Key features of the Machine Learning for Beginners
- Recognised qualification upon successful completion of the course
- Study from anywhere, anytime, whenever it is convenient for you.
- Get 24/7 support from our Customer Success Team
- Affordable and engaging e-learning study materials
- Study at your own pace from a tablet, PC or smartphone
- Online tutor support when you are in need.
Who is this course for?
There is no experience or previous qualifications required for enrolment on this course. It is available to all students, of all academic backgrounds.
Requirements
Our Machine Learning for Beginners is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation.
Career Path
After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market.
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 | ||
| Certificate and Transcript | |||
| Order Your Certificates or Transcripts | 00:00:00 | ||
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