Machine Learning Basics

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

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Machine Learning Basics Course Highlights

Start on a transformative journey into the realm of Machine Learning. This course offers a comprehensive exploration of essential concepts and techniques, providing a solid foundation for data-driven decision-making. You’ll delve into regression analysis, predictors, and statistical analysis using Minitab software. Gain essential insights into regression and classification trees, mastering techniques like binary logistics regression and data cleansing.

By the end, you’ll be equipped with the skills to create and interpret data models, enabling you to tackle real-world challenges with confidence. Whether you’re a beginner looking to enter the field of Machine Learning or a professional seeking to enhance your analytical capabilities, this course provides the knowledge and tools you need to succeed.

Join us and unlock the power of data insights to shape a future driven by innovation and discovery.

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

Learning outcome

Understand regression and classification trees.
Apply Minitab for statistical analysis.
Implement binary logistics regression.
Cleanse data effectively.
Create and interpret data models.
Identify factors contributing to learning success.

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 for only £129.

Order Your QLS Certificate

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

Skill Up Recognised Certificate

Upon successful completion of the Machine Learning Basics course, you have the opportunity to request a Skill Up Recognised Certificate. This certificate holds significant value, and its validation will endure throughout your lifetime.

1. PDF Certificate + PDF Transcript: £14.99
2. Hardcopy Certificate + Hardcopy Transcript: £19.99
3. Delivery Charge: £10.00 (Applicable for International Students)

CPD Quality Standards Accredited Certificate

After successfully completing the Machine Learning Basics course, you can apply for the CPD Quality Standards Accredited Certificate of Achievement.

1. PDF Certificate: £25.00
2. Hardcopy Certificate: £35.00
3. Delivery Charge: £10.00 (Applicable for International Students)

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Why should I take this course?

Gain foundational knowledge in Machine Learning.
Learn special techniques for data analysis.
Enhance your analytical skills.
Prepare for data-driven decision-making roles.
Open doors to diverse career opportunities.

Career Path

Requirements

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