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Data Science 2: Machine Learning

In this course we continue our journey in the world of data science, moving from the data wrangling and explanation on Python seen in the previous course toward an overview of 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 29th May 2024

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. 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

Course Content

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Meet Your Instructor

Dr. Sterling Ramroach

Sterling has a Ph.D. in Machine Learning from The University of the West Indies. He moved to London after his Ph.D. to pursue a Computer Vision Researcher position in the industry. His Ph.D. 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.

Our Partners

We are proud to partner with the leading In2scienceUK as a step towards achieving our mission of imparting quality education and knowledge across the globe.

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Usually, there are no general prerequisites when enrolling for any individual courses on cbehx.co.uk. However, some courses are certainly more advanced than others that are of introductory and intermediate level. For advanced-level courses, prior knowledge on the topic will be helpful for understanding complex concepts but not mandatory.

The relevant information about the suggested knowledge and background that might be helpful to follow each course is provided on the respective course registration page.

Yes, the courses are flexible and self-paced to suit your schedule. The term “self-paced” implies that the courses do not follow a pre-assigned schedule for learning within the course duration. You will be able to access all the course materials for each course that you have enrolled in as soon as the course begins.

The course completion certificate is CPD accredited. For more informations please visit https://cpduk.co.uk