Name: Machine Learning Basics
Price: 20 GBP
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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. While this comprehensive training is popular for preparing people for job opportunities in the relevant fields, it also helps to advance your career for promotions.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Accreditation

All of our courses, including this Machine Learning Basics, are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.

Course Curriculum

The detailed curriculum outline of our Machine Learning Basics is as follows:

Section 01: Introduction

Introduction to Supervised Machine Learning

Section 02: Regression

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

Section 03: Predictors

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?

Section 04: Minitab

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

Section 05: Regression Trees

The Basic idea of Regression Trees
Regression Trees with Minitab. Example. Bike Sharing: Part 1
Regression Trees with Minitab. Example. Bike Sharing: Part 2

Section 06: Binary Logistics Regression

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

Section 07: Classification Trees

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

Section 08: Data Cleaning

Data Cleaning: Part 1
Data Cleaning: Part 2
Creating New Features

Section 09: Data Models

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 Duration: A More Detailed Error Analysis
The “Just Right” Model for TotalCharge
The “Just Right” Model for ToralCharge: A More Detailed Error Analysis

Section 10: Learning Success

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 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