Should I learn scikit-learn or PyTorch first?

scikit-learn. Understand the classical workflow before you touch deep learning.

How much data do I need?

For classical ML, hundreds to thousands of well-labelled examples can be enough. For deep learning, orders of magnitude more — unless you use pre-trained models.

Machine Learning for Beginners

The mental model

Machine learning is the practice of writing programs that get better at a task by looking at data. Instead of a human writing every rule, we let a program find the rules — with our supervision.

Think of it as shape-fitting. You have a bunch of examples and a target. The algorithm tries to find a function that turns inputs into outputs with the smallest possible error.

The three flavours

Supervised learning

You have labelled data — inputs plus the "right answer". The model learns to predict the answer from the input. Almost every commercial ML system you have used is supervised.

Unsupervised learning

You have data but no labels. The model looks for structure — clusters, dimensions, patterns. Useful for exploration and preprocessing.

Reinforcement learning

An agent takes actions in an environment and receives rewards. Over time it learns which actions maximise long-term reward. This is how game-playing AI and robot control usually work.

What a "model" really is

A model is just a function with millions or billions of tunable numbers, called parameters. Training is the process of nudging those numbers so the function produces better outputs on your data.

You do not need to understand the maths to be useful. You do need to understand this shape: inputs → parameters → outputs → error → adjustment.

The workflow

Frame the problem. Is it classification (which category?), regression (what number?), ranking, or generation?
Get the data. Clean it, split it into training / validation / test sets.
Pick a baseline model. Start simple. Logistic regression before deep learning.
Train and evaluate. Watch training vs validation error like a hawk.
Iterate. Feature engineering, model tuning, more data — in that order.
Deploy and monitor. A model that isn't monitored is a model that is silently failing.

The three pitfalls beginners always hit

Overfitting. Your model memorises training data and fails on new data. The single most common failure.
Leakage. Information from the future or from the label sneaks into your features. The model looks brilliant in training and disappoints in production.
Optimising the wrong metric. Accuracy on an imbalanced dataset is meaningless. Understand your metric before you tune to it.

Where to go from here

Once these ideas feel comfortable, deep learning is a natural next step — but only because it uses the same shape at a larger scale. Do not skip the fundamentals; every senior ML practitioner has scars from doing exactly that.