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Business Intelligence Course

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Overview

Machine Learning is a well understood process. We typically start with some existing data and pass it through an algorithm. The algorithm 'learns' from that specific data and produces a 'data model'. This model has learnt from the data and now encapsulates information derived from the raw data. We then have to test the model (to see how good it is) and try to incrementally improve it. Finally, we evaluate the finished model and deploy it.

Audience

Each participant will leave with a recognised certificate

Outline

Module 1: Introduction

  • This module introduces the background to Machine Learning.
  • Definition of Machine Learning (ML)
  • Origins of ML
  • Rule deduction (Expert Systems) vs induction (ML)
  • Why do we want machines to learn?
  • Case studies
  • Regression as a classic example of ML

Module 2: Data collection and preparation

  • Collecting the correct data for the training and testing phases is crucial. The data is often 'dirty' and needs to be cleaned. But more than that, the way in which the data is pre-processed is often the difference between poor and highly effective ML.
  • Data for supervised and unsupervised learning
  • Data selection
  • Data sampling
  • Data volume reduction
  • Removing ambiguities
  • Normalisation
  • Discretisation
  • Cleansing
  • Missing values
  • Outliers
  • Data and dimensional reduction
  • Data understanding
  • Generalisation of hierarchies

Module 3: Creating or choosing the algorithm

  • Building a new algorithm for the data modelling (or, as is often done, choosing an existing one) is a vital part of the process. Examples of creating algorithms
  • The use of data mining algorithms
  • Classes and examples of data mining

Module 4: Classic ML algorithms in Plain English

  • A number of algorithms are used very frequently. This module will look at some examples and explain what they are trying to achieve and how they work.
  • Regression
  • Clustering
  • Support Vector Machines (SVMs)
  • Classification
  • Segmentation
  • Association
  • Sequence analysis
  • Neural nets
  • Deep Learning

Module 5: training the data model

  • Once the algorithm has been built/selected we train it.
  • Selecting the training data
  • Ratio of training to test data
  • How to make an unbiased selection
  • How to use training data to create the model

Module 6: Testing and improving the data model

  • Testing is a vital (and complex) part of the process.
  • Confusion matrices
  • False positives vs False negatives
  • Measuring efficiency
  • ROC curves
  • Overfitting and bias

Module 7: Introduction to ML in R

  • R is a well-established open source language with many built-in ML algorithms. This module introduces the language and provides some practical ML work.
  • Introduction to R
  • Lab : ML with R

Module 8: ML underpinnings

  • It is much easier to create effective ML systems if we understand (and have a knowledge of) the hierarchically arranged systems that underpin ML. And we also need to understand that AI (Artificial Intelligence) is based on ML, ML on Data mining and so on.
  • AI
  • ML
  • Data mining
  • Statistics
  • Maths

Module 9: Combining data models

  • Any one ML system that we build will have a certain level of efficiency. But we can build a number of different data models and combine them in various ways so that the efficiency of the whole is greater than the sum of the parts.
  • Ensemble
  • Boosting
  • Gradient boosting

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