Course Overview

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, Support 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.

What You Will Learn

  • This course is a one stop shop for an introduction to sklearn
  • the most commonly used Python package for statistical modelling
  • This course will cover all aspects of the modelling workflow
  • We will look at Preprocessing
  • Regressions
  • Classifications
  • Neural Networks and Clustering algorithms
  • We will also cover Evaluation methodology for building highly successful models

Program Curriculum

  • Preprocessing
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  • Chapter 1 Quiz

  • Regression
  • Chapter 2 Quiz

  • Classification
  • Chapter 3 Quiz

  • Regression and Classification
  • Chapter 4 Quiz

  • Clustering
  • Chapter 5 Quiz

  • Model Selection and Evaluation
  • Chapter 6 Quiz

Final Thoughts

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Instructor

Dhruva Krishna

Dhruva is a Data Scientist with experience in the Trading, IoT and Sports Analytics sectors. He spent considerable time understanding Machine Learning models, both in Python and on a theoretical level and building complex visualizations in BI tools such as Tableau. He has been responsible for building high-speed trading algorithms in the foreign exchange and commodities markets, both from a technical and fundamental analysis perspective. He also has built statistical modelling tools in Sports Analysis, for both the Betting markets and Behavioral Analysis sector.

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