Switch to the store?

Machine Learning with C++ [Video]

More Information
  • Start your Machine Learning journey with C++
  • Understand the difference between generative and discriminative Machine Learning.
  • Understand the difference between unsupervised and supervised learning.
  • Explore the benefits of Linear Regression and Logistic Regression.
  • Implement a Linear Regression algorithm.
  • Understand the difference between K-means and K-NN.
  • Implement a K-means algorithm.

ML has become a fundamental part of the 21st century; from Netflix recommendations to fraud detection, ML is ever- present in our daily lives. At its roots, ML effectively applies statistics and pattern recognition, we will use these ideas to help solve a range of modern-day problems. C++ is a very fast language to execute your code and is extensively used when your final “models” are being deployed. If you want to run a program, with a lot of array calculation then C++ should be your weapon of choice.

This course will start off with a broad overview of ML and the varying methods associated with it. You will understand data types, Machine Learning algorithms, and a simple classification task. We then study two simple but effective algorithms to deepen your understanding and provide some practical experience. Specifically, the two algorithms that we will be investigating are linear regression and K-means clustering.

By taking this course, you will be able to get your machine Learning basics right and be able to build efficient algorithms which will help you to predict and cluster data.

Style and Approach

This course takes you through the fundamentals of Machine Learning, and how you can utilize your C++ skills to build efficient algorithms for predicting and clustering data.

  • An introduction to Machine Learning with C++.
  • Understand linear regression and it’s benefits and pitfalls.
  • Understand K-means clustering and it’s benefits and pitfalls.
Course Length 1 hour 29 minutes
Date Of Publication 16 Jan 2018


Tom Joy

Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, machine learning, and cloud computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the World's most popular soft drinks companies, helping each of them to make better sense of its data and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.

Tom Joy is studying for a PhD at the University of Oxford in the field of Semantic SLAM, which is the process of simultaneously localizing a robot in space; producing a map/understanding of the surrounding area whilst also detecting and delineating objects in 3D space. Achieving this requires a high level of competency in computer vision, machine learning, and optimization.

Tom has extensive experience in computer vision and machine learning, having taken several internships and placements over the course of his degree and spent time in industry prior to starting his PhD. He is a big advocate of explaining concepts simply and in a clear and concise manner; he strives to obtain and provide a comprehensive understanding of all relevant methods to the task at hand.