Understanding Machine Learning, 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.
Key features
- Fully certified trainer with real world experience
- Get key skills and practical knowledge
- This course is available live online, onsite, on demand, in person
- Course materials included
- Certificates for each participant
What will I learn?
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
Decision trees
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
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
Decision trees
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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