Certificate Programme in Machine Learning and Deep Learning (Batch 5)

• 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

• 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

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

Autoencoder (AE)

• Deep Learning for Unsupervised Learning

• Architecture Design of AE

• Convolutional AE

• Training with Unlabelled Data

• Use Cases

• Python Demo in Denoising and 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

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 and Style Transfer

Subtopic 3 - Diffusion:

• Generative Modelling 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 Small Dataset

• Use Cases

• Python Demo on Transfer Learning in Computer Vision

Knowledge Distillation

• Optimisation 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 

• 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

• Masterclasses on ChatGPT

Bring Your Own Project

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 deploying 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.

Completing this certificate programme in Machine Learning and Deep Learning from IIT Delhi can offer several benefits:

• High-Quality Education: IIT Delhi is renowned for its academic excellence and rigorous curriculum. By enrolling in this certificate programme, you gain access to top-tier faculty members, cutting-edge research, and a comprehensive curriculum designed to impart in-depth knowledge of machine learning and deep learning concepts.

• Prestigious Credential: Obtaining a certificate from IIT Delhi carries significant weight and recognition in the industry. It serves as a validation of your expertise and commitment to mastering machine learning and deep learning techniques, enhancing your credibility and marketability to potential employers.

• Comprehensive Curriculum: The programme covers a wide range of topics relevant to machine learning and deep learning, including algorithms, neural networks, natural language processing, computer vision, and more. This comprehensive curriculum ensures that you acquire a strong foundation in both theoretical principles and practical applications of these technologies.

• Hands-On Experience: The programme likely includes practical exercises, projects, and case studies that provide hands-on experience with real-world datasets and tools used in machine learning and deep learning projects. This practical exposure helps reinforce theoretical concepts and equips you with the skills needed to tackle complex data analysis and modelling tasks.

• Networking Opportunities: IIT Delhi attracts talented students and industry professionals from diverse backgrounds. Participating in this certificate programme allows you to interact with peers, faculty members, and guest speakers who share a passion for machine learning and deep learning. These networking opportunities can lead to valuable collaborations, mentorship, and career connections in the field.

• Career Advancement: With the growing demand for professionals skilled in machine learning and deep learning, completing this certificate programme can significantly enhance your career prospects. Whether you are seeking to advance in your current role, transition to a new career path, or start your own venture in artificial intelligence, the knowledge and credentials gained from this programme can open doors to exciting opportunities in various industries.

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 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.