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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study, Virtual Classroom, Campus Based/Physical Classroom	Video and Text Based

Course Overview

The Certificate Programme in Machine Learning and Deep Learning course provides a comprehensive understanding of methodologies, applications, and principles of ML and DL. The course is a 6-month duration programme and is conducted in Direct-to-device mode. This course in collaboration with Timespro equips the participants with the required skills and insights of Artificial Intelligence (AI).

The course provides programming with Python, an introduction to data analytics, applied mathematics and applications of ML and DL. The Certificate Programme in Machine Learning and Deep Learning by IIT Delhi is designed for graduates of Science or Engineering and professionals working in the Software, and IT industry, and aspiring Data Engineers, Data Scientists, and ML Engineers.

Also Read: Online Machine Learning Courses & Certifications

The Highlights

6 Months Duration Course
E-certificate from CEP, IIT Delhi
76 Hours of Live Online Sessions
3 weeks Capstone Project
4 - 6 hours Masterclass on ChatGPT

Programme Offerings

live online sessions
Direct-to-Device Mode
One-Day Campus Immersion
career support

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IIT Delhi

The Certificate Programme in Machine Learning and Deep Learning total fee is Rs 1,99,420 with GST of 18%. The candidates can pay the course fee to the IITD CEP Account only.

Certificate Programme in Machine Learning and Deep Learning Fee Structure

Certification Course	Fees
Certificate Programme in Machine Learning and Deep Learning	Rs 1,69,000
GST@18%	Rs 30,420
Total Fees	Rs 1,99,420

Eligibility Criteria

Academic Qualifications

The eligibility criteria to join the Certificate Programme in Machine Learning and Deep Learning course is that the students must have pursued a B.E/B.Tech/M.E/M.Tech/BIT/MIT/BCA/MCA/MCM (any stream) or B.Sc/M.Sc/BS/MS in Mathematics, Statistics, Electronics, Physics, Computer Science, AI, and DS.

Certification Qualifying Details

To obtain a certificate, the candidates must gain at least 50% marks overall and have a minimum attendance of 50% to receive a ‘Certificate of Successful Completion.’ The candidates who score less than 50% marks overall and have a minimum attendance of 50%, will receive a ‘Certificate of Participation’.

What you will learn

Upon completion of the Certificate Programme in Machine Learning and Deep Learning syllabus, students will become efficient in Python programming and will be able to develop, load and pre-process the data from online to offline using various applications. They will also gain a deep understanding of various challenges in ML and its fundamental aspects.

After completing the Certificate Programme in Machine Learning and Deep Learning training, the participants will be able to apply various ML and DL techniques in real-world applications. They will also gain an in-depth understanding of popular ML approaches and will be able to design and train their neural networks using Keras and TensorFlow modules.

Who it is for

Data AnalystData ScientistMachine Learning EngineerDeep Learning Engineer

This certification course is designed for aspiring students and working professionals in the field of machine learning and deep learning to enhance their required knowledge and skills. This certification course is also beneficial for:

Admission Details

To join the Certificate Programme in Machine Learning and Deep Learning classes, follow the steps mentioned below:

Step 1: Browse the link mentioned below:

https://timespro.com/executive-education/iit-delhi-certificate-programme-in-machine-learning-and-deep-learning

Step 2: Click on the "Enroll Now" button, fill out the application form and submit it.

Step 3: Log in with the registered email ID or continue with Google.

Step 4: Select the course and register.

Step 5: The programme advisors will contact the participants and provide counselling.

Step 6: Submit the required documents and get them verified.

Step 7: Aspirants are required to attend the interview.

Step 8: Participants will obtain an offer letter and they are required to provide acceptance.

Step 9: Students can pay the preliminary course fee, complete onboarding and begin the course.

