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Create Your Own Datasets

with Google Colab and sklearn
0.0
0.0/5
(0 reviews)
1 students
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9.1

CourseMarks Score®

10.0

Freshness

N/A

Feedback

7.8

Content

Platform: Udemy
Video: 4h 34m
Language: English
Next start: On Demand

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Detailed Analysis

CourseMarks Score®

9.1 / 10

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

Freshness Score

10.0 / 10
This course was last updated on 1/2022.

Course content can become outdated quite quickly.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

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Content Score

7.8 / 10
Video Score: 8.3 / 10
The course includes 4h 34m 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.
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.
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Table of contents

Description

In this course the student will learn how to use Google Colab and Python’s machine learning library, sklearn, to create datasets and use them in machine learning enterprises.
The datasets will be created in sklearn and they are comprised of classifications and regressions, being twenty in total.
When the datasets have been created, machine learning techniques will be employed to make predictions on the labels. In addition, the concepts of supervised and unsupervised learning will be discussed. Although most of the examples will be of supervised learning, clustering will be brushed upon.
Some of the datasets introduce noise into the system, and this will decrease accuracy of predictions. The student will be shown how to tune the parameters of the appropriate datasets to reduce noise and thereby improve accuracy of the predictions. This proves that noise has an inverse relationship to accuracy of the model.
Some of the datasets will have outliers, so methods for reducing outliers in the dataset will also be discussed. When the outliers are removed, accuracy of the predictions are also likely to be increased. This proves that outliers have an inverse relationship to accuracy of the model.
The student will be taken through the following steps to create a dataset and write a program to make predictions on the labels:-
1. Import libraries.
2. Create dataset.
3. Plot a graph of the dataset so it can be seen in the computer’s memory.
4. Analyse the label.
5. Remove outliers if necessary.
6. Normalise or standardise the independent variable if necessary.
7. Split the dataframe into training and validation sets.
8. Select the model.
9. Train and fit the training set into the model.
10. Make predictions on the validation set.
11. Check accuracy and / or error of the predictions.
12. Compare the predictions against the actual values.
13. Plot the predictions on a graph.

You will learn

✓ Students will learn how to create their own datasets using Google Colab and sklearn.
✓ Students will be given an introduction to Google Colab, which they will use to write their own programs.
✓ Students will be given an introduction to Python’s machine learning library, sklearn, which they will need in order to create their own datasets.
✓ Students will learn how to create the twenty datasets that are included in sklearn.

Requirements

• The equipment needed for this course is a computer with an internet connection
• A prerequisite to this course is the course I have created, “Use Google Colab to learn Python programming”.

This course is for

• This course is designed for anyone who would like to create their own datasets using sklearn, which is Python’s machine learning library.

How much does the Create Your Own Datasets course cost? Is it worth it?

The course costs $12.99. And currently there is a 35% discount on the original price of the course, which was $19.99. So you save $7 if you enroll the course now.

Does the Create Your Own Datasets course have a money back guarantee or refund policy?

YES, Create Your Own Datasets 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 Create Your Own Datasets course, but there is a $7 discount from the original price ($19.99). So the current price is just $12.99.

Who is the instructor? Is Tracy Renee a SCAM or a TRUSTED instructor?

Tracy Renee has created 8 courses that got 12 reviews which are generally positive. Tracy Renee has taught 2,003 students and received a 4.2 average review out of 12 reviews. Depending on the information available, Tracy Renee is a TRUSTED instructor.
Data Scientist
I have almost five decades experience in work, to include United States Air Force, the corporate sector, and non profit sectors, and charities. I also have a BA in Computer Studies, a MSc in Finance, and have a Diploma in Accounting through the AAT. My hobbies include data science, creating content on social media, and writing.

Follow Tracyrenee on medium publications

9.1

CourseMarks Score®

10.0

Freshness

N/A

Feedback

7.8

Content

Platform: Udemy
Video: 4h 34m
Language: English
Next start: On Demand

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