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Machine Learning Basics

4.9 (8 ratings) 5 enrolled 11h 17m of learning Updated Nov 2026
CertificateOn completion
11h 17mSelf-paced
47 lessonsOn the UISC learning portal
All LevelsNo prior training needed

Course curriculum

1 modules 47 lessons 11h 17m total
01 Machine Learning Basics 47 lessons
  1. Introduction to Supervised Machine Learning
  2. Introduction to Regression
  3. Evaluating Regression Models
  4. Conditions for Using Regression Models in ML versus in Classical Statistics
  5. Statistically Significant Predictors
  6. Regression Models Including Categorical Predictors. Additive Effects
  7. Regression Models Including Categorical Predictors. Interaction Effects
  8. Multicollinearity among Predictors and its Consequences
  9. Prediction for New Observation. Confidence Interval and Prediction Interval
  10. Model Building. What if the Regression Equation Contains “Wrong” Predictors?
  11. Stepwise Regression and its Use for Finding the Optimal Model in Minitab
  12. Regression with Minitab. Example. Auto-mpg: Part 1
  13. Regression with Minitab. Example. Auto-mpg: Part 2
  14. The Basic idea of Regression Trees
  15. Regression Trees with Minitab. Example. Bike Sharing: Part 1
  16. Regression Trees with Minitab. Example. Bike Sharing: Part 2
  17. Introduction to Binary Logistics Regression
  18. Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC
  19. Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1
  20. Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2
  21. Introduction to Classification Trees
  22. Node Splitting Methods 1. Splitting by Misclassification Rate
  23. Node Splitting Methods 2. Splitting by Gini Impurity or Entropy
  24. Predicted Class for a Node
  25. The Goodness of the Model – 1. Model Misclassification Cost
  26. The Goodness of the Model – 2 ROC. Gain. Lit Binary Classification
  27. The Goodness of the Model – 3. ROC. Gain. Lit. Multinomial Classification
  28. Predefined Prior Probabilities and Input Misclassification Costs
  29. Building the Tree
  30. Classification Trees with Minitab. Example. Maintenance of Machines: Part 1
  31. Classification Trees with Miitab. Example. Maintenance of Machines: Part 2
  32. Data Cleaning: Part 1
  33. Data Cleaning: Part 2
  34. Creating New Features
  35. Polynomial Regression Models for Quantitative Predictor Variables
  36. Interactions Regression Models for Quantitative Predictor Variables
  37. Qualitative and Quantitative Predictors: Interaction Models
  38. Final Models for Duration and TotalCharge: Without Validation
  39. Underfitting or Overfitting: The “Just Right Model”
  40. The “Just Right” Model for Duration
  41. The “Just Right” Model for Duration: A More Detailed Error Analysis
  42. The “Just Right” Model for TotalCharge
  43. The “Just Right” Model for ToralCharge: A More Detailed Error Analysis
  44. Regression Trees for Duration and TotalCharge
  45. Predicting Learning Success: The Problem Statement
  46. Predicting Learning Success: Binary Logistic Regression Models
  47. Predicting Learning Success: Classification Tree Models

About this course

 16 others bought this course in the last 24 hours!

Machine Learning Basics teaches you everything on the topic thoroughly from scratch so you can achieve a professional certificate for free to showcase your achievement in professional life. This Machine Learning Basics is a comprehensive, instructor-guided course, designed to provide a detailed understanding of the nature of the related sector and your key roles within it.

To become successful in your profession, you must have a specific set of skills to succeed in today’s competitive world. In this in-depth training course, you will develop the most in-demand skills to kickstart your career, as well as upgrade your existing knowledge & skills.

The training materials of this course are available online for you to learn at your own pace and fast-track your career with ease.

  • Accredited by CPD
  • Instant e-certificate
  • Fully online, interactive course with Professional voice-over
  • Self paced learning and laptop, tablet, smartphone friendly
  • 24/7 Learning Assistance
  • Discounts on bulk purchases

Who should take the course

Anyone with a knack for learning new skills can take this Machine Learning Basics. While this comprehensive training is popular for preparing people for job opportunities in the relevant fields, it also helps to advance your career for promotions.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Accreditation

All of our courses, including this Machine Learning Basics, are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.

