Machine Learning Basics
Discover the fundamentals of Machine Learning. Dive into Binary Logistics Regression, Predictors, Minitab, classification trees, and more to unlock the power of data-driven insights.
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.
An extra £10 postage charge will be required for students leaving overseas.
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)
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: £9.99
- 2. Hardcopy Certificate + Hardcopy Transcript: £14.99
- 3. Delivery Charge: £10.00 (Applicable for International Students)
Course media
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
- Data Analyst
- Machine Learning Engineer
- Business Intelligence Analyst
- Data Scientist
- AI Researcher
- Data Engineer
Requirements
- Basic understanding of statistics.
- Familiarity with programming concepts.
- Access to Minitab software.
Course Curriculum
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 | ||
| Module 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 | ||
| Submit Your Assignment & Order QLS Certificate | |||
| Submit Your Assignment & Order QLS Certificate | 00:00:00 | ||
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1 Year Access 
1 Students 
11 hours, 17 minutes