Machine Learning Basics
Machine Learning Basics
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Description
Machine Learning Basics teaches you everything on the topic thoroughly from scratch so you can achieve a professional certificate for free to showcase your achievement in professional life. This Machine Learning Basics is a comprehensive, instructor-guided course, designed to provide a detailed understanding of the nature of the related sector and your key roles within it.
To become successful in your profession, you must have a specific set of skills to succeed in today’s competitive world. In this in-depth training course, you will develop the most in-demand skills to kickstart your career, as well as upgrade your existing knowledge & skills.
The training materials of this course are available online for you to learn at your own pace and fast-track your career with ease.
Sneak Peek
Who Should Take the Course
Anyone with a knack for learning new skills can take this Machine Learning Basics
Certification
Once you’ve successfully completed your course, you will immediately be sent a digital certificate.
Accreditation
All of our courses, including this Machine Learning Basics, are fully accredited.
Course Curriculum
This section provides an in-depth breakdown of the course structure, topics covered, and what students can expect from each module.
- Introduction to Supervised Machine Learning
- Introduction to Regression
- Evaluating Regression Models
- Conditions for Using Regression Models in ML versus in Classical Statistics
- Statistically Significant Predictors
- Regression Models Including Categorical Predictors. Additive Effects
- Regression Models Including Categorical Predictors. Interaction Effects
- Multicollinearity among Predictors and its Consequences
- Prediction for New Observation. Confidence Interval and Prediction Interval
- Model Building. What if the Regression Equation Contains “Wrong” Predictors?
- Stepwise Regression and its Use for Finding the Optimal Model in Minitab Regression with Minitab. Example. Auto-mpg: Part 1
- Regression with Minitab. Example. Auto-mpg: Part 2
- The Basic idea of Regression Trees Regression Trees with Minitab. Example. Bike Sharing: Part 1
- Regression Trees with Minitab. Example. Bike Sharing: Part 2
- Introduction to Binary Logistics Regression Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC
- Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1
- Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2
- Introduction to Classification Trees
- Node Splitting Methods 1. Splitting by Misclassification Rate
- Node Splitting Methods 2. Splitting by Gini Impurity or Entropy
- Predicted Class for a Node
- The Goodness of the Model – 1. Model Misclassification Cost
- The Goodness of the Model – 2 ROC. Gain. Lit Binary Classification
- The Goodness of the Model – 3. ROC. Gain. Lit. Multinomial Classification
- Predefined Prior Probabilities and Input Misclassification Costs
- Building the Tree
- Classification Trees with Minitab. Example. Maintenance of Machines: Part 1
- Classification Trees with Miitab. Example. Maintenance of Machines: Part 2
- Data Cleaning: Part 1
- Data Cleaning: Part 2
- Creating New Features
- Polynomial Regression Models for Quantitative Predictor Variables
- Interactions Regression Models for Quantitative Predictor Variables
- Qualitative and Quantitative Predictors: Interaction Models
- Final Models for Duration and TotalCharge: Without Validation
- Underfitting or Overfitting: The “Just Right Model”
- The “Just Right” Model for Duration
- The “Just Right” Model for TotalCharge
- The “Just Right” Model for Duration: A More Detailed Error Analysis
- Regression Trees for Duration and TotalCharge
- Predicting Learning Success: The Problem Statement
- Predicting Learning Success: Binary Logistic Regression Models
- Predicting Learning Success: Classification Tree Models
Course Rating
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