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An Introduction to Scikit-Learn

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An Introduction to Scikit-Learn, available at $19.99, has an average rating of 4.5, with 8 lectures, based on 14 reviews, and has 66 subscribers.

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You will learn about 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, running Regressions, Classifications, Neural Networks and Clustering algorithms We will also cover Evaluation methodology for building highly successful models This course is ideal for individuals who are Beginner Python developers with an interest in running Python statistical models. It is particularly useful for Beginner Python developers with an interest in running Python statistical models.

Enroll now: An Introduction to Scikit-Learn

Summary

Title: An Introduction to Scikit-Learn

Price: $19.99

Average Rating: 4.5

Number of Lectures: 8

Number of Published Lectures: 8

Number of Curriculum Items: 8

Number of Published Curriculum Objects: 8

Original Price: £19.99

Quality Status: approved

Status: Live

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, running Regressions, Classifications, Neural Networks and Clustering algorithms
  • We will also cover Evaluation methodology for building highly successful models

Who Should Attend

  • Beginner Python developers with an interest in running Python statistical models.

Target Audiences

  • Beginner Python developers with an interest in running Python statistical models.

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Preprocessing

Lecture 1: Preprocessing

Chapter 3: Regression

Lecture 1: Regression

Chapter 4: Classification

Lecture 1: Classification

Chapter 5: Regression & Classification

Lecture 1: Feature Selection and Neural Networks

Chapter 6: Clustering

Lecture 1: Clustering

Chapter 7: Model Selection and Evaluation

Lecture 1: Model Selection and Evaluation

Chapter 8: Final Thoughts

Lecture 1: Final Thoughts

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 0 votes
  • 3 stars: 0 votes
  • 4 stars: 2 votes
  • 5 stars: 11 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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