Understanding Machine Learning

4 Day Course
Hands On
Code QAIML

This course has been retired. Please view currently available Machine Learning.

Modules

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Introduction (7 topics)

  • Definition of Machine Learning (ML)
  • Origins of ML
  • Rule deduction (Expert Systems) vs induction (ML)
  • Why do we want machines to learn?
  • Supervised vs. unsupervised learning
  • Case studies
  • Regression as a classic example of ML

Data collection and preparation (12 topics)

  • Data selection
  • Data sampling
  • Data volume reduction
  • Removing ambiguities
  • Normalisation
  • Discretisation
  • Cleansing
  • Missing values
  • Outliers
  • Data and dimensional reduction
  • Data understanding
  • Generalisation of hierarchies

Introduction to ML in R (2 topics)

  • Introduction to R
  • Lab: ML with R

Creating or choosing an algorithm (17 topics)

  • Examples of creating algorithms
  • The use of data mining algorithms
  • Classes and examples of data mining/Machine Learning algorithms
  • Decision trees
  • Clustering
  • Segmentation
  • Association
  • Classification
  • Sequence analysis
  • Neural nets
  • History
  • Layers
  • Weights
  • Back propagation
  • Deep Learning
  • KNN
  • SVM

Training and test data (3 topics)

  • Selecting the training and testing data
  • Ratio of training to test data
  • How to make an unbiased selection

Testing and confusion matrices (5 topics)

  • Type 1, 2 and 3 errors
  • False positives vs False negatives
  • PCC
  • Classification models
  • Confusion matrices

ROC curves (2 topics)

  • Measuring efficiency
  • ROC space and ROC curves

Efficiency, Overfitting, Bias and Variance (3 topics)

  • More about efficiency
  • Overfitting
  • Bias and Variance

Combining data models (5 topics)

  • Ensemble
  • Boosting
  • Gradient boosting
  • Case study of combining models
  • Summary

Prerequisites

  • An understanding of data
  • A good logical mind
  • We do not expect people to have a background in mathematics

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