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Machine Learning Basics

Machine Learning Basics

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About This Course

Educational accomplishments and income are closely correlated. Education and skill with endorsed certificates from credible and renowned authorities typically lead to better jobs with attractive salaries. Educated and skilled workers also have lower rates of unemployment. Therefore, skills and endorsed certificates to showcase are essential for people early in their careers.

Imperial Academy offers this premium Machine Learning Basics course to ensure security in your career. In addition, this comprehensive Machine Learning Basics course will assist you in building relevant skills that will help you find a job in the related sectors. Also, the Certificate you’ll get after completing the Machine Learning Basics will put your head and shoulder above others in front of potential employers.

Become the person who would attract the results you seek. What you plant now, you will harvest later. So, grab this opportunity and start learning Machine Learning Basics!

QLS/ CPD/ CIQ Accredited
24/7 Assistance from our Support Team
100% Online
Self-paced course
Bite-sized Audio-visual Modules
Rich Learning Materials
Developed by Industry Specialists
Career Guidance

Learn at your own pace from the comfort of your home, as the rich learning materials of this premium course is accessible from any place at any time. The advanced course curriculums are divided into tiny bite-sized audio-visual modules by industry specialists with years of experience behind them.

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