Live Sessions

Artificial Intelligence and Deep Learning

Work with neural networks and GenAI using TensorFlow, PyTorch, and Transformers.

Tools you’ll work with
TensorFlow
PyTorch
OpenCV
Gensim
₹75000
32 weeksAdvanced
Instructor
Neha Kapoor
PhD in Artificial Intelligence, IIT Delhi • 8+ years • Ex - IBM Research
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Course Overview

Description

25 lessons•57 exercises•4 exams•~12 hours

Are you looking for a well-structured data science fundamentals course?

Do you want to gain a clear understanding of the data science field?

This is the perfect course for you.

If terms like traditional data, big data, business intelligence, and machine learning sound confusing, this course will help you understand both meaning and practical application.

25 lessons•57 exercises•4 exams•12 hours

Course Curriculum

A structured, progressive curriculum designed to build depth, intuition, and real-world proficiency over time.

This section introduces key concepts and builds intuition through structured lessons and exercises.

Introduction to AI, ML, DL & real-world applications
Python refresh for DL (NumPy, Pandas, Matplotlib)
Linear algebra for DL (vectors, matrices, eigen concepts)
Probability & statistics basics for neural networks
Data preprocessing, normalization & train-test splits

This section introduces key concepts and builds intuition through structured lessons and exercises.

Perceptron & multi-layer neural networks
Activation functions & weight initialization
Loss functions & optimization objectives
Gradient Descent variants (Batch, SGD, Adam)
Backpropagation theory & implementation
Regularization techniques (L1, L2, Dropout, BatchNorm)

This section introduces key concepts and builds intuition through structured lessons and exercises.

Introduction to TensorFlow & Keras
Building deep models using TensorFlow
PyTorch fundamentals & tensors
Model training loops & custom loss functions
GPU acceleration & performance optimization
Model debugging & best practices

This section introduces key concepts and builds intuition through structured lessons and exercises.

Image fundamentals & OpenCV basics
Convolution Neural Networks (CNN architecture)
Popular CNN models (LeNet, AlexNet, VGG, ResNet)
Image classification & object detection
Transfer learning & fine-tuning models

This section introduces key concepts and builds intuition through structured lessons and exercises.

Text preprocessing & NLP fundamentals
Word embeddings (Word2Vec, GloVe)
Recurrent Neural Networks (RNN, LSTM, GRU)
Sequence modeling & text classification
Final deployment concepts & project presentation

How You’ll Learn

This course is designed to help you move beyond tutorials — toward deep understanding, confident implementation, and long-term career growth.

Live Classes
Quizzes
Assignments
Projects
Certification

Build Skills That Scale With You

This course is designed to help you move beyond tutorials — toward deep understanding, confident implementation, and long-term career growth.

Learn deeply. Apply repeatedly.