Machine Learning Basics

Home
Course
IT & Software
Machine Learning Basics

Level: Intermediate
Modules: 47 Modules
Duration: 11 hours, 18 minutes
Students: 5

Embark on a captivating journey into the world of artificial intelligence with our course, “Machine Learning Basics.” This voyage begins with an immersive introduction, setting the stage for an exploration into the intricate and fascinating realm of machine learning. Envision yourself unlocking the mysteries of algorithms and data patterns, essential skills in today’s technology-driven landscape. The course offers a comprehensive foray into the core principles of machine learning, starting from the very basics and gradually building to more complex concepts, making it an ideal path for beginners and enthusiasts alike.

As you delve deeper, each section unravels a vital component of machine learning. Grasp the essentials of regression analysis, understand the role of predictors, and navigate through the functionalities of Minitab, a key tool in data analysis. Journey through the structured world of regression trees and binary logistic regression, and master the art of classification trees. The course also emphasizes the importance of data cleaning and constructing robust data models, culminating in the achievement of learning success. This course is not just an educational experience; it’s a gateway to the future of data science and AI.

Learning Outcomes

Comprehend the basic principles and applications of machine learning.
Develop proficiency in regression analysis and predictor identification.
Gain practical skills in Minitab for data analysis.
Understand and apply regression and classification trees.
Acquire expertise in data cleaning and model creation.

Why choose this Machine Learning Basics course?

Unlimited access to the course for a lifetime.
Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course.
Structured lesson planning in line with industry standards.
Immerse yourself in innovative and captivating course materials and activities.
Assessments designed to evaluate advanced cognitive abilities and skill proficiency.
Flexibility to complete the Course at your own pace, on your own schedule.
Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience.
Unlock career resources for CV improvement, interview readiness, and job success.

Who is this Machine Learning Basics course for?

Novices eager to delve into machine learning.
Data enthusiasts looking to enhance their analytical skills.
Professionals in IT and related fields expanding their expertise.
Academics and students in computer science and data studies.
Career changers interested in the field of data science and AI.

Career path

Data Analyst – £30,000 to £55,000
Machine Learning Engineer – £40,000 to £80,000
AI Developer – £35,000 to £75,000
Business Intelligence Analyst – £32,000 to £60,000
Research Scientist (Machine Learning) – £45,000 to £85,000
Software Engineer (AI Specialization) – £38,000 to £70,000

Prerequisites

This Machine Learning Basics does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Machine Learning Basics was made by professionals and it is compatible with all PC’s, Mac’s, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.

Certification

After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8.

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: Part1	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	00:17:00
	Predicting Learning Success: Classification Tree Models	00:09:00