You're interested in machine learning. What can you do to get a better grasp of it?

1 Learn the basics

Before diving into the advanced topics of machine learning, you need to have a solid foundation of the basic concepts and techniques. You should familiarize yourself with the types of machine learning problems, such as supervised, unsupervised, and reinforcement learning, and the common algorithms, such as linear regression, logistic regression, k-means clustering, and neural networks. You should also learn how to evaluate the performance of machine learning models, such as using accuracy, precision, recall, and confusion matrix.

3 Explore real-world datasets

One of the best ways to learn machine learning is to apply it to real-world datasets and problems. You can find many open-source datasets on platforms such as Kaggle, UCI Machine Learning Repository, and Google Dataset Search. You can also use your own data or data from your industry or domain. You should try to explore the data, understand its features and distributions, preprocess it, and apply different machine learning algorithms to it. You should also compare and analyze the results and draw insights from them.

4 Join online communities

Learning machine learning can be challenging and sometimes frustrating, especially if you encounter errors or bugs that you don't know how to fix. That's why it's helpful to join online communities where you can ask questions, share your projects, and learn from others. Some of the most active and supportive communities for machine learning are Stack Overflow, Reddit, Quora, Medium, and Twitter. You can also join online forums and groups that are specific to your field or interest, such as fintech, trading, or banking.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?