Course Curriculum

The detailed curriculum outline of our Machine Learning Basics is as follows:

Section 01: Introduction

  • Introduction to Supervised Machine Learning

Section 02: Regression

  • Introduction to Regression
  • Evaluating Regression Models
  • Conditions for Using Regression Models in ML versus in Classical Statistics
  • Statistically Significant Predictors
  • Regression Models Including Categorical Predictors. Additive Effects
  • Regression Models Including Categorical Predictors. Interaction Effects

Section 03: Predictors

  • Multicollinearity among Predictors and its Consequences
  • Prediction for New Observation. Confidence Interval and Prediction Interval
  • Model Building. What if the Regression Equation Contains “Wrong” Predictors?

Section 04: Minitab

  • Stepwise Regression and its Use for Finding the Optimal Model in Minitab
  • Regression with Minitab. Example. Auto-mpg: Part 1
  • Regression with Minitab. Example. Auto-mpg: Part 2

Section 05: Regression Trees

  • The Basic idea of Regression Trees
  • Regression Trees with Minitab. Example. Bike Sharing: Part 1
  • Regression Trees with Minitab. Example. Bike Sharing: Part 2

Section 06: Binary Logistics Regression

  • Introduction to Binary Logistics Regression
  • Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC
  • Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1
  • Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2

Section 07: Classification Trees

  • Introduction to Classification Trees
  • Node Splitting Methods 1. Splitting by Misclassification Rate
  • Node Splitting Methods 2. Splitting by Gini Impurity or Entropy
  • Predicted Class for a Node
  • The Goodness of the Model – 1. Model Misclassification Cost
  • The Goodness of the Model – 2 ROC. Gain. Lit Binary Classification
  • The Goodness of the Model – 3. ROC. Gain. Lit. Multinomial Classification
  • Predefined Prior Probabilities and Input Misclassification Costs
  • Building the Tree
  • Classification Trees with Minitab. Example. Maintenance of Machines: Part 1
  • Classification Trees with Miitab. Example. Maintenance of Machines: Part 2

Section 08: Data Cleaning

  • Data Cleaning: Part 1
  • Data Cleaning: Part 2
  • Creating New Features

Section 09: Data Models

  • Polynomial Regression Models for Quantitative Predictor Variables
  • Interactions Regression Models for Quantitative Predictor Variables
  • Qualitative and Quantitative Predictors: Interaction Models
  • Final Models for Duration and TotalCharge: Without Validation
  • Underfitting or Overfitting: The “Just Right Model”
  • The “Just Right” Model for Duration
  • The “Just Right” Model for Duration: A More Detailed Error Analysis
  • The “Just Right” Model for TotalCharge
  • The “Just Right” Model for ToralCharge: A More Detailed Error Analysis

Section 10: Learning Success

  • Regression Trees for Duration and TotalCharge
  • Predicting Learning Success: The Problem Statement
  • Predicting Learning Success: Binary Logistic Regression Models
  • Predicting Learning Success: Classification Tree Models

Your certificate

CPD certificate — download it when you finish

Issued the moment you complete the final module. Every certificate lists the course aims and objectives, anticipated learning outcomes, the quality-assurance process — the elements required for verifiable CPD. Non-registrants can use it as CQC training evidence and for appraisals.

Aims & objectives Learning outcomes Unique certificate number

Learner ratings

4.9

Rated by 8 learners on the UISC learning portal · 5 enrolled

Common questions

Where do I take the course?

On the UISC learning portal. "Enrol now" takes you to this course there, where you pay securely, get your login and study at your own pace. Your certificate is issued there when you finish.

When can I start?

Immediately. Access opens the moment your order is confirmed and you can learn from any device, in any order, as many times as you like.

How is the course assessed?

A short multiple-choice or scenario-based assessment at the end of the course with instant results. You can retake it as many times as you need at no extra cost.

Can I enrol my whole team?

Yes. Organisations can buy multiple places on one invoice and receive a training record for every learner. Contact us for team pricing.

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