing for MIMO systems, free-space optical communication, satellite communications, and machine learning.
Programme Faculty
PROF. TANMOY CHAKRABORTY
Associate Professor
Department of Electrical Engineering
Indian Institute of Technology Delhi
Prof. Tanmoy Chakraborty holds the position of Associate Professor of Electrical Engineering and Associate Faculty of the Yardi School of AI at IIT Delhi. Previously, he served as an Associate Professor of Computer Science at IIIT Delhi, where he also held the roles of head of the Infosys Centre for AI and Project Director of the Technology Innovation Hub. He leads the Laboratory for Computational Social Systems (LCS2), a research group specialising in Natural Language Processing, Computational Social Science, and Graph Mining. His current research primarily focuses on empowering frugal language models for applications such as mental health and Cyber-informatics. Tanmoy obtained his PhD from IIT Kharagpur in 2015 as a Google PhD scholar and worked as a postdoctoral researcher at the University of Maryland, College Park. Tanmoy has received numerous awards and honours, including the Ramanujan Fellowship, faculty awards/gifts/grants from industries like Facebook, Google, Accenture, LinkedIn, the PAKDD'22 Early Career Award, IEI Young Engineers Award, and the Paired Indo-German Early Career Award, and several faculty excellence awards. He is an ACM Distinguished Speaker and has authored two books: "Social Network Analysis'' (a textbook) and "Data Science for Fake News"
PROF. ANIRBAN DASGUPTA
Assistant Professor
Department of Electronics and Electrical Engineering
Indian Institute of Technology Guwahati
Prof. Anirban Dasgupta is an Assistant Professor in the Department of Electronics and Electrical Engineering at the Indian Institute of Technology (IIT) Guwahati. He received his doctorate (PhD) in Electrical Engineering from the Indian Institute of Technology Kharagpur in 2019, his Master of Science (MS) by research in Electrical Engineering from the Indian Institute of Technology Kharagpur in 2014, and his Bachelor of Technology (B.Tech.) in Electrical Engineering from the National Institute of Technology, Rourkela, in 2010. He was the co-founder of the start-up company 'Humosys Technologies Private Limited' and worked there as a Chief Technical Officer (CTO) from January 2019 to July 2021. In July 2021, he joined Boeing India Private Limited, Bengaluru, as a Data Scientist, and worked there till November 2021. From December 2021 onwards, he is associated with IIT Guwahati. He has ten publications in peer-reviewed international journals, which include five IEEE Transactions. He has also filed three Indian patents and published 16 IEEE conferences and one book chapter. His research areas include machine learning, the internet of things, digital signal and image processing for human cognition, and affective computing. He has served as a reviewer in more than 10 journals, which include IEEE Transactions on Signal Processing, IEEE Transactions on Image Processing, and IEEE Transactions on Pattern Recognition and Machine Learning
PROF. AASHISH MATHUR
Assistant Professor
Department of Electrical Engineering
Indian Institute of Technology Jodhpur
Prof. Aashish Mathur (Senior Member, IEEE) received the B.E. degree (Hons.) in Electronics and Instrumentation Engineering from the Birla Institute of Technology and Science, Pilani, Pilani, Rajasthan, India, in 2011, the MTech. degree in Telecommunication Technology and Management from IIT Delhi, New Delhi, India, in 2013, and the PhD degree in power line communications from the Department of Electrical Engineering, IIT Delhi. He was a Software Engineer with Intel Technology India Pvt. Ltd., Bangalore, India, briefly before joining IIT Delhi for his PhD in 2013. He is currently an Assistant Professor with the Department of Electrical Engineering, IIT Jodhpur, India. He has also worked as an Assistant Professor with the Department of Electrical and Electronics Engineering, BITS Pilani, Pilani Campus, and the Department of Electronics Engineering, IIT (BHU), Varanasi. He was engaged as a visiting faculty at the Indian Institute of Information Technology, Kota, India, for the 2nd Semester, 2018-19. He received the Best Student Paper Award for his co-authored paper at the 2017 Conference on Decision and Game Theory for Security (GameSec 2017), Vienna, Austria. He was awarded the Early Career Research Award by the Science and Engineering Research Board, DST, Government of India, in 2019. He was awarded the Teaching Excellence Award at IIT Jodhpur in 2019. He served as an Adjunct Faculty (part-time) from 2019–2022 on the 5G testbed project at IIT Delhi. He was recognised as an Exemplary Reviewer 2021 for IEEE Transactions on Communications. His research interests include power line communications, visible light communications, free-space optical communications, and physical layer security. He has published research papers in reputed IEEE journals and conferences. Some of his research works have appeared as popular articles in IEEE Communications Letters. He has also served as a reviewer for reputed IEEE journals and conferences
PROF. MANOJ B R
Assistant Professor
Department of Electronics and Electrical Engineering
Indian Institute of Technology Guwahati
Prof. Manoj B R is an Assistant Professor in the Department of Electronics and Electrical Engineering at the Indian Institute of Technology Guwahati, India. He received a B.E. degree in Electronics and Communication Engineering from the Visvesvaraya Technological University, India, in 2007, a MTech degree in Signal Processing from the Indian Institute of Technology Guwahati, in 2011, and a PhD in Wireless Communications from the Indian Institute of Technology Delhi, in 2019. He has gained a mixed exposure to academic and industrial backgrounds. Before joining IIT Guwahati, he was an Early Doctoral Research Fellow with the Indian Institute of Technology Delhi; a Postdoctoral Researcher with the Division of Communication Systems, Department of Electrical Engineering, Linköping University, Sweden; and a Senior Researcher with the Radio Transmission Technology Lab, Huawei Technologies, Stockholm, Sweden. His research interests include wireless communication and networks, machine learning, deep learning for wireless communications and signal processing, security and robustness of deep learning-based wireless systems, large-scale sensing using radio signals, buffer-aided relaying networks, Markov chains and their applications, diversity combining, and multi-hop communications.
