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This course introduces students to the basic theory of machine learning, the most efficient machine learning algorithms and practical implementation of these algorithms. Students will gain hands-on experience of getting these algorithms to solve real-world problems.
Topics included in this unit are: the foundations of machine learning, types of learning problems (classification, regression, clustering etc.), taxonomy of machine learning algorithms (supervised learning, unsupervised learning, reinforcement learning), machine learning algorithms (decision tree, naïve Bayes, k-nearest neighbor, support vector machine etc.).

On successful completion of this course, students will understand the concept of machine learning and machine learning algorithms. They will have gained hands-on experience in implementing algorithms using a programming language such as C/C++, C#, Java, Python, R, or a machine learning tool such as Weka, KNIME, Microsoft AzureML. As a result, students will develop skills such as communication literacy, critical thinking, analysis, reasoning and interpretation, which are crucial for gaining employment and developing academic competence

By the end of this course a student will be able to:

  1. Analyse the theoretical foundation of machine learning to determine how an intelligent machine works.
  2. Investigate the most popular and efficient machine learning algorithms used in industry.
  3. Develop a machine learning application using an appropriate programming language or machine learning tool for solving a real-world problem.
  4. Evaluate the outcome or the result of the application to determine the effectiveness of the learning algorithm used in the application.

There are no formal prerequisites or qualifications required to enroll. Here are a few things to keep in mind:

  • As the course is conducted entirely in English, we recommend that learners have a good grasp of the English language, both written and spoken, to gain maximum benefit.
  • Since this is an online course, you’ll need a computer, tablet, or smartphone with internet access. Familiarity with basic computer skills would be advantageous.
  • While you can learn at your own pace, dedicating consistent time to the course will help you understand and retain the material better.

          Registration process:

          • Based on course settings, students might be requested to fill in an application (online) and provide us with correct information about themselves before being able to proceed with the course.
          • We will contact you by email if we need more details after submitting your application.
          • After approval, you can proceed with payment and start the course.

          Materials are organized under different lessons. The lessons consist of self-study contents including reading-texts, practical activity, assignment and quizzes in which students can engage and learn.

          The course has supervisors and instructors; they are available online to support students by answering their questions or explain different topics to them. This will be managed under discussion section, forums, direct contact or by online meetings.

          When student successfully completes all lessons, quizzes and pass assignment, he/she will be offered a certificate.

          The course is available as an individual or under Pearson Qualifications (HNC/HND) in Computing. The price will be updated based on the selected option. The individual course fee is higher because awarding body has additional fees for individual enrollment. At the same time, a discount will be automatically added when you order HNC/HND in Computing or if you’re already enrolled under these qualifications.

          Courses awarded under Next Gates have no discount, and the fee is already reduced.

          * Note that prices don’t include VAT. Additional fee might be applied at checkout.

          Course Curriculum

          Section One
          Section Two
          Section Three
          Section Four
          Section Five
          Final Assignments

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