When you enroll through our links, we may earn a small commission—at no extra cost to you. This helps keep our platform free and inspires us to add more value.
AI Bootcamp: Beginner to Expert in Machine Learning 2024
The Ultimate Machine Learning Journey: From Beginner to Expert with a Step-by-Step Guide in Python
This Course Includes
udemy
5 (12 reviews )
2h 43m 
english
Online - Self Paced
professional certificate
Udemy
About AI Bootcamp: Beginner to Expert in Machine Learning 2024
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.
What You Will Learn?
- Bootcamp-style course: Hands-on labs, projects boost understanding. Use projects for resume/GitHub profile to advance career. .
- 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. .
- 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. Show moreShow less.