Linear equations and solutions Matrices and their Properties; Eigenvalues and eigenvectors; Matrix Factorizations; Inner products; Distance measures; Projections; Notion of hyperplanes; halfplanes.
Probability theory and axioms; Random variables; Probability distributions and density functions ;Expectations and moments; Covariance and correlation; Statistics and sampling distributions; Hypothesis testing of means, proportions, variances and correlations; Confidence intervals; Correlation functions; Parameter estimation – MLE and Bayesian methods
Unconstrained optimization; Necessary and Sufficiency conditions for optima; Gradient descent methods; constrained optimization, KKTConditions; Introduction to least squares optimization;
Learning Outcomes: By completing this module, you will gain a solid foundation in Python programming and key concepts in probability and statistics for data-driven decisions. You'll also learn basic calculus to understand change and trends, along with optimization techniques to solve real-world problems and enhance performance.
Learning Outcomes: In this module, you’ll explore core machine learning algorithms like linear regression, decision trees, and k-nearest neighbors. You’ll learn to train, evaluate, and apply these models effectively, and develop the skill to choose the right algorithm based on the problem and data.
1. Use cases from the healthcare domain where NLP is applied
2. Models such as Bi-LSTM-CRF, CAML, HAN, ResNexT.
3. Public domain datasets - MIMIC-III.
1. Levels of omics data, basic information flow in biology
2. Genomics, Transcriptomics, Epigenomics, Proteomics and Multi omics - Identification human disease genes using genomics
3. Application of transcriptomics for identifying disease mechanisms
4. Clinical data - kinds of clinical data Garbhini dataset - a clinical data case study
Learning Outcomes: In this module, you'll explore how machine learning drives innovation in industries like healthcare, finance, and marketing. You'll learn to analyze real-world use cases, understand the ML workflow, and identify problems where ML can create practical impact.
a. Artificial Neuron
b. Multilayer Perceptron
c. Universal Approximation Theorem
d. Backpropagation in MLPs
e. Backprop on general graphs
a. Gradient Descent and its variants
b. Momentum, Adam, etc.
c. Batch Normalization
a. Introduction
b. CNN Operations
c. CNN Training
d. Illustrative Example (“Hello World”) - MNIST digit classification e. Image Recognition-SoTA model(s)
f. Object detection/localization - SoTA model(s)
g. Semantic segmentation -SoTA model(s)
Learning Outcomes: At the end of this module you will gain knowledge about foundation in deep learning, from understanding neural networks to building and training models using TensorFlow or PyTorch. Learn to apply these techniques to real-world tasks like image classification, NLP, and explore their role in powering Generative AI.
a. Smart Cities
b. Industry Use case 1
c. Climate Science
d. Manufacturing
e. Bio-informatics
Industry Use case 2
Learning Outcomes: At the end of this module, you'll understand how deep learning powers real-world applications like image recognition, speech processing, and recommendation systems. You'll also learn to select the right architectures for different problems and tackle key deployment challenges.
Dr. Balaji Ph.D, Stanford University, USA
Research Expertise : Fluid Dynamics, Turbulence in compressible and hypersonic flows, Computation of rarefied flows, Numerical Analysis and High-Performance Computing.
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Dr. Balaraman Ph.D., Computer Science, University of Massachusetts, Amherst, USA
Research Expertise : Reinforcement Learning, Geometric Deep Learning, Data Mining
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Dr. Nandan Ph.D., Engineering Systems, MIT, Cambridge, MA, USA
Research Expertise : Experimentation, Applied Statistics and Machine Learning
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Dr. Nirav P. Ph.D., Computer, Communication and Information Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Research Expertise : Machine Learning for Biological and Engineering Networks, Safe Reinforcement Learning
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Dr. Karthik Ph. D., Systems Biology, IISc
Research Expertise : Biological Networks, Computational Systems Biology, Genomics and Computational Biology
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Dr. Gitakrishnan Ph.D. ,Transportation Engineering, Rensselaer Polytechnic Institute, Troy, NY
Research Expertise : Network Modeling, Dynamic Traffic Simulation and Assignment...
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Prof. Himanshu Ph.D., Cambridge University, UK
Research Expertise : System Genetics, Multiomic Analysis of Genotype & Phenotype Relationship, Healthcare Data Science & AI
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Dr. Arun Ph.D., IISc Bangalore
Research Expertise : Machine Learning, Rank Aggregation, Statistical Learning
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Dr. Chandrashekar Ph.d., IISc Bangalore
Research Expertise : Deep Learning, Reinforcement Learning, Stochastic Approximation and Large Scale Markov Decision Processes
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Dr. Harish G. Ph.D, IISc Bangalore
Research Expertise : Machine Learning, Learning Theory and Optimisation
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Prof. Ganapathy Ph.D., Purdue University, USA
Research Expertise : Multi-Modal Pre-Clinical Imaging System, Medical Image Processing
Know More| Features | ADSML Programme by IITM Pravartak | Other Data Science Courses |
|---|---|---|
| Duration | 12 Months of executive friendly programme | Short term or self paced format |
| Curriculum | Practical curriculum with strong industry exposure designed by IIT Madras Faculties | Lacks Industry alignment with no scope for practical exposure |
| Faculty | Taught by 12+ distinguished faculty having 300+ years of collective experience | Only one faculty throughout that too for few masterclasses |
| Tools Exposure | Get access to 30+ most in demand tools and libraries like Phyton, Tensorflow, Fast API, Microsoft Azure and many more | Limited tools exposure |
| Project Based Learning | 30 Mini Projects, assignment & 1 Capstone Project |
No real world project exposure |
| Format | 100% Live Interactive Sessions 100% Live engagement with faculty |
Mostly pre-recorded videos with few live classes Minimal engagement from faculty |
| Campus Visit | 2 days of campus visit at IIT Madras | Campus visits are optional and limited to a single day |
| Certification | Certification from IITM Pravartak and WSAI at IIT Madras | Certification from institutions with limited recognition |
| Alumni Network | Become part of growing ADSML alumni network & 250K TalentSprint network | No Alumni or community support |
| Fee | Priced for 12 months of learning, offering much greater value | Shorter courses often come at a higher cost |
