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.

Who this course is for:

• 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.