Machine Learning for Beginners
Last Updated:
19 May 2026
Number of Students:
25 Students
Accreditation:
CPD Accredited
Prerequisites:
No formal qualification required (Anyone can participate)
Course Rating:
4.8 out of 5
Key features of the Machine Learning for Beginners
- Recognised qualification upon successful completion of the course
- Study from anywhere, anytime, whenever it is convenient for you.
- Get 24/7 support from our Customer Success Team
- Affordable and engaging e-learning study materials
- Study at your own pace from a tablet, PC or smartphone
- Online tutor support when you are in need.
Who is this course for?
There is no experience or previous qualifications required for enrolment on this course. It is available to all students, of all academic backgrounds.
Requirements
Our Machine Learning for Beginners is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation.
Career Path
After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market.
Course Curriculum
11 Sections | 48 Lessons
Introduction to Supervised Machine Learning
6m
Introduction to Regression
13m
Evaluating Regression Models
11m
Conditions for Using Regression Models in ML versus in Classical Statistics
21m
Statistically Significant Predictors
9m
Regression Models Including Categorical Predictors. Additive Effects
20m
Regression Models Including Categorical Predictors. Interaction Effects
18m
Multicollinearity among Predictors and its Consequences
21m
Prediction for New Observation. Confidence Interval and Prediction Interval
6m
Model Building. What if the Regression Equation Contains "Wrong" Predictors?
13m
Stepwise Regression and its Use for Finding the Optimal Model in Minitab
13m
Regression with Minitab. Example. Auto-mpg: Part 1
17m
Regression with Minitab. Example. Auto-mpg: Part 2
18m
The Basic idea of Regression Trees
18m
Regression Trees with Minitab. Example. Bike Sharing: Part 1
15m
Regression Trees with Minitab. Example. Bike Sharing: Part 2
10m
Introduction to Binary Logistics Regression
23m
Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC
20m
Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1
16m
Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2
18m
Introduction to Classification Trees
12m
Node Splitting Methods 1. Splitting by Misclassification Rate
20m
Node Splitting Methods 2. Splitting by Gini Impurity or Entropy
11m
Predicted Class for a Node
6m
The Goodness of the Model - 1. Model Misclassification Cost
11m
The Goodness of the Model - 2 ROC. Gain. Lit Binary Classification
15m
The Goodness of the Model - 3. ROC. Gain. Lit. Multinomial Classification
8m
Predefined Prior Probabilities and Input Misclassification Costs
11m
Building the Tree
8m
Classification Trees with Minitab. Example. Maintenance of Machines: Part 1
17m
Classification Trees with Miitab. Example. Maintenance of Machines: Part 2
10m
Data Cleaning: Part 1
16m
Data Cleaning: Part 2
17m
Creating New Features
12m
Polynomial Regression Models for Quantitative Predictor Variables
20m
Interactions Regression Models for Quantitative Predictor Variables
15m
Qualitative and Quantitative Predictors: Interaction Models
28m
Final Models for Duration and TotalCharge: Without Validation
18m
Underfitting or Overfitting: The "Just Right Model”
18m
The "Just Right” Model for Duration
16m
The "Just Right" Model for Duration: A More Detailed Error Analysis
12m
The "Just Right" Model for TotalCharge
14m
The "Just Right" Model for ToralCharge: A More Detailed Error Analysis
6m
Regression Trees for Duration and TotalCharge
18m
Predicting Learning Success: The Problem Statement
7m
Predicting Learning Success: Binary Logistic Regression Models
16m
Predicting Learning Success: Classification Tree Models
9m
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Key features of the Machine Learning for Beginners
- Recognised qualification upon successful completion of the course
- Study from anywhere, anytime, whenever it is convenient for you.
- Get 24/7 support from our Customer Success Team
- Affordable and engaging e-learning study materials
- Study at your own pace from a tablet, PC or smartphone
- Online tutor support when you are in need.
Who is this course for?
There is no experience or previous qualifications required for enrolment on this course. It is available to all students, of all academic backgrounds.
Requirements
Our Machine Learning for Beginners is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation.
Career Path
After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market.
11 Sections | 48 Lessons
Introduction to Supervised Machine Learning
6m
Introduction to Regression
13m
Evaluating Regression Models
11m
Conditions for Using Regression Models in ML versus in Classical Statistics
21m
Statistically Significant Predictors
9m
Regression Models Including Categorical Predictors. Additive Effects
20m
Regression Models Including Categorical Predictors. Interaction Effects
18m
Multicollinearity among Predictors and its Consequences
21m
Prediction for New Observation. Confidence Interval and Prediction Interval
6m
Model Building. What if the Regression Equation Contains "Wrong" Predictors?
13m
Stepwise Regression and its Use for Finding the Optimal Model in Minitab
13m
Regression with Minitab. Example. Auto-mpg: Part 1
17m
Regression with Minitab. Example. Auto-mpg: Part 2
18m
The Basic idea of Regression Trees
18m
Regression Trees with Minitab. Example. Bike Sharing: Part 1
15m
Regression Trees with Minitab. Example. Bike Sharing: Part 2
10m
Introduction to Binary Logistics Regression
23m
Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC
20m
Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1
16m
Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2
18m
Introduction to Classification Trees
12m
Node Splitting Methods 1. Splitting by Misclassification Rate
20m
Node Splitting Methods 2. Splitting by Gini Impurity or Entropy
11m
Predicted Class for a Node
6m
The Goodness of the Model - 1. Model Misclassification Cost
11m
The Goodness of the Model - 2 ROC. Gain. Lit Binary Classification
15m
The Goodness of the Model - 3. ROC. Gain. Lit. Multinomial Classification
8m
Predefined Prior Probabilities and Input Misclassification Costs
11m
Building the Tree
8m
Classification Trees with Minitab. Example. Maintenance of Machines: Part 1
17m
Classification Trees with Miitab. Example. Maintenance of Machines: Part 2
10m
Data Cleaning: Part 1
16m
Data Cleaning: Part 2
17m
Creating New Features
12m
Polynomial Regression Models for Quantitative Predictor Variables
20m
Interactions Regression Models for Quantitative Predictor Variables
15m
Qualitative and Quantitative Predictors: Interaction Models
28m
Final Models for Duration and TotalCharge: Without Validation
18m
Underfitting or Overfitting: The "Just Right Model”
18m
The "Just Right” Model for Duration
16m
The "Just Right" Model for Duration: A More Detailed Error Analysis
12m
The "Just Right" Model for TotalCharge
14m
The "Just Right" Model for ToralCharge: A More Detailed Error Analysis
6m
Regression Trees for Duration and TotalCharge
18m
Predicting Learning Success: The Problem Statement
7m
Predicting Learning Success: Binary Logistic Regression Models
16m
Predicting Learning Success: Classification Tree Models
9m
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