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Machine learning model evaluation in Python

A practical course on how to evaluate the performance of a machine learning model using Python
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9.5

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10.0

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8.6

Content

Platform: Udemy
Video: 1h 18m
Language: English
Next start: On Demand

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CourseMarks Score®

9.5 / 10

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Freshness Score

10.0 / 10
This course was last updated on 5/2022.

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Content Score

8.6 / 10
Video Score: 7.7 / 10
The course includes 1h 18m 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: 8.7 / 10

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Extra Content Score: 9.5 / 10

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Table of contents

Description

In this practical course, we are going to focus on the performance evaluation of supervised machine learning models using Python programming language.
After a model has been trained or during hyperparameter tuning, we have to check its performance in order to assess whether it overfits or not. That’s why, according to particular projects and needs, we need to select performance metrics carefully. In fact, the choice of the wrong metrics may give us an unreliable model. On the contrary, using the proper performance indicators can lead our project to a higher value.
With this course, you are going to learn:
•Performance metrics for regression models (R-squared, Mean Absolute Error, Mean Absolute Percentage Error)
•Performance metrics for binary classification models (confusion matrix, precision, recall, accuracy, balanced accuracy, ROC curve and its area)
•Performance metrics for multi-class classification models (accuracy, balanced accuracy, macro averaged precision)
All the lessons of this course start with a brief introduction and end with a practical example in Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the Jupyter notebooks are downloadable.
This course is part of my Supervised Machine Learning in Python online course, so you’ll find some lessons that are already included in the larger course.

You will learn

✓ Regression metrics (R-squared, MAE, MAPE)
✓ Confusion matrix
✓ ROC curve and its area
✓ Precision, Recall, F1 score
✓ Accuracy, balanced accuracy

Requirements

• Python porgramming language

This course is for

• Python developers
• Data Scientists
• Computer engineers
• Researchers
• Students

How much does the Machine learning model evaluation in Python course cost? Is it worth it?

The course costs $14.99. And currently there is a 25% discount on the original price of the course, which was $49.99. So you save $35 if you enroll the course now.

Does the Machine learning model evaluation in Python course have a money back guarantee or refund policy?

YES, Machine learning model evaluation in 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 model evaluation in Python course, but there is a $35 discount from the original price ($49.99). So the current price is just $14.99.

Who is the instructor? Is Gianluca Malato a SCAM or a TRUSTED instructor?

Gianluca Malato has created 9 courses that got 130 reviews which are generally positive. Gianluca Malato has taught 7,737 students and received a 4.6 average review out of 130 reviews. Depending on the information available, Gianluca Malato is a TRUSTED instructor.
Your Data Teacher
My name is Gianluca Malato, I’m Italian and have a Master’s Degree cum laude in Theoretical Physics of disordered systems at “La Sapienza” University of Rome.
I’m a Data Scientist who has been working for years in the banking and insurance sector. I have extensive experience in software programming and project management and I have been dealing with data analysis and machine learning in the corporate environment for several years.
I am also skilled in data analysis (e.g. relational databases and SQL language), numerical algorithms (e.g. ODE integration, optimization algorithtms) and simulation (e.g. Monte Carlo techniques).
I’ve written many articles about Machine Learning, R and Python and I’ve been a Top Writer on Medium in Artificial Intelligence category.

9.5

CourseMarks Score®

10.0

Freshness

N/A

Feedback

8.6

Content

Platform: Udemy
Video: 1h 18m
Language: English
Next start: On Demand

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