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via Udemy |
Go to Course: https://www.udemy.com/course/machine-learning-model-evaluation-in-python/
Certainly! Here's a comprehensive review and recommendation for the Coursera course on performance evaluation of supervised machine learning models: --- **Course Review: Performance Evaluation of Supervised Machine Learning Models in Python** This practical Coursera course offers a thorough exploration of evaluating the performance of supervised machine learning models using Python, with a focus on real-world applications. Designed for data scientists, machine learning practitioners, and students, the course emphasizes the importance of selecting appropriate performance metrics to ensure reliable and effective models. **Course Content & Structure:** The course effectively covers key performance metrics for various types of models: - **Regression Models:** R-squared, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) - **Binary Classification Models:** Confusion matrix, precision, recall, accuracy, balanced accuracy, ROC curve, and its area - **Multi-class Classification Models:** Accuracy, balanced accuracy, and macro-averaged precision Each lesson begins with a concise introduction and concludes with practical examples implemented in Python, specifically using the powerful scikit-learn library. Participants will work in a Jupyter notebook environment, a standard in the data science industry, with downloadable notebooks for hands-on practice. **Strengths:** - Practical, hands-on approach - Focus on selecting the right metrics for different project needs - Clear explanations with real-world examples - Use of Jupyter notebooks for an industry-standard environment - Modular content that complements the larger "Supervised Machine Learning in Python" course **Who Should Enroll:** - Data scientists and machine learning engineers looking to refine their evaluation skills - Students and beginners wanting a practical understanding of performance metrics - Anyone involved in model selection or hyperparameter tuning **Recommendation:** I highly recommend this course for anyone seeking to deepen their understanding of model evaluation in machine learning. The emphasis on real-world examples and practical coding exercises makes it an invaluable resource for ensuring your models are both accurate and reliable. Whether you're working on regression, binary classification, or multi-class problems, this course will equip you with the essential tools to assess and improve your models effectively. --- Would you like a summary version for social media, or a more detailed, technical review?
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.