From the course: Machine Learning with Python: Decision Trees

Next steps with decision trees - Python Tutorial

From the course: Machine Learning with Python: Decision Trees

Next steps with decision trees

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- [Frederick] Congratulations. You now know what a decision tree is, how it's built, and when to use one. You've learned how to build, visualize, and prune a classification tree, as well as a regression tree in Python. The foundational knowledge and skills you've acquired in this course should serve as a stepping stone to continue learning about machine learning. Specifically, it should serve as a launchpad for solving more complex, supervised machine learning problems using decision trees. Here are a few recommended next steps. Decision trees are one of many supervised machine learning models we can build in Python. I encourage you to continue to explore other LinkedIn Learning courses that illustrate the use of different types of machine learning models. One such course is Machine Learning with Python, k-Means Clustering. Besides courses that explore other models, I also encourage you to explore courses that highlight the importance of ethics in data collection and use. An example of such a course is Data Ethics, Watching Out for Data Misuse. If you're interested in broadening your skillset into other languages, such as R, then also check out my book, "Practical Machine Learning in R." Finally, I recommend that you continue to practice what you've learned. Find new problems to solve, find interest in data sets on which to practice your new skills. The journey into the world of machine learning with Python is a lifelong one. Thanks for coming along with me on this journey. I'll see you next time.

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