DR. PRATITI PAUL
Dr. Pratiti Paul is the recipient of the Presidential Postdoctoral Fellowship and is currently working at Virginia Tech, Arlington, USA. Before joining Virginia Tech, she had worked as a Research Associate at the University of Edinburgh, UK. She received her Ph.D. from the Indian Institute of Technology, Delhi, in 2023. She has published multiple research papers in reputed peer-reviewed IEEE journals, magazines, and conferences. She is also serving as a technical reviewer for IEEE Transactions on Communication and the IETE Journal of Research. Her research interests include free-space optical communications, multiple-input multiple-output systems, radar signal detection, signal processing, physical layer security, and machine learning applications in wireless communications.
Programme Sample Certificate
Candidates who score at least 50% marks overall and have a minimum attendance of 50%, will receive a ‘Certificate of Successful Completion’ from CEP, IIT Delhi.
Candidates who score less than 50% marks overall and have a minimum attendance of 50%, will receive a ‘Certificate of Participation’ from CEP, IIT Delhi.
The organising department of this programme is the Department of Electrical Engineering, IIT Delhi.
Installment Schedule
Programme Fees: 1,69,000 + 18 % GST
Instalment | Date | Amount (₹)** |
Application Fee* | At the time of application | 1,000 GST @ 18% |
1st Instalment | Within 5 days of the offer rollout | 43,000 GST @ 18% |
2nd Instalment | 4th August, 2025 | 42,000 GST @ 18% |
3rd Instalment | 3rd September, 2025 | 42,000 GST @ 18% |
4th Instalment | 3rd October, 2025 | 42,000 GST @ 18% |
Note:
Application Fee of ₹1,000 is non-refundable and will not be adjusted in the total programme fee.
**GST will be additional as applicable.
Refund Policy
Candidates can withdraw within 15 days from the programme start date. A total of 80% of the total fee received will be refunded. However, the applicable tax amount paid will not be refunded on the paid amount.
Candidates withdrawing after 15 days from the start of the programme session will not be eligible for any refund.
If you wish to withdraw from the programme, you must email cepaccounts@admin.iitd.ac.in and icare@timespro.com, stating your intent to withdraw. The refund, if applicable, will be processed within 30 working days from the date of receiving the withdrawal request.
Programme Related Video
Testimonials
Frequently asked questions
The IIT Delhi Machine Learning and Deep Learning course is designed to provide candidates with a comprehensive understanding of the motivations, principles, methodologies, and applications of these technologies. It covers essential programming with Python, an introduction to data analytics, applied mathematics, and core modules and applications of machine learning and deep learning in various domains. The programme is designed to equip professionals with the skills and insights needed to navigate the dynamic landscape of ML and DL.
This Machine Learning and Deep Learning IIT Delhi course is suitable for graduates from science or engineering backgrounds seeking a career in the ML/DL domain and for professionals in the software and IT industry seeking to upskill with ML/DL expertise. This course is also suitable for professionals who aspire to work as data engineers, data scientists, machine learning engineers, etc.
Candidates must have a BE/B.Tech/ME/MTech/BIT/MIT/BCA/MCA/MCM (any stream), OR BSc/MSc/BS/MS in Mathematics, Statistics, Electronics, Physics, Computer Science, AI, DS to apply for this Machine Learning and Deep Learning IIT Delhi programme.
Secure your spot in the course by clicking the "Enrol Now” button and filling in your details. Our team will swiftly connect with you to provide all the information you need.
The course covers a range of topics such as Foundations of Python Programming
Functional Programming in Python, Data Structures, Loops, and Control Structures, Mathematical Foundations for Machine Learning, Artificial Intelligence Terminologies and Data Analysis, Neural Networks, Fundamentals of Deep Learning, Architectures and Recent Advances, Computer Vision, etc.
The teaching methodology for this IIT Delhi Machine Learning and Deep Learning course will be highly interactive, leveraging technology, and deploy diverse pedagogical tools and techniques, including lectures, project work, etc.
The IIT Delhi Certificate Programme in Machine Learning and Deep Learning is a live course that will be delivered in a direct-to-device (D2D) mode.
No, the candidates have to pay the registration fee when enrolling for this programme. The rest of the fees can be paid in three easy instalments.
The IIT Delhi Certificate Programme in Machine Learning and Deep Learning in India offers a comprehensive curriculum covering key concepts such as supervised and unsupervised learning, neural networks, natural language processing, computer vision and deep learning. With a focus on practical hands-on experience through projects and case studies, the program equips participants with the skills and knowledge required to excel in the rapidly growing fields of machine learning and deep learning. The certification from a prestigious institution like IIT Delhi adds significant value to one's professional profile, opening up opportunities in sectors like technology, finance, healthcare, and more. Overall, this program provides a solid foundation for individuals looking to pursue a career in artificial intelligence and data science.
IIT Delhi offers a cutting-edge Machine Learning and Deep Learning course that stands out due to its rigorous academic curriculum, top-notch faculty, and state-of-the-art research facilities. The program emphasises hands-on learning through practical projects and real-world applications, ensuring students gain practical skills alongside theoretical knowledge. Additionally, IIT Delhi's industry collaborations provide valuable networking opportunities and exposure to the latest trends in the field. By choosing IIT Delhi for this course, students can benefit from a holistic learning experience that equips them with the necessary expertise to excel in the rapidly evolving field of artificial intelligence.