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Machine Learning Basics
CPDUK Accredited | 50% OFF Certificate & Transcript
4 Students enrolled on this course 4.7 (10 Reviews)
Last updated September 8, 2023
Register on the Machine Learning Basics today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career.
The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials.
Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as a proof of your course completion.
The Machine Learning Basics is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones.
The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly!
What You Get With The Machine Learning Basics
- Receive an e-certificate upon successful completion of the course
- Get taught by experienced, professional instructors
- Study at a time and pace that suits your learning style
- Get instant feedback on assessments
- 24/7 help and advice via email or live chat
- Get full tutor support on weekdays (Monday to Friday)
Certificate of Achievement
Endorsed Certificate of Achievement from the Quality Licence Scheme
Upon successful completion of the final assessment, you will be eligible to apply for the Quality Licence Scheme Endorsed Certificate of achievement. This certificate will be delivered to your doorstep through the post for £119. An extra £10 postage charge will be required for students leaving overseas.
CPD Accredited Certificate
After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.
Who Is This Course For
The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge.
Requirements
The online training is open to all students and has no formal entry requirements. To study the Machine Learning Basics, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16.
Course Curriculum
| Section 01: Introduction | |||
| Introduction to Supervised Machine Learning | 00:06:00 | ||
| Section 02: Regression | |||
| Introduction to Regression | 00:13:00 | ||
| 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 Total Charge: 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 Total Charge | 00:14:00 | ||
| The “Just Right” Model for Toral Charge: 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 | 00:17:00 | ||
| Predicting Learning Success: Classification Tree Models | 00:09:00 | ||
| Order your Certificates & Transcripts | |||
| Order your Certificates & Transcripts | 00:00:00 | ||
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Frequently asked questions
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There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course.
You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience.
For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime.
Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks.
We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase.
Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course.
If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.