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Machine Learning Basics Course Highlights
Machine Learning Basics Course HighlightsStart 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
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 (Transcripts Included): £49.99
2. Hard Copy Certificate (Transcripts Included): £79.00
3. Delivery Charge: £10.00 (Applicable for International Students)
Course media
Why should I take this course?
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
Requirements- Basic understanding of statistics.
- Familiarity with programming concepts.
- Access to Minitab software.
Machine Learning Basics Reviews
A well-organised and highly valuable course with clear, easy-to-understand guidance throughout. I’ve gained knowledge that’s directly relevant to my day-to-day responsibilities. It’s given me greater confidence in applying these skills professionally.
Engaging content delivered in a straightforward and structured format. The examples were realistic and helped reinforce key concepts effectively. I would certainly recommend it to colleagues looking to upskill
Comprehensive, insightful and professionally presented from start to finish. The course materials were clear and well supported. A worthwhile investment for anyone serious about career development
Curriculum
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Introduction to Supervised Machine Learning
00:06:00
00:06:00 -
Introduction to Regression
00:13:00
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Evaluating Regression Models
00:11:00
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Conditions for Using Regression Models in ML versus in Classical Statistics
00:21:00
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Statistically Significant Predictors
00:09:00
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Regression Models Including Categorical Predictors. Additive Effects
00:20:00
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Regression Models Including Categorical Predictors. Interaction Effects
00:18:00
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Multicollinearity among Predictors and its Consequences
00:21:00
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Prediction for New Observation. Confidence Interval and Prediction Interval
00:06:00
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Model Building. What if the Regression Equation Contains “Wrong” Predictors?
00:13:00
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Stepwise Regression and its Use for Finding the Optimal Model in Minitab
00:13:00
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Regression with Minitab. Example. Auto-mpg: Part 1
00:17:00
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Regression with Minitab. Example. Auto-mpg: Part 2
00:18:00
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The Basic idea of Regression Trees
00:18:00
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Regression Trees with Minitab. Example. Bike Sharing: Part 1
00:15:00
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Regression Trees with Minitab. Example. Bike Sharing: Part 2
00:10:00
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Introduction to Binary Logistics Regression
00:23:00
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Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC
00:20:00
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Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1
00:16:00
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Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2
00:18:00
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Introduction to Classification Trees
00:12:00
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Node Splitting Methods 1. Splitting by Misclassification Rate
00:20:00
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Node Splitting Methods 2. Splitting by Gini Impurity or Entropy
00:11:00
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Predicted Class for a Node
00:06:00
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The Goodness of the Model – 1. Model Misclassification Cost
00:11:00
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The Goodness of the Model – 2 ROC. Gain. Lit Binary Classification
00:15:00
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The Goodness of the Model – 3. ROC. Gain. Lit. Multinomial Classification
00:08:00
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Predefined Prior Probabilities and Input Misclassification Costs
00:11:00
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Building the Tree
00:08:00
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Classification Trees with Minitab. Example. Maintenance of Machines: Part 1
00:17:00
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Classification Trees with Miitab. Example. Maintenance of Machines: Part 2
00:10:00
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Data Cleaning: Part 1
00:16:00
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Data Cleaning: Part 2
00:17:00
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Creating New Features
00:12:00
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Polynomial Regression Models for Quantitative Predictor Variables
00:20:00
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Interactions Regression Models for Quantitative Predictor Variables
00:15:00
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Qualitative and Quantitative Predictors: Interaction Models
00:28:00
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Final Models for Duration and TotalCharge: Without Validation
00:18:00
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Underfitting or Overfitting: The “Just Right Model”
00:18:00
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The “Just Right” Model for Duration
00:16:00
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The “Just Right” Model for Duration: A More Detailed Error Analysis
00:12:00
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The “Just Right” Model for TotalCharge
00:14:00
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The “Just Right” Model for ToralCharge: A More Detailed Error Analysis
00:06:00
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Regression Trees for Duration and TotalCharge
00:18:00
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Predicting Learning Success: The Problem Statement
00:07:00
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Predicting Learning Success: Binary Logistic Regression Models
00:16:00
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Predicting Learning Success: Classification Tree Models
00:09:00
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Submit Your Assignment & Order QLS Certificate
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Skill Up Recognised Certificate
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Order CPDQS Certificate
Offer Ends in
TAKE ALL COURSES FOR £99
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Duration:11 hours, 17 minutes
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Access:1 Year
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Units:50
