Understanding Machine Learning

About this course

Course type Performance Plus

Course code QAIML

Duration 4 Days

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.

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Prerequisites

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

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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?
Supervised vs. unsupervised learning
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 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: Introduction to ML in R

R is a well-established, open source language with many built-in ML algorithms. his module introduces the language and provides some practical ML work.

Introduction to R
Lab: ML with R

Module 4: Creating or choosing an algorithm

Building a new algorithm for the data modelling (or, as is often done, choosing an existing one) is another vital part of the process. There are a number of algorithms that are very frequently used. This module will look as some examples and explain what they are trying to achieve and how they work.

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

Module 5: training and test data

How and why we train and test

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

Module 6: Testing and confusion matrices

Testing is a vital (and complex) part of the process.

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

Module 7: ROC curves

ROC space and curves are also vital tools for estimating the efficiency of algorithms

Measuring efficiency
ROC space and ROC curves

Module 8: Efficiency, Overfitting, Bias and Variance

More about efficiency
Overfitting
Bias and Variance

Module 9: Combining data models

Any one ML system that we built 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
Case study of combining models
Summary

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Performance Plus

4 Days

Duration

This course is authored by QA

Delivery method

Classroom

Face-to-face learning in the comfort of our quality nationwide centres, with free refreshments and Wi-Fi.

Find dates and prices

We have 4 courses available across 2 locations on 4 different dates

Trusted, awarded and accredited

Fully accredited to ensure we provide the highest possible standards in learning