Machine Learning Basics
Machine Learning Basics
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Course Overview
Become a trained professional from the safety and comfort of your own home by taking Machine Learning Basics . Whatever your situation and requirements, One Education can supply you with professional teaching, gained from industry experts, and brought to you for a great price with a limited-time discount.
One Education has been proud to produce an extensive range of best-selling courses, and Machine Learning Basics is one of our best offerings. It is crafted specially to promote easy learning at any location with an online device. Each topic has been separated into digestible portions that can be memorised and understood in the minimum of time.
Teaching and training are more than just a job for the staff at One Education; we take pride in employing those who share our vision for e-learning and its importance in today’s society. To prove this, all learning materials for each course are available for at least one year after the initial purchase.
All of our tutors and IT help desk personnel are available to answer any questions regarding your training or any technical difficulties.
By completing Machine Learning Basics, you will have automatically earnt an e-certificate that is industry-recognised and will be a great addition to your competencies on your CV.
Whatever your reason for studying Machine Learning Basics, make the most of this opportunity from One Education and excel in your chosen field.
Please be aware that there are no hidden fees, no sudden exam charges, and no other kind of unexpected payments. All costs will be made very clear before you even attempt to sign up.
Course Benefits
- Learning experience gained rapidly in related fields
- Quick assessment and guidance for all assignments
- Certificate for proof of acquired skills and knowledge
- Freedom to study in any location and at a pace that suits you
- Support from dedicated tutors committed to online learning
Will I receive a certificate of completion?
After successfully completing this course, you will qualify for the CPD Certificate from One Education as proof of your continued expert development. Certificate is available in PDF format, at the cost of £9, or a hard copy can be sent to you via post, at the cost of £15.
Why Study This Course
It doesn’t matter if you are an aspiring professional or absolute beginner; this course will enhance your expertise and boost your CV with critical skills and an accredited certification attesting to your knowledge.
The Machine Learning Basics is fully available to anyone, and no previous qualifications are needed to enrol. All One Education needs to know is that you are eager to learn and are over 16.
Course Curriculum
| 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: Part 1 | 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 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 | ||
| 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 | 00:16:00 | ||
| Predicting Learning Success: Classification Tree Models | 00:09:00 | ||
| Order Your Certificate | |||
| Order Your Certificate Now | 00:00:00 | ||
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