Applied Machine Learning: Algorithms - Matt Harrison (Learn Commonly Used Machine Learning Algorithms)

In this course, "Applied Machine Learning: Algorithms - Matt Harrison, working professionals begin a detailed analysis of critical machine learning algorithms. Although Matt pays relatively less attention to deep learning strategies, he thoroughly explains PCA, clustering, linear and logistic regressions, decision trees, random forests, and gradient boosting.

Through practical challenges and solutions available in GitHub Codespaces, participants of "Applied Machine Learning: Algorithms - Matt Harrison” not only teach readers the details, concepts, and basics of these algorithms but also give them some practical experience with how these algorithms should be implemented in different applications.

Applied Machine Learning: Algorithms - Matt Harrison Table of Contents:

1. Introduction
2. Clustering
3. PCA
4. Linear Regression
5. Logistic Regression
6. Decision Trees
7. Conclusion

Who is this course for?

1. Researchers, data scientists & analysts who are interested in understanding more about the mathematical algorithms used in machine learning.
2. Practical ML techniques i.e. those that can be effectively applied in business and other real life contexts.
3. The target audience is anyone interested in enhancing their ability to efficiently utilize non-deep learning algorithms.

Click on the links below to Download Applied Machine Learning: Algorithms - Matt Harrison!