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Machine Learning Basics
By Studyhub UK
(3)- 30 Day Money Back Guarantee
- Completion Certificate
- 24/7 Technical Support
Highlights
On-Demand course
11 hours 18 minutes
All levels
Description
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 |
About The Provider
Studyhub UK
London, England
(3)Studyhub is a premier online learning platform which aims to help individuals worldwide to realise their educational dreams. For 5 years, we have been dedicated...
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