Machine Learning Basics with Free Certificate

Free QLS Certificate | Advanced Diploma in Machine Learning Basics at QLS Level 7 | CPD Certified | 24/7 Tutor Support

Course Highlights

Do you want to advance your career and gain a strong foundation in Machine Learning Basics? Our Machine Learning Basics course is the ideal remedy. This course, created by professionals in the field, offers a methodical approach to gaining the fundamental abilities and understanding required to succeed in Machine Learning Basics. This course will assist you in understanding the fundamental ideas, instruments, and methods utilised in the industry, regardless of your experience level.

You will have valuable experience with exercises supporting your study through the Machine Learning Basics. Our course module will ensure you understand the more complicated techniques and the essential ideas to help you grasp Machine Learning Basics more deeply. Furthermore, networking with industry professionals and other students will improve both your academic and career opportunities.

After the course, you’ll be prepared to assume positions in Machine Learning Basics on a professional level. As an asset to any organisation, your strong foundation of knowledge will be an asset. Additionally, your CV will provide you a competitive edge over other applicants by showcasing your accomplishments and skills. Start your journey to consistent income and career success by enrolling in our Machine Learning Basics right now.

Note: Skill-up is a Janets-approved resale partner for Quality Licence Scheme Endorsed courses. Free QLS Certificate Included.

Learning outcome

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Certificate of Achievement

Quality Licence Scheme Endorsed Certificate

Upon completing the final assessment, you can apply for the Quality Licence Scheme Endorsed Certificate of Achievement. Endorsed certificates can be ordered and delivered to your home by post.

Free QLS Level 7 Certificate Included with this Machine Learning Basics course.

An extra £10 postage charge will be required for students leaving overseas.

Skill Up Recognised Certificate

Upon successfully completing the Machine Learning Basics course, you can request a Skill Up Recognised Certificate. This certificate holds significant value, and its validation will endure throughout your lifetime.

1. PDF Certificate + PDF Transcript: FREE With This Course

2. Hard Copy Certificate + Hard Copy Transcript: £19.99

3. Delivery Charge: £10.00 (Applicable for International Students)

Note: There are no discount coupons available for this course at the moment.

Why should I take this course?

- You will receive QLS Endorsed & CPD Accredited Certificate upon completing the course.
- Affordable premium-quality e-learning content, you can learn at your own pace.
- Internationally recognised Accredited Qualifications will boost up your resume.
- You will learn the researched and proven approach successful people adopt to transform their careers.
- You can incorporate various techniques successfully and understand your customers better.

Endorsement

This course has received endorsement by the Quality Licence Scheme for its exceptional quality, non-regulated training program. However, it is not regulated by Ofqual and does not offer accredited qualifications. Your training provider can advise you on prospective recognition opportunities, such as avenues to additional or higher education.

Method of Assessment

To assess your knowledge, you will take an automated multiple-choice exam. Passing and meeting the criteria for the Quality Licence Scheme-endorsed certificate requires a minimum score of 60%. Once you have achieved this score, you can apply for your certificate.

Furthermore, there are assignment questions at the end of the course that we highly recommend to answer. Completing these questions will help you understand your progress. You can answer them any time you want. The best thing is that our knowledgeable tutors will go over your work and provide insightful criticism.

Requirements

This Machine Learning Basics course is open to everyone, and no specific prerequisites are required. Anyone with an interest in the field can join!

With complete access to any internet-enabled device, you can learn anytime, anywhere—right from the comfort of your home.

Course Curriculum

Module 01: Introduction

Introduction to Supervised Machine Learning

00:06:00

Module 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

Module 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

Module 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

Module 05: Regression Trees

The Basic idea of Regression Trees

00:18:00
Regression Trees with Minitab. Example. Bike Sharing: Part 1

00:15:00
Regression Trees with Minitab. Example. Bike Sharing: Part 2

00:10:00

Module 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

Module 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

Module 08: Data Cleaning

Data Cleaning: Part 1

00:16:00
Data Cleaning: Part 2

00:17:00
Creating New Features

00:12:00

Module 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