Conclusion

Congratulations on completing this course on Machine Learning!

What we’ve learned

We’ve covered a lot of ground in this course. We started with the basic definitions of machine learning and then explored the different categories of ML. We learned about the process of training and testing a model.

Then, we got our hands dirty and built five different machine learning models from scratch:

• Linear Regression
• Logistic Regression
• Naive Bayes
• K-Nearest Neighbors
• K-Means Clustering

You now have a solid foundation in the core concepts of machine learning.

Where to go from here?

This course was just the beginning. The field of machine learning is vast and constantly evolving. Here are a few ideas for what you can do next:

• Experiment! Try changing the parameters of the models we built. See how it affects their performance.
• Find your own data. Find a dataset that you’re interested in and try to build a model to make predictions on it.
• Learn about other algorithms. There are many other machine learning algorithms to explore, like Decision Trees, Support Vector Machines (SVMs), and Neural Networks.
• Dive deeper into the math. If you’re interested, you can learn more about the mathematical principles that underpin these algorithms.

The most important thing is to stay curious and keep learning. The world of machine learning is at your fingertips. Good luck!