Machine Learning (ML)
Machine learning is a science of getting computer systems to act without being explicitly programmed. Instead of writing rules, you feed data to an algorithm, and it figures out the rules itself.
At-a-Glance
The Godfather: Arthur Samuel in 1959 while he was at IBM wrote a program that learned to play checkers.
Black Box: In Deep Learning (modern ML), often even the creators don't know exactly why the model made a specific decision.
Data is King: An average algorithm with great data will almost always beat a great algorithm with average data.
ELI5 (Explain Like I'm 5)
You don't define anything. You just show the computer 10,000 pictures of cats and 10,000 pictures of not cats. The computer analyzes the pixels and figures out the patterns (shapes, textures) that define cat-ness on its own.
What is Machine Learning?
Machine Learning is a subset of Artificial Intelligence (AI). It is the process of using mathematical models of data to help a computer learn without direct instruction.
ML is mostly divided into two buckets:
- Classical ML (Predictive): "Will this user churn?" "Is this transaction fraud?" "What movie will they like next?" (Powered by decision trees, regression).
- Deep Learning / GenAI (Creative): "Write a poem." "Generate an image." (Powered by Neural Networks and Transformers).
ML Core Loop: Data Training Model Inference.
Popular ML Tools
| Tools | Why |
|---|---|
| Scikit-learn | Classical ML (Regression, clustering). The first library every Python dev learns. |
| PyTorch | The industry standard for building Neural Networks. |
| TensorFlow / JAX | Google's heavy-duty frameworks for high-performance ML. |
| MLflow / Weights & Biases | Tracking your experiments. |
| vLLM / TGI | The engines used to actually run the models once they are trained. |
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