Overview

  • Key Learnings

    Gain holistic understanding of deep learning algorithms such as CNN, RNN, LSTM, NLP models and their implementation in Python.

  • Capstone Project

    Complete an end-to-end Capstone Project in which you will solve a real-world problem statement using deep learning.

  • Job Guidance

    Become job ready with dedicated lectures on resume development, case studies, puzzles and one-on-one Mock Interviews.

Methodology

  • All classes will be one-to-one instructor-led live web sessions

  • Lecture materials and Jupyter notebooks will be shared with lectures on same day

  • Post completion of the course, you will receive a certificate of completion

  • This course will take 4 weeks to complete and end with one-on-one mock interview

Instructors

A team of IIT Kharagpur graduates & experienced data science professionals

Chief AI Faculty

Shivam Dutta

Helped set-up the data science division of Radware in India. Worked extensively on convolutional neural networks and reinforcement learning algorithms.

Chief Faculty of Data Science

Vikash Srivastava

Developed the entire FX options volatility prediction stack at HSBC using deep supervised LSTM networks. Possesses keen interest in Natural Language Processing.

Chief Data Scientist

Alok Anand

Worked extensively on hybrid fraud detection techniques at American Express. Built a state-of-the-art merchant recommender system being used globally.

Curriculum

  • 1

    Exemplifying the Deep Learning Paradigm

  • 2

    Neural Networks

    • L03 - Artificial Neural Network Architecture

    • L04 - Gradient Descent and Backpropagation
  • 3

    Building an ANN

    • L05 - Building Neural Network with Keras
  • 4

    Making Deep Networks More Powerful

    • L06 - Evaluating an ANN

    • L07 - Improving an ANN

    • L08 - Tuning an ANN
  • 5

    Convolutional Neural Networks

    • L09 - What is Convolutional Networks?

    • L11 - Building a CNN

    • L12 - Improving and Tuning CNN
  • 6

    Recurrent Neural Networks

    • L13 - Introduction to RNN

    • L14 - Vanishing Gradient Problem

    • L15 - Long Short Term Memory Neural Networks

    • L16 - Implementing RNN in Keras

    • L17 - Improving and Tuning RNN
  • 7

    Capstone Project

    • L18 - Defining the Problem Statement

    • L19 - Modeling Approaches
  • 8

    Get Job Ready

    • L20 - Writing Capstone Project in your Resume

    • L21 - Nailing your Data Science Interviews

    • L22 - One-on-One Mock Interview