This comprehensive course is designed to develop the knowledge and skills to effectively utilize the scikit-learn library in Python for machine learning tasks. It is an excellent resource to help you develop practical machine learning applications using Python and scikit-learn.
In this course, you’ll learn fundamental concepts such as supervised and unsupervised learning, data preprocessing, and model evaluation. You’ll also learn how to implement popular machine learning algorithms, including regression, classification, and clustering, using scikit-learn’s user-friendly API. The course also introduces advanced topics such as ensemble methods, model interpretation, and hyperparameter optimization.
After taking this course, you’ll gain hands-on experience in applying machine learning techniques to solve diverse data-driven problems. You’ll also be equipped with the expertise to confidently leverage scikit-learn for a wide range of machine learning applications in industry as well as academia.
This comprehensive course is designed to develop the knowledge and skills to effectively utilize the scikit-learn library in Pyt...Show More
WHAT YOU'LL LEARN
An understanding of data preprocessing steps
Proficiency in model selection and evaluation
Implementation level skills for designing supervised learning algorithms
An insight into unsupervised learning techniques
Working knowledge of hyperparameter tuning and optimization
An understanding of data preprocessing steps
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Content
1.
Course Overview
1 Lessons
Get familiar with fundamental machine learning concepts, data preprocessing, techniques, and model evaluation using scikit-learn.
2.
Introduction to Machine Learning
7 Lessons
Look at core machine learning principles, process steps, and using scikit-learn for practical applications.
3.
Preprocessing
10 Lessons
Break apart preprocessing techniques like feature extraction, scaling, encoding, and imputation for data preparation.
4.
Supervised Learning
11 Lessons
Apply your skills to train and evaluate supervised learning models using key algorithms and techniques.
5.
Unsupervised Learning
8 Lessons
Explore clustering techniques for uncovering patterns in unlabeled data using unsupervised learning.
6.
Model Evaluation
9 Lessons
See how it works to evaluate machine learning models through metrics, cross-validation, and real-world application.
7.
Tips and Tricks
8 Lessons
Master the strategies for enhancing machine learning workflows with scikit-learn.
8.
Conclusion
1 Lessons
Learn how to use scikit-learn to build, evaluate, and improve machine learning models.
Certificate of Completion
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Course Author:
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