
Understanding Machine Learning
Prerequisites
- An understanding of data
- A good logical mind
- We do not expect people to have a background in mathematics
Outline
- 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 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 R
- Lab: ML with R
- 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
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Neural nets
- History
- Layers
- Weights
- Back propagation
- Deep Learning
- KNN
- SVM
- Selecting the training and testing data
- Ratio of training to test data
- How to make an unbiased selection
- Type 1, 2 and 3 errors
- False positives vs False negatives
- PCC
- Classification models
- Confusion matrices
- Measuring efficiency
- ROC space and ROC curves
- More about efficiency
- Overfitting
- Bias and Variance
- Ensemble
- Boosting
- Gradient boosting
- Case study of combining models
- Summary

4 Days

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.
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