Course curriculum

  • 1

    Module 01: Course Introduction

    • Segment - 01 - Introduction
    • Segment - 02 - Get Started With the Python Data Science Environment: Anaconda
    • Segment - 03 - Anaconda for Mac Users
    • Segment - 04 - The iPython Environment
    • Segment - 05 - Why PyTorch?
    • Segment - 06 - Install PyTorch
    • Segment - 07 - Further Installation Instructions for Mac
    • Segment - 08 - Working With CoLabs
  • 2

    Module 02: Introduction to Python Data Science Packages

    • Segment - 09 - Python Packages for Data Science
    • Segment - 10 - Introduction to Numpy
    • Segment - 11 - Create Numpy Arrays
    • Segment - 12 - Numpy Operations
    • Segment - 13 - Numpy for Basic Vector Arithmetic
    • Segment - 14 - Numpy for Basic Matrix Arithmetic
    • Segment - 15 - PyTorch Basics: What Is a Tensor?
    • Segment - 16 - Explore PyTorch Tensors and Numpy Arrays
    • Segment - 17 - Some Basic PyTorch Tensor Operations
  • 3

    Module 03: Other Python Data Science Packages For Dealing With Data

    • Segment - 18 - Read in CSV Data
    • Segment - 19 - Read in Excel Data
    • Segment - 20 - Basic Data Exploration With Pandas
  • 4

    Module 04: Basic Statistical Analysis With PyTorch

    • Segment - 21 - Ordinary Least Squares (OLS) Regression - Theory
    • Segment - 22 - OLS Linear Regression - Without PyTorch
    • Segment - 23 - OLS Linear Regression From First Principles - Theory
    • Segment - 24 - OLS Linear Regression From First Principles - Without PyTorch
    • Segment - 25 - OLS Linear Regression From First Principles - With PyTorch
    • Segment - 26 - More OLS With PyTorch
    • Segment - 27 - Generalized Linear Models (GLMs) - Theory
    • Segment - 28 - Logistic Regression - Without PyTorch
    • Segment - 29 - Logistic Regression - With PyTorch
  • 5

    Module 05: Introduction to Artificial Neural Networks (ANN)

    • Segment - 30 - Introduction
    • Segment - 31 - PyTorch ANN Syntax
    • Segment - 32- What Are Activation Functions?
    • Segment - 33 - More on Back Propagation
    • Segment - 34 - Bringing them Together
    • Segment - 35 - Setting up ANN Analysis with PyTorch
    • Segment - 36 - DNN Analysis with PyTorch
    • Segment - 37 - More DNNs
    • Segment - 38 - DNNs For Identifying Credit Card Fraud
    • Segment - 39 - An Explanation of Accuracy Metrics
  • 6

    Module 06: Neural Networks on Images

    • Segment - 40 - What are Images?
    • Segment - 41 - Read in Images in Python
    • Segment - 42 - Basic Image Conversions
    • Segment - 43 - Why AI and Deep Learning?
    • Segment - 44 - Artificial Neural Networks (ANN) For Image Classification
    • Segment - 45 - Deep Neural Networks (DNN) For Image Classification
  • 7

    Module 07: Introduction to Artificial Intelligence (AI) and Deep Learning

    • Segment - 48 - What is CNN?
    • Segment - 50 - More on CNN
    • Segment - 51 - Introduction to Transfer Learning: Theory
    • Segment - 49 - Implementing CNN on Imaginary Data
    • Segment - 52 - Implement CNN Using a Pre-Trained Model