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38 Videos

  • 1

    Basic Regression and Decision Trees

    • 1 - Introduction

    • 2 - What is Machine Learning

    • 3 - Regression and Classification

    • 4 - More modern ideas

    • 5 - Supervised machine learning

    • 6 - Loss functions

    • 7 - Linear Regression

    • 8 - Implementation of Linear Regression

    • 9 - A real data scenario

    • 10 - Cleaning and Pre Propressing Data

    • 11 - Decision trees

    • 12 - Summary and next steps

    • 13 - Further reading
  • 2

    Classification

    • 1 - Intro

    • 2 - What is classification

    • 3 - Evaluating a classification model

    • 4 - ROC Curve

    • 5 - Area Under Curve

    • 6 - Logistic Regression

    • 7 - K nearest Neighbours

    • 8 - Pros and Cons of K nearest Neighbours

    • 9 - Summary
  • 3

    Introduction to Neural Networks

    • 1 - Intro

    • 2 - Classification and Regression

    • 3 - Two Classes

    • 4 - Multiclass Classification

    • 5 - Linearly Separable Data

    • 6 - Training a neural network in keras

    • 7 - A single node

    • 8 - Model compilation

    • 9 - Loss function

    • 10 - Backpropogation

    • 11 - Optimiser

    • 12 - Metrics

    • 13 - Model Training

    • 14 - Recap

    • 15 - Evaluating our model

    • 16 - Follow up resources

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