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Course Overview
Learn how to make a genuine difference in your life by taking our popular Machine Learning Basics. Our commitment to online learning and our technical experience have been put to excellent use within the content of these educational modules. By enrolling today, you can take your knowledge of Machine Learning Basics to a whole new level and quickly reap the rewards of your study in the field you have chosen.
We are confident that you will find the skills and information that you will need to succeed in this area and excel in the eyes of others. Do not rely on substandard training or half-hearted education. Commit to the best, and we will help you reach your full potential whenever and wherever you need us.
Please note that Machine Learning Basics provides valuable and significant theoretical training for all. However, it does not offer official qualifications for professional practice. Always check details with the appropriate authorities or management.
Learning Outcomes
Your Path to Success
By completing the training in Machine Learning Basics, you will be able to significantly demonstrate your acquired abilities and knowledge of Machine Learning Basics. This can give you an advantage in career progression, job applications, and personal mastery in this area.
Is This Course Right for You?
This course is designed to provide an introduction to Machine Learning Basics and offers an excellent way to gain the vital skills and confidence to start a successful career. It also provides access to proven educational knowledge about the subject and will support those wanting to attain personal goals in this area. Full-time and part-time learners are equally supported, and the study periods are entirely customisable to your needs.
Certificate of Achievement
Endorsed Certificate of Achievement by the Quality Licence Scheme
An endorsed certificate will be issued for the learners as proof of their achievement after the completion of this course.
After successful course completion, learners will be able to order an endorsed certificate (Advanced Diploma in Machine Learning Basics at QLS Level 7) as proof of their new achievement. Only for £139 you can order and get endorsed certificates delivered to your home. International students have to pay an extra £10 as a postage charge.
CPD Accredited Certificate from HF Online
The learners have to successfully complete the assessment of this course to achieve the CPD certificates. Digital certificates can be ordered for only £10. The learner can purchase printed hard copies inside the UK for £29, and international students can purchase printed hard copies for £39.
Endorsement
This course and/or training programme has been endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. This course and/or training programme is not regulated by Ofqual and is not an accredited qualification. Your training provider will be able to advise you on any further recognition, for example progression routes into further and/or higher education. For further information please visit the Learner FAQs on the Quality Licence Scheme website.
Assessment Method
You have to complete the assignment questions given at the end of the course and score a minimum of 60% to pass the exam and achieve Quality Licence Scheme endorsed certificates.
Our expert trainers will assess your assignment and give you feedback after you submit the assignment.
Section 01: Introduction | |||
Introduction to Supervised Machine Learning | 00:06:00 | ||
Section 02: Regression | |||
Introduction to Regression | 00:14:00 | ||
Evaluating Regression Models | 00:11:00 | ||
Conditions for Using Regression Models in ML versus in Classical Statistics | 00:21:00 | ||
Statistically Significant Predictors | 00:09:00 | ||
Regression Models Including Categorical Predictors. Additive Effects | 00:20:00 | ||
Regression Models Including Categorical Predictors. Interaction Effects | 00:18:00 | ||
Section 03: Predictors | |||
Multicollinearity among Predictors and its Consequences | 00:21:00 | ||
Prediction for New Observation. Confidence Interval and Prediction Interval | 00:06:00 | ||
Model Building. What if the Regression Equation Contains “Wrong” Predictors? | 00:13:00 | ||
Section 04: Minitab | |||
Stepwise Regression and its Use for Finding the Optimal Model in Minitab | 00:13:00 | ||
Regression with Minitab. Example. Auto-mpg: Part 1 | 00:17:00 | ||
Regression with Minitab. Example. Auto-mpg: Part 2 | 00:18:00 | ||
Section 05: Regression Trees | |||
The Basic idea of Regression Trees | 00:18:00 | ||
Regression Trees with Minitab. Example. Bike Sharing: Part1 | 00:15:00 | ||
Regression Trees with Minitab. Example. Bike Sharing: Part 2 | 00:10:00 | ||
Section 06: Binary Logistics Regression | |||
Introduction to Binary Logistics Regression | 00:23:00 | ||
Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC | 00:20:00 | ||
Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1 | 00:16:00 | ||
Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2 | 00:18:00 | ||
Section 07: Classification Trees | |||
Introduction to Classification Trees | 00:12:00 | ||
Node Splitting Methods 1. Splitting by Misclassification Rate | 00:20:00 | ||
Node Splitting Methods 2. Splitting by Gini Impurity or Entropy | 00:11:00 | ||
Predicted Class for a Node | 00:06:00 | ||
The Goodness of the Model – 1. Model Misclassification Cost | 00:11:00 | ||
The Goodness of the Model – 2 ROC. Gain. Lit Binary Classification | 00:15:00 | ||
The Goodness of the Model – 3. ROC. Gain. Lit. Multinomial Classification | 00:08:00 | ||
Predefined Prior Probabilities and Input Misclassification Costs | 00:11:00 | ||
Building the Tree | 00:08:00 | ||
Classification Trees with Minitab. Example. Maintenance of Machines: Part 1 | 00:17:00 | ||
Classification Trees with Miitab. Example. Maintenance of Machines: Part 2 | 00:10:00 | ||
Section 08: Data Cleaning | |||
Data Cleaning: Part 1 | 00:16:00 | ||
Data Cleaning: Part 2 | 00:17:00 | ||
Creating New Features | 00:12:00 | ||
Section 09: Data Models | |||
Polynomial Regression Models for Quantitative Predictor Variables | 00:20:00 | ||
Interactions Regression Models for Quantitative Predictor Variables | 00:15:00 | ||
Qualitative and Quantitative Predictors: Interaction Models | 00:28:00 | ||
Final Models for Duration and TotalCharge: Without Validation | 00:18:00 | ||
Underfitting or Overfitting: The “Just Right Model” | 00:18:00 | ||
The “Just Right” Model for Duration | 00:16:00 | ||
The “Just Right” Model for Duration: A More Detailed Error Analysis | 00:12:00 | ||
The “Just Right” Model for TotalCharge | 00:14:00 | ||
The “Just Right” Model for ToralCharge: A More Detailed Error Analysis | 00:06:00 | ||
Section 10: Learning Success | |||
Regression Trees for Duration and TotalCharge | 00:18:00 | ||
Predicting Learning Success: The Problem Statement | 00:07:00 | ||
Predicting Learning Success: Binary Logistic Regression Models | 00:16:00 | ||
Predicting Learning Success: Classification Tree Models | 00:09:00 | ||
Order Your Certificate | |||
Claim Your QLS Certificate | 00:00:00 |