Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
AI Bootcamp: Beginner to Expert in Machine Learning 2024
Rating: 4.8 out of 5(14 ratings)
2,059 students

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
Created bySoroush Arab
Last updated 4/2024
English

What you'll 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.

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

See a demo
Image of coding exercise example

Course content

11 sections27 lectures2h 43m total length
  • Introduction3:47
  • Welcome2:16
  • Optimizing Your Video Watching Experience0:59

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.

Description

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.