Overview
This comprehensive course on Machine Learning Basics will deepen your understanding on this topic.
After successful completion of this course you can acquire the required skills in this sector. This Machine Learning Basics comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.
So enrol in this course today to fast-track your career ladder.
How will I get my certificate?
You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.
Who is This course for?
There is no experience or previous qualifications required for enrolment on this Machine Learning Basics. It is available to all students, of all academic backgrounds.
Requirements
Our Machine Learning Basics is fully compatible with PC’s, Mac’s, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.
There is no time limit for completing this course, it can be studied in your own time at your own pace.
Career Path
Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to—
- Open doors of opportunities
- Increase your adaptability
- Keep you relevant
- Boost confidence
And much more!
Course Curriculum
- 10 sections
- 47 lectures
- 11 hours, 17 minutes total length
-
Introduction to Supervised Machine Learning 00:06:00
-
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
-
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
-
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
-
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
-
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
-
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
-
Data Cleaning: Part 1 00:16:00
-
Data Cleaning: Part 2 00:17:00
-
Creating New Features 00:12:00
-
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
-
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
Frequently Bought Together
Big Data Analytics with PySpark Tableau Desktop and MongoDB
4.7
(1 student)4.8 course rating - 15 reviews
£19.99
£419
This course includes:
-
Duration:11 hours, 17 minutes
-
Access:1 year access
-
Units:47
-
Level:Intermediate
-
CPD Points:10
-
Certificate:Yes
