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Data Science 2: Machine Learning

In this course we continue our journey in the word of data science, moving from the data wrangling and explanation on Python seen in the previous course toward an overview on the most popular machine learning models in the industry. Starting from linear regression to the latest updates with gradient boosting, but also, clustering and feature selection/ranking algorithms. Each topic provides a theoretical understanding paired with a hands-on exercise to be solved in python and real word data.

Starts 12th August 2022

About This Course

This course provides a deeper look into machine learning techniques. You will be exposed to some of the most popular Python libraries that facilitate the training of various types of models and some ideas behind hyperparameter tuning. You will learn how to handle labelled versus unlabelled data. You will be exposed to a wide array of machine learning models, including, K-Means clustering, Linear Regression, Logistic Regression, various regularization techniques to deal with the bias/variance problem, K-Nearest Neighbours, Random Forest, Gradient Boosting, and many more. Finally, you will learn how to handle high-dimensional data via dimensionality reduction algorithms. An exposure to the world of machine learning research will also be provided.

Who Is This Course For

This course is for anyone with a basic understanding of data science concepts. The only pre-requisite is a good understanding of the Python programming language. The knowledge gained in this course can serve as the first step of a deep dive into any of the concepts. This class certification will be advantageous to anyone seeking an entry-level data science job or looking to advance their education in the fields of data science, machine learning, and/or artificial intelligence.

What you'll learn

  • Fundamental machine learning concepts
  • An understanding of various performance metrics for various models and techniques to optimise them
  • How to work with supervised versus unsupervised data
  • How to deal with overfitting and underfitting
  • How to manipulate data and build classical machine learning models
  • Bagging and Boosting techniques
  • Dimensionality reduction

Meet Your Instructor

Dr. Sterling Ramroach

Sterling has a PhD in Machine Learning from The University of the West Indies. He moved to London after his PhD to pursue a Computer Vision Researcher position in industry. His PhD research focused on accelerating machine learning techniques with parallel processing and applying it to bioinformatics. His work is published in peer-reviewed scientific journals including Molecular Omics, Expert Systems with Application, and BMJ Innovations.

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The courses together with the seminars offered are based on a subscription package. You can choose a six-months and annual subscription. A six-months subscription costs £210/€260/$280, while an annual subscription costs £399/€480/$530. With a six-months or annual subscription, you get unlimited access to all the courses and on-demand seminars offered by CBEHx for the subscription duration. Upon unsubscribing, you will no longer have access to the contents. There is no limit to how many certificates you can earn during your subscription period. To subscribe, please visit the pricing page on

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