How do I start learning machine learning as a complete beginner?

Start by building something that works, then learn the maths behind it when you hit the wall. The maths-first roadmaps are academically correct and practically demoralising — most people who complete them are people who were already going to succeed anyway. A sequence that keeps momentum: **Weeks 1-3: get one model working end to end.** Use scikit-learn on a classic dataset. Load data, split into train and test, fit a model, measure accuracy. Do this three or four times on different problems. You'll understand the *shape* of ML work before understanding any internals, which gives every later concept something to attach to. **Weeks 3-8: learn the concepts that decide whether your model is real.** This is the part beginners skip and it's the part that matters most professionally: - Train/validation/test splits and why testing on training data is meaningless - Overfitting and underfitting — the central problem of the field - Cross-validation - Why accuracy is a bad metric on imbalanced data, and what precision, recall and F1 mean - Feature engineering and data cleaning, which is where most real-world performance actually comes from Someone who understands these and knows three algorithms is far more employable than someone who can derive backpropagation but evaluates on the training set. **Weeks 8-16: classical algorithms with intuition.** Linear and logistic regression, decision trees, random forests, gradient boosting, k-means. Understand what each assumes and when it fails. For tabular business data, gradient boosting still beats deep learning routinely — this is not a stepping stone to the 'real' stuff, it's most industry work. **Then: maths, motivated by need.** Linear algebra (vectors, matrices, dot products), statistics (distributions, correlation, hypothesis testing, Bayes), and calculus intuition (derivatives, gradients — the idea of following a slope downhill). Learn each when you meet it. Now it's answering a question you have, which is a completely different experience from abstract study. **Then: deep learning**, if you still want it. PyTorch or TensorFlow, neural network basics, then a specialisation. Two things that matter more than the curriculum: work on datasets you find interesting rather than the same tutorial ones everyone uses, and finish projects to the point of a write-up explaining what you tried and what failed. 'I tried three approaches and here's why the simple one won' demonstrates far more than a notebook with 94% accuracy and no reasoning.