Machine Learning Training Course

Course CodeCourse Code

bspkaml

Duration Duration

21 hours (usually 3 days including breaks)

Overview Overview

This course will be a combination of theory and practical work with specific examples used throughout the event.

Course OutlineCourse Outline

Introduction

This section provides a general introduction of when to use 'machine learning', what should be considered and what it all means including the pros and cons. Datatypes (structured/unstructured/static/streamed), data validity/volume, data driven vs user driven analytics, statistical models vs. machine learning models/ challenges of unsupervised learning, bias-variance trade off, iteration/evaluation, cross-validation approaches, supervised/unsupervised/reinforcement.

MAJOR TOPICS

1.Understanding naive Bayes

  • Basic concepts of Bayesian methods 
  • Probability 
  • Joint probability
  • Conditional probability with Bayes' theorem 
  • The naive Bayes algorithm 
  • The naive Bayes classification 
  • The Laplace estimator
  • Using numeric features with naive Bayes

2.Understanding decision trees

  • Divide and conquer 
  • The C5.0 decision tree algorithm 
  • Choosing the best split 
  • Pruning the decision tree

3. Understanding neural networks

  • From biological to artificial neurons 
  • Activation functions 
  • Network topology 
  • The number of layers 
  • The direction of information travel 
  • The number of nodes in each layer 
  • Training neural networks with backpropagation
  • Deep Learning

4. Understanding Support Vector Machines

  • Classification with hyperplanes 
  • Finding the maximum margin 
  • The case of linearly separable data 
  • The case of non-linearly separable data 
  • Using kernels for non-linear spaces

5. Understanding clustering

  • Clustering as a machine learning task
  • The k-means algorithm for clustering 
  • Using distance to assign and update clusters 
  • Choosing the appropriate number of clusters

6. Measuring performance for classification

  • Working with classification prediction data 
  • A closer look at confusion matrices 
  • Using confusion matrices to measure performance 
  • Beyond accuracy – other measures of performance 
  • The kappa statistic 
  • Sensitivity and specificity 
  • Precision and recall 
  • The F-measure 
  • Visualizing performance tradeoffs 
  • ROC curves 
  • Estimating future performance 
  • The holdout method 
  • Cross-validation 
  • Bootstrap sampling

7. Tuning stock models for better performance

  • Using caret for automated parameter tuning 
  • Creating a simple tuned model 
  • Customizing the tuning process 
  • Improving model performance with meta-learning 
  • Understanding ensembles 
  • Bagging 
  • Boosting 
  • Random forests 
  • Training random forests 
  • Evaluating random forest performance

MINOR TOPICS

8. Understanding classification using nearest neighbors 

  • The kNN algorithm 
  • Calculating distance 
  • Choosing an appropriate k 
  • Preparing data for use with kNN 
  • Why is the kNN algorithm lazy?

9. Understanding classification rules 

  • Separate and conquer 
  • The One Rule algorithm 
  • The RIPPER algorithm 
  • Rules from decision trees

10.Understanding regression 

  • Simple linear regression 
  • Ordinary least squares estimation 
  • Correlations 
  • Multiple linear regression

11.Understanding regression trees and model trees 

  • Adding regression to trees

12. Understanding association rules 

  • The Apriori algorithm for association rule learning 
  • Measuring rule interest – support and confidence 
  • Building a set of rules with the Apriori principle

Extras

  • Spark/PySpark/MLlib and Multi-armed bandits

Bookings, Prices and EnquiriesBookings, Prices and Enquiries

Guaranteed to run even with a single delegate!
Private Classroom
 
Private Classroom
Participants are from one organisation only. No external participants are allowed. Usually customised to a specific group, course topics are agreed between the client and the trainer.
Private Remote
From £3300
Private Remote
The instructor and the participants are in two different physical locations and communicate via the Internet. More Information

The more delegates, the greater the savings per delegate. Table reflects price per delegate and is used for illustration purposes only, actual prices may differ.

Number of Delegates Private Remote
1 £3300
2 £2325
3 £2000
4 £1838
Public Classroom
From £3750
Request
Public Classroom
Participants from multiple organisations. Topics usually cannot be customised

The more delegates, the greater the savings per delegate. Table reflects price per delegate and is used for illustration purposes only, actual prices may differ.

Number of Delegates Public Classroom
1 £3750
2 £2575
3 £2183
4 £1988
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