AI Bootcamp: Beginner to Expert in Machine Learning 2024

AI Bootcamp: Beginner to Expert in Machine Learning 2024, available at $54.99, has an average rating of 4.5, with 28 lectures, 13 quizzes, based on 6 reviews, and has 2046 subscribers.

You will learn about 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. This course is ideal for individuals who are Need to quickly start working with Machine Learning, such as students. or Want to prepare themselves for work tasks or job interviews. or Have an interest in beginning their journey in machine learning, deep learning, AI, or large language models like ChatGPT. It is particularly useful for Need to quickly start working with Machine Learning, such as students. or Want to prepare themselves for work tasks or job interviews. or Have an interest in beginning their journey in machine learning, deep learning, AI, or large language models like ChatGPT.

Enroll now: AI Bootcamp: Beginner to Expert in Machine Learning 2024

Summary

Title: AI Bootcamp: Beginner to Expert in Machine Learning 2024

Price: $54.99

Average Rating: 4.5

Number of Lectures: 28

Number of Quizzes: 13

Number of Published Lectures: 27

Number of Published Quizzes: 13

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 40

Original Price: $89.99

Quality Status: approved

Status: Live

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.

Who Should Attend

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.

Target Audiences

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.

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 Pythonprogramming 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.

Course Curriculum

Chapter 1: Welcome and Introduction

Lecture 1: Introduction

Lecture 2: Welcome

Lecture 3: Optimizing Your Video Watching Experience

Chapter 2: What is machine learning

Lecture 1: Introduction to Machine Learning

Lecture 2: Python for Machine Learning

Lecture 3: Introduction to Programming with Python

Lecture 4: Supervised vs Unsupervised

Chapter 3: Embrace the Learning Journey

Lecture 1: Join Our Supportive Learning Community on Telegram

Chapter 4: Linear Regression

Lecture 1: Introduction to Regression

Lecture 2: Simple Linear Regression

Lecture 3: Model Evaluation in Regression Models

Lecture 4: Evaluation Metrics in Regression Models

Lecture 5: Multiple Linear Regression

Chapter 5: K-Nearest Neighbours

Lecture 1: Introduction to Classification

Lecture 2: K-Nearest Neighbours

Lecture 3: Evaluation Metrics in Classification

Chapter 6: Decision Trees

Lecture 1: Introduction to Decision Trees

Lecture 2: Building Decision Trees

Chapter 7: Regression Trees

Lecture 1: Regression Trees

Chapter 8: Logistic Regression

Lecture 1: Intro to Logistic Regression

Lecture 2: Logistic Regression vs Linear Regression

Lecture 3: Logistic Regression Training

Chapter 9: Support Vector Machine

Lecture 1: Support Vector Machine

Chapter 10: Multiclass Prediction

Lecture 1: Multiclass Prediction

Chapter 11: Clustering

Lecture 1: Intro to Clustering

Lecture 2: k-Means Clustering

Lecture 3: More on k-Means

Instructors

Soroush Arab
Senior Embedded Systems Engineer

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 2 votes
5 stars: 3 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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