The Syllabus

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

Vectors, Matrices, Norms, Subspaces
Projections, SVD, EVD, Derivatives of Matrices
Vector Derivative Identities, Least Squares

Optimization

Constrained and Unconstrained Optimization
Maxima and Minima, Convex and Non-Convex
Gradient and Hessian, Positive Definite and Semi-Definite
Second Derivative Test, Steepest Descent
Adam, AdaGrad, RMSProp, KKT

Probability Theory

Discrete and Continuous Random Variables,
Conditional Probability, Joint Probability Distribution
Multivariate, MAP Criterion, 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, 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), 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 online learning
Activation Function, Backpropagation Algorithm, Rate of Learning
Stopping Criteria, XOR Problem, Loss Function, Bias and Variance
Regularization, Cross-Validation, Early-Stopping Criteria
Demonstration of All Machine Learning Algorithms for Classification and Regression Applications

Basics of Deep Learning

Importance of deep learning
Learning from large datasets
Types of data and architectures
End-to-end model design for feature learning and decision-making

Convolutional Neural Network (CNN)

Architecture design
Training methodology of CNN
Use cases
State-of-the-art CNN models
Python demo on object detection/image classification

Autoencoder (AE)

Deep learning for unsupervised learning
Architecture design of AE
Convolutional AE
Training with unlabeled data
Use cases
Python demo in denoising, dimensionality reduction

Generative Modelling

Subtopic 1 - Variational Autoencoder (VAE)
- Fundamentals of generative modeling
- Architecture of VAE
- Estimating data distribution
- Training methodology of VAE
- Use cases
- Python demo for image generation

Generative Modelling

Subtopic 2 - Generative Adversarial Network (GAN)
- Generative modeling as a game-theoretic approach
- Architecture design of GAN
- Training methodology of GAN
- Use cases
- Python demo on image generation, style transfer

Subtopic 3 - Diffusion
- Generative modeling through denoising
- Architecture design of diffusion models
- Training of diffusion models
- Python demo on high-quality image generation

Attention and Transformer

Attention mechanism
Advantages of Attention
Architecture design of Transformers
Training of Transformer
Python demo on language translation using Transformer

Special Topics

Transfer learning
- Leverage knowledge from one task to improve performance on another task
- Pre-training on large datasets
- Fine-tuning DL models on a small dataset
- Use cases
- Python demo on transfer learning in computer vision

Special Topics

Knowledge distillation
- Optimization of DL models
- Transfer knowledge from a complex teacher model to a simpler student model
- Training methodology for distillation
- Use cases
- Python demo on knowledge distillation in computer vision and natural language processing

Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM)

Modelling of time-series data
Architecture design of RNN
Training methodology of RNN
Architectures of LSTM and advantages over RNN
Use cases
Python demo on machine translation, stock prediction

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

Evaluation process

The students must score 50% marks in the MCQ-based exam, 20% marks in the assignments and quizzes, 20% marks in the Capstone Project and 10% in Attendance.

Instructors

IIT Delhi Frequently Asked Questions (FAQ's)

1: What is the duration of the Certificate Programme in Machine Learning and Deep Learning online course?

The duration of this certification programme is 6 months. The certification course provides 76 hours of live online sessions by IIT Delhi faculty and industry experts.

2: Is the Certificate Programme in Machine Learning and Deep Learning online course available on weekends?

Yes, the certificate course is available on weekends only. The timings of this course are Saturdays and Sundays from 10:00 a.m. to 12:00 p.m.

3: Is the Certificate Programme in Machine Learning and Deep Learning course fee refundable?

80% of the total fee is refunded (excluding the applicable tax amount) if candidates withdraw from the course within 15 days from the programme start date. Withdrawing after 15 days is not applicable.

4: How can I register for the Certificate Programme in Machine Learning and Deep Learning course?

Participants can register for this certification course by visiting the TimesPro website, clicking on the "Enrol Now” button, filling in the details and submitting. Students will get a callback for further processing.

5: Is there any interview conducted to join the Certificate Programme in Machine Learning and Deep Learning course?

The selection process of the candidates is based on the review of the application and an interview conducted by the faculty if required.