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Full Course

£80

£400
80% OFF

4.7

(1316 reviews)
30-Days money-back guarantee
Course Includes:
  • ci Online Study Mode
  • ci 450 Hours
  • ci Study at your own Pace
  • ci Completion Certificate

Key Aspects of Course

  • icon Level 5 Diploma
  • icon Full Online Course
  • icon CPD Approved
  • icon Employer Approved
  • icon Fully Endorsed Course
  • icon 450 Hours Training
  • icon No Entry Requirements
  • icon Boost Your Resume
  • icon Own Pace Study
  • icon Premium Course Material
  • icon Completion Certificate
Special offer

£80

£400
paygroup

Supervised Learning Techniques

 

The Supervised Learning Techniques course introduces learners to the fundamental methods of teaching machines how to learn from labelled datasets. By mastering these approaches, students gain the ability to build predictive models that can classify, forecast, and make data-driven decisions with accuracy. The course also explains how to define supervised and unsupervised learning, making it easier for learners to distinguish between the two and understand their unique applications.

 

Throughout the course, students will explore the most widely used algorithms, including linear models, decision trees, k-nearest neighbours (k-NN), and support vector machines. Practical emphasis is placed on feature engineering, model evaluation, and hyperparameter tuning to ensure strong performance in real-world scenarios. By the end of the program, learners will be able to apply supervised learning techniques effectively to solve complex business, research, and AI challenges.

Learning Outcomes
  • How to define supervised and unsupervised learning Applying supervised learning algorithms for prediction and classification
  • Building linear and non-linear models
  • k-NN
  • Applying ensemble methods like bagging
  • and random forests
  • Exploring real-world applications of supervised learning
  • Data preprocessing and feature engineering
  • Using decision trees
  • and support vector machines
  • boosting
  • Evaluating models and tuning hyperparameters
Who should learn
this course
  • Data scientists
  • Machine learning engineers
  • AI researchers
  • Software developers
  • Business analysts
  • IT professionals
  • Students pursuing data science and AI

SYLLABUS

Module 1

Module 1

Introduction to Supervised Learning

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Module 2

Module 2

Data Pre-processing and Feature Engineering

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Module 3

Module 3

Linear Models in Supervised Learning

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Module 4

Module 4

Classification Algorithms: Decision Trees and k-Nearest Neighbours (k-NN)

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Module 5

Module 5

Support Vector Machines (SVM) for Classification

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Module 6

Module 6

Ensemble Methods: Bagging, Boosting, and Random Forests

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Module 7

Module 7

Model Evaluation and Hyperparameter Tuning

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Module 8

Module 8

Real-World Applications and Future Trends in Supervised Learning

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Future market Growth
250 Billion

Expected Artificial Intelligence market growth by the end of 2026

90% Growth

Increased in growth in different learning organizations

Career opportunities

Average Salary

£50k - £110k per Annum

Hiring Companies

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Certifications
Frequently Asked Questions (FAQs)

Our AI Intelligence courses are entirely self-paced, allowing you to study whenever it suits you best. Whether you finish quickly or take your time, there are no deadlines or expiry dates.

No attendance is required. All Artificial Intelligence study materials and assessments are delivered online, enabling full home-study learning without any campus visits.

The course fee displayed on the website includes everything—learning materials, registration, and tutor support. Unless you decide to upgrade your certificate, there are no additional charges.

You’ll be guided by a dedicated tutor who specialises in Artificial Intelligence. They will assist with academic questions, clarify difficult topics, and provide detailed assessment feedback.

Absolutely. Our AI Intelligence courses are open to learners worldwide. As long as you have internet access, you can join from any country.

Our introductory AI Intelligence courses are created specifically for newcomers. They focus on foundational AI concepts before moving into advanced algorithms and applications, making them ideal for first-time learners.

All you’ll need is a laptop or computer with a stable internet connection. Every learning resource, including modules and assessments, is provided digitally—no additional software is required.

Yes. The entire learning journey is completed online, including lessons, reading materials, assignments, and tutor communication. There is no requirement for in-person training.

Upon finishing your chosen programme, you’ll receive an endorsed certificate that can enhance your CV and support your entry into the growing Artificial Intelligence industry.

Completing an AI course can lead to roles such as AI technician, data analyst, machine learning assistant, automation specialist, or support positions within tech-driven companies. Advanced study may open doors to senior AI and machine learning roles.