This course will cover the theory behind many key concepts in the model building workflow. We will look at how to preprocess data and then how to use sklearn to run a series of models, including Regressions, Suport Vector Machines, Neural Networks and Hierarchical Clustering methods. We will also discuss how to evaluate models for their performance and improve them through Cross Validation and Hyperparameter Tuning.
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An Introduction to Scikit-Learn
Your one stop shop for getting familiar with Scikit-Learn, one of the most important modelling packages in Python.
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This course contains:
Table of contents
You will learn
✓ This course will cover all aspects of the modelling workflow.
✓ We will look at Preprocessing, running Regressions, Classifications, Neural Networks and Clustering algorithms
✓ We will also cover Evaluation methodology for building highly successful models
This course is for
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I have been responsible for building high speed trading algorithms in the foreign exchange and commodities markets, both from a technical and fundamental analysis perspective. I also have built statistical modelling tools in Sports Analysis, for both the Betting markets and Behavioural Analysis sector.