Machine Learning allows the computers to act without being explicitly programmed. Machine learning uses computers to predict future behaviours, outcomes, and trends by analysing from existing data.
Throughout the data science course [course_title], you will learn machine learning theory including the practical scenarios and hands-on experience building, validating, and deploying machine learning models. The course teaches you how to build and derive insights from these models using R, Python, and Azure Machine Learning.
Upon completion, you will get a solid understanding of the concepts and principles of Machine Learning.
Assessment
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
Certification
Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.
Having an Official Edukite Certification is a great way to celebrate and share your success. You can:
- Add the certificate to your CV or resume and brighten up your career
- Show it to prove your success
Course Credit: Microsoft
Course Curriculum
Before You Start | |||
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Module 1: Classification | |||
Lesson 1: Introduction to Classification | |||
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Lesson 2: Building Classification Models | |||
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Module 2: Regression | |||
Lesson 1: Introduction to Regression | |||
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Lesson 2: Creating Regression Models | |||
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Module 3: Improving Machine Learning Models | |||
Lesson 1: Principles of Model Improvement | |||
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Lesson 2: Techniques for Improving Models | |||
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Module 4: Tree and Ensemble Methods | |||
Lesson 1: Introduction to Decision Trees | |||
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Lesson 2: Ensemble Methods | |||
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Module 5: Optimization-Based Methods | |||
Lesson 1: Neural Networks | |||
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Lesson 2: Support Vector Machines (SVMs) | |||
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Module 6: Clustering and Recommenders | |||
Lesson 1: Clustering | |||
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Lesson 2: Recommenders | |||
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Assessment | |||
Submit Your Assignment | 00:00:00 | ||
Certification | 00:00:00 |
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