
This course includes our updated coding exercises so you can practice your skills as you learn.
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Explore how regression uses historical car data to predict continuous outcomes like CO2 emissions, distinguishing dependent and independent variables and simple versus multiple regression.
Explore classification, a supervised learning method that uses labeled training data to assign test cases to categorical outcomes, enabling binary and multi-class predictions across finance and recognition tasks.
Learn how decision trees classify data and predict drug A or B using age, gender, pressure, blood pressure, and cholesterol by splitting data with attribute tests.
This course adopts a bootcamp-style learning approach, delivering essential information through hands-on labs and projects to enhance your understanding of the material. You can freely use the projects to enhance your resume or GitHub profile to boost your career.
In this module, you'll explore the applications of Machine Learning across various fields, including healthcare, banking, and telecommunications. You'll gain a broad understanding of Machine Learning concepts, such as supervised versus unsupervised learning, and how to implement Machine Learning models using Python libraries.
It is suitable for individuals who:
Need to quickly start working with Machine Learning, such as students.
Want to prepare themselves for work tasks or job interviews.
Have an interest in beginning their journey in Machine Learning, Deep Learning, AI, or Large Language Models like ChatGPT.
Requirements:
Firstly, don't be afraid to delve into unfamiliar topics just because of their titles; everything is achievable step by step.
The course has no specific prerequisites, but for the labs, it's helpful to have some basic knowledge of the Python programming language. If you're unfamiliar, the course provides guides to assist you.
Learning Objectives:
Provide examples of Machine Learning applications in different industries.
Outline the problem-solving steps used in Machine Learning.
Present examples of various machine learning techniques.
Describe Python libraries used in Machine Learning.
Explain the distinctions between Supervised and Unsupervised algorithms.
Describe the capabilities of different machine learning algorithms.