When you have a basic understanding of machine learning concepts, you want to systematize everything: what types of tasks exist, which models to use for them, and how it all connects.
🔹 At the start, it's worth understanding the main types of tasks:
- Classification - when you need to determine a category (e.g., spam / not spam).
- Regression - when predicting a numerical value (e.g., apartment price).
- Clustering - when there is a lot of data but no labels, and the data needs to be grouped somehow.
🔹 Then it's logical to move on to key models: linear models, trees, ensembles. It's important not just to "know the names" but to understand in which cases each works best, how it learns, what data it takes as input, and what its strengths and weaknesses are.
📈 Once this stage is completed, you can safely move on to neural networks.
This is where the most interesting part begins: how a network is built, how it learns, what architectures exist, and why transformers have become so popular.
💡 In fact, neural networks are not "something separate" but simply a logical continuation of basic machine learning algorithms.
Such a gradual transition - from classics to modern architectures - helps not to get lost and to approach the next topic consciously: LLM (Large Language Models).
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👩💻 Data Flow
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