You don't need a PhD to start with machine learning. You need Python, data and curiosity.
Direct answer
To learn machine learning from scratch: learn Python basics, learn to load and clean data, train a simple model, test it on data it hasn't seen, and repeat on different problems. Start with classic methods before deep learning. Free courses and public datasets are enough.
The path
- Python basics. See the Python course.
- Data skills: tables, cleaning, charts. See learn data science from scratch.
- Your first model.
- Evaluate honestly. Test on unseen data.
- Improve the data, not just the model.
- Try different problems: classification, prediction, grouping.
- Learn the theory gradually.
A tiny example
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
model = KNeighborsClassifier()
model.fit(X_train, y_train)
print(model.score(X_test, y_test))
It trains a model on flower measurements and checks it on flowers it hasn't seen.
Project ideas
- Predict prices from a small dataset
- Classify messages as spam or not
- Group customers by behaviour
Common mistakes
- Testing on the same data you trained on
- Ignoring messy data
- Jumping to deep learning too soon
Related
Is machine learning easy to learn and how to start studying AI.
Where Teyro fits
Teyro's Machine Learning for Beginners course has you build one real model step by step, with real Python in your browser, 10-minute lessons, no maths degree and nothing to install. It has 200 lessons and is free to start.
The bottom line
One dataset, one model, honest testing.
