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Machine Learning with SciKit-Learn with Python

Get a practical understanding of the Scikit-Learn library and learn the ML implementation
3.0
3.0/5
(95 reviews)
27,626 students
Created by

7.7

CourseMarks Score®

9.5

Freshness

5.0

Feedback

8.0

Content

Platform: Udemy
Video: 8h 23m
Language: English
Next start: On Demand

Top Scikit-learn courses:

Detailed Analysis

CourseMarks Score®

7.7 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

9.5 / 10
This course was last updated on 7/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

5.0 / 10
We analyzed factors such as the rating (3.0/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

8.0 / 10
Video Score: 8.8 / 10
The course includes 8h 23m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 4 hours 17 minutes of 4 Scikit-learn courses on Udemy.
Detail Score: 9.6 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 5.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
0 resource.
0 exercise.
0 test.

Table of contents

Description

The goal of this course is to help the trainee’s expertise working with the python based Scikit-learn library. This training will enable one to implement the concepts of Machine learning using applications by the virtue of Scikit-learn. The sole purpose of this course is to provide a practical understanding of the Scikit-learn library to the trainees. After completing this training, the trainees will be able to endure the application development that requires ML implementation using the Scikit-learn library. In this unit, you will be getting a brief introduction of the concept which includes all the basic details together with the topics that are important to understand. You will understand how this library helps the application by helping the developers in adding machine learning-based concepts. After the mid part of the video, you will be learning about the topics that fall under the court of advanced level concepts. After this unit, you will be able to work to implement the concepts of Machine learning with the help of SciKit-Learn.
Scikit-learn can be defined as the python based library which is used to implement the concepts of machine learning in the application. It could also be explained as the predefined set of functions that is leveraged to bring the features in the application which are considered linked with machine learning. It is the library that consists of various tools for statistical modeling and machine learning. Regression, clustering, and classification are some of the most useful tools that could be found in this library. It is built on top of NumPy, SciPy, and Matplotlib which is one of the reason behind the functions it provides. Being based on python, it will only be supported while implementing things using the python programming language. It can be used the same way as other libraries are used in python but the features it will offer will be unique and focused on Machine learning.

You will learn

✓ This Scikit-learn Training has been designed in a manner so that it can contain all the topics that the trainees have to expertise so that they can work effectively with this library. At the starting of the course, you will get to learn about Machine Learning with SciKit-Learn which is one of the important components of this course where you will be learning every single thing about SciKit-Learn.
✓ You will be getting deep exposure to python in this training. Once you are done with this course, you will be possessing an ample skillset to work efficiently with the SciKit-Learn library.

Requirements

• Several topics or concepts are there for which you should have a basic understanding of to make the learning of this library easy for you. The very first thing is the basics of python. As this library is entirely based on python, the trainees need to have a basic understanding of the concepts of python. If you would have worked with python, you will find the concepts covered here pretty simple.
• The next important concept is the basics of Machine learning. With the help of this library, we will be implementing the concepts of ML. So it is very necessary to understand how it could be used. In this Scikit-learn Training, we have included all the topics that we are considering as the prerequisite here so that the trainees can brush up their understanding before beginning this training.

This course is for

• This course is open to all who want to master working with this library. We have developed the course in a manner so that I could have something for any sort of audience. The students who want to grow their career in python and want to learn about this library can be the best target audience for this course.
• The developers who are working in other programming languages and want to jump to Python to begin working with Machine learning can be the best target audience for this course. They will be learning about this library in a very detailed manner and will also learn how to implement this in python.
• The educators who are training folks in python or machine learning can also be the best target audience for this Scikit-learn Training. They will be learning about this library very deeply and will be able to deliver their understanding to their trainees.

How much does the Machine Learning with SciKit-Learn with Python course cost? Is it worth it?

The course costs $14.99. And currently there is a 82% discount on the original price of the course, which was $84.99. So you save $70 if you enroll the course now.
The average price is $12.2 of 4 Scikit-learn courses on Udemy.

Does the Machine Learning with SciKit-Learn with Python course have a money back guarantee or refund policy?

YES, Machine Learning with SciKit-Learn with Python has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Machine Learning with SciKit-Learn with Python course, but there is a $70 discount from the original price ($84.99). So the current price is just $14.99.

Who is the instructor? Is Exam Turf a SCAM or a TRUSTED instructor?

Exam Turf has created 36 courses that got 1,512 reviews which are generally positive. Exam Turf has taught 137,045 students and received a 4.0 average review out of 1,512 reviews. Depending on the information available, Exam Turf is a TRUSTED instructor.
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7.7

CourseMarks Score®

9.5

Freshness

5.0

Feedback

8.0

Content

Platform: Udemy
Video: 8h 23m
Language: English
Next start: On Demand

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