course - training

Machine Learning: Decision Trees & Random Forests

£162.50

With material brought to you by experts with decades of experience in the field, you know you’ll be in safe hands when you take this short course on decision trees and random forests. We’re not talking about getting outdoors here, these are tools that you can use as machine learning techniques to help solve problems.

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With material brought to you by experts with decades of experience in the field, you know you’ll be in safe hands when you take this short course on decision trees and random forests. These are tools that you can use as machine learning techniques to help solve problems. You’ll learn about each of these concepts and how they are applied, and you’ll also be able to take part in a Python based activity all about the ill-fated Titanic – who will survive? Why not consider this course and find out.

What Does This Course Include?

Ever seen diagrams that look like branches of a tree that can predict outcomes or provide solutions? This is the world of decision trees and random forests, and you can expect to gain an understanding of both, and their applications in machine learning.

Why Choose This Course?

This is a bite size course that is easy to fit in to your daily life, but is packed full of content. If you are new to these data science concepts, or are looking for a taster course to set you on the right path, then this e-learning option can deliver. With a wide ranging target audience, from analytics professionals and engineers, to data scientists, many can benefit from learning more about these machine learning techniques.

KEY LEARNING POINTS

To gain knowledge in two popular machine learning concepts, and apply them to a practical challenge on Kaggle, a competitive data science community.

  • Learn how decision trees and random forests are defined and the differences between them.
  • Understand more about random forests and decision trees as machine learning concepts.
  • Get to grips with overfitting, and why overly complex models offer poor results in data science.
  • Understand the role of random forests, and how they can reduce the risk of overfitting.
  • Look into use-cases that apply to random forests and decision trees.
  • Study ensemble learning, cross validation and regularisation and how they relate to overfitting.
  • Use what you’ve learned to help you create your own decision tree.
  • Complete a Python activity to predict survival of passengers aboard the Titanic. Submit your project to Kaggle, a popular data science community.
  • As you create your decision tree you’ll explore data before turning it into feature vectors that can eventually be fed into a decision tree classification system.
  • If you have experience running Python you can complete a coding exercise and make use of the source code supplied as part of the course content.

ADVANTAGES OF THIS COURSE

  • Learn wherever you are in an e-learning environment.
  • No entry requirements for this course although a working knowledge of Python is useful.
  • Material prepared by a team with a lot of combined experience in the field.
  • Further your knowledge of machine learning techniques.
  • Ideal course for a range of professionals from tech executives to data scientists.
  • On completion you’ll have a thorough grounding in decision trees and random forests.
  • Enhance your CV as you look for new tech/data science opportunities.