Course curriculum

  • 1

    Computer Vision Basics

    • L01 - Computer Vision

    • L02 - Edge Detection
  • 2

    Exploring the CNN architecture

    • L03 - Pooling and Padding

    • L04 - Convolutions
  • 3

    Special Convolution Layers

    • L05 - Resnets

    • L06 - 1*1 Convolutions

    • L07 - Inception Networks

    • L08 - Transfer Learning
  • 4

    Object Detection Techniques

    • L09 - Bounding box

    • L10 - YOLO

    • L11 - Face Recognition Techniques
  • 5

    Natural Language Processing

    • L12 - N-gram models

    • L13 - Word2Vec models

    • L14 - Topic Modeling
  • 6

    Introduction to Sequence Models

    • L15 - Types of architecture, Use cases

    • L16 - RNNs : Propagation through time
  • 7

    Advanced Sequence Models

    • L17 - Gated Recurrent Unit

    • L18 - Long Short Term Memory Networks

    • L19 - Bi-directional LSTM

    • L20 - Deep RNNs

    • L21 - Attention Mechanism

    • L22 - Speech Recognition
  • 8

    Unsupervised Neural Networks

    • L23 - Boltzmann Machines

    • L24 - Self Organizing Maps

    • L25 - AutoEncoders
  • 9

    Capstone Project

    • L26 - Face Recognition

    • L27 - Modeling Approaches
  • 10

    Get Job Ready

    • L28 - Writing Capstone Project in your Resume

    • L29 - Nailing your Data Science Interviews

    • L30 - One-on-One Mock Interview