Description

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 Content

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

Frequently Asked Questions

Are there any prerequisites for taking the course?

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.

Can I access the course at any time, or is there a set schedule?

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.

How long will I have access to the course?

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.

Is there a certificate of completion provided after completing the course?

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.

Can I switch courses or get a refund if I'm not satisfied with the course?

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.

How do I track my progress in the course?

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.

What if I have technical issues or difficulties with 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.

Course Images

Machine Learning Basics

By Janets

Booking options

Highlights

Description

About The Provider

Tags

Reviews