Wrap-Up
We’ve now covered the basic terminology of machine learning. At its core, machine learning is a branch of Artificial Intelligence that enables machines to adapt their behavior based on new data.
We introduced the three fundamental types of machine learning: Supervised, Unsupervised, and Reinforcement Learning.
- Supervised Learning uses labeled datasets, where each input is paired with a known output. It’s primarily used for prediction and inference, including tasks like regression (predicting continuous values) and classification (predicting discrete categories).
- Unsupervised Learning deals with unlabeled data, and the goal is to uncover hidden patterns or groupings within the data. One of its main applications is clustering.
- Reinforcement Learning is different from the other two. Here, the agent learns by trial and error, guided by a delayed reward signal. This type of learning is ideal for problems involving sequential decision-making in environments with uncertainty, delayed feedback, and the need for exploration.
We also covered how to install Python and use the NumPy library—a powerful tool for array manipulation. You learned basic operations like creating arrays, indexing, slicing, and checking their size and shape.
So… What’s Next?
You might be thinking: “Where do I go from here?”
The truth is, applying machine learning can be easy. With modern libraries, you can load a pre-built algorithm, plug in your data, and get results with just a few lines of code.
But we’re not just here to use machine learning—we’re here to master it.
To truly understand how these algorithms work and why they behave the way they do, we need a strong mathematical foundation—especially in Linear Algebra and Probability Theory.
- Linear Algebra is the study of vectors, matrices, and the rules for manipulating them. It forms the backbone of most machine learning models.
- Probability Theory equips us with the tools to reason about uncertainty, a core aspect of learning from data.
For many, the math behind machine learning can seem intimidating. But don’t worry—we’ll take it step by step. With consistent effort and an open mindset, you’ll build the foundation you need.
Your Next Step
The best next step is to build a solid foundation in math, which will empower you to understand and implement machine learning algorithms in depth. You can also dive deeper into each learning type through dedicated courses—but keep in mind: these algorithms rely heavily on the math you’re about to learn.