**Dates will be decided keeping the safety of participants in mind. Fees will be based on actuals.
Note: These are edited versions based on the details submitted by various programme applicants.
All logos belongs to respective companies
The selection for the programme will be done by IITM Pravartak and WSAI at IIT Madras and is strictly based on the eligibility criteria
and the motivation of applicants as expressed in their statement of purpose.
Application Fee*
₹2,000
Program Fee* ₹2,50,000
Programme Fee with Scholarship* ₹1,87,500
(*18% GST as applicable)
*Fees paid is non-refundable and non-transferable.
Campus visit fee to be borne by participants. Will be based on actuals.
Special Program Fee for Corporate Nominations**
**Applicable only for enterprises nominating their employees as a group
Modes of payment available
Internet Banking
Credit/Debit Card
UPI PaymentsEMI as low as ₹7,943/Month
Indian Institute of Technology Madras (IIT Madras) is globally recognized for excellence in technical education, basic and applied research, innovation, entrepreneurship and industrial consultancy. Founded in 1959 with technical and financial assistance from the former government of West Germany, IIT Madras has been the top-ranked engineering institute in India for four consecutive years as well as the ‘Best Educational Institution’ in Overall Category in the NIRF Rankings by the Ministry of Human Resource Development. For more information visit www.iitm.ac.in
IITM Pravartak Technologies Foundation, a Section 8 company hosted by IIT Madras and funded by the Department of Science and Technology, Government of India, drives innovation through the Technology Innovation Hub on Sensors, Networking, Actuators, and Control Systems (SNACS) under the National Mission on Interdisciplinary Cyber-Physical Systems (NM-ICPS). Focused on advancing Cyber-Physical Systems (CPS), which integrate digital and physical elements for data-driven decision-making, IITM Pravartak fosters cutting-edge research, technology development, entrepreneurship, and human resource development while collaborating with industry, academia, government, and international organizations to translate research into impactful products.
The 12-month online PG Level Advanced Certification Programme in Applied Data Science and Machine Learning, offered by IITM Pravartak and Wadhwani School of Data Science & AI (WSAI) at IIT Madras in collaboration with TalentSprint, is a cutting-edge course designed to enable learners to build deep tech capabilities and make data-driven business decisions. With the massive amount of data generated daily from millions of devices, Applied Data Science has become a crucial field in today's world. The program offers a unique learning experience that combines masterclass lectures, hands-on labs, hackathons, workshops, industry interactions, and a campus visit to fast-track learning.
The Data Science course covers various topics such as data preprocessing, machine learning algorithms, deep learning models, and natural language processing, among others, to prepare learners to handle complex data sets and provide data-driven solutions. The curriculum is designed to meet industry standards and includes real-world case studies, ensuring learners have hands-on experience working with industry-relevant tools and technologies. The course also enables learners to acquire soft skills such as communication, teamwork, and leadership, which are essential for success in the workplace.
Participants of the course benefit from the expertise of leading faculty members and industry experts, who provide personalized guidance and mentorship throughout the course. Upon completion of the course, learners receive a PG Level Advanced Certification in Applied Data Science and Machine Learning from IITM Pravartak and WSAI (IIT Madras), which enhances their career prospects and enables them to become sought-after professionals in the data science and machine learning field.
It is well known that Data Science helps businesses extract actionable insights from massive sets of data. Data Science and the technologies empowering it, like AI and Machine Learning, have become central to every business strategy today.
However, Applied Data Science takes the game many notches higher. It broadens the scope of data science to include
Applied Data Science becomes important in the backdrop of the fact that the global data created per day is likely to reach 394 zettabytes per day by 2028. Such massive data cannot be made sense of by traditional algorithms.
This is where Machine Intelligence (MI) can add immense value to business in conjunction with Applied Data Science.
Machine Intelligence as a higher evolution of machine learning - a stepping stone to true AI.
According to LinkedIn, Applied Data Science and Machine Learning offers exciting job opportunities for professionals with expertise in these fields.
The IITM Pravartak Advantage
Cutting-edge Applied Learning
The TalentSprint Advantage
The programme will be delivered in an interactive online format, retaining effectiveness while maintaining safety. The format uniquely combines the benefits of an in-class programme with the flexibility and safety of online learning.
The programme will be delivered on TalentSprint's iPearl.ai, a leading digital learning platform of choice used for programs delivered by the likes of Google, IIM Calcutta, IIT Hyderabad, IISc, and IIT Kanpur, to name a few.
S SangeethDigital Team Lead
Annu PandeyAccenture
Debnita KarDelivery Manager
Abhijit SanyalData Scientist
Abhishek ChavanData Engineer