Machine Learning Specialization Study Roadmap

Review functions, loops, arrays, vectorized operations, plotting, and simple algebra. You should be comfortable reading NumPy-style code and matching variables in code to inputs, parameters, predictions, cost, and gradients.

Begin with regression and classification. Understand training examples, features, labels, cost functions, predictions, and decision boundaries. Linear regression and logistic regression teach the workflow that later models reuse.

Study gradient descent, learning rate, normalization, standardization, and feature scaling. If training is unstable or slow, the first diagnostic step is often to inspect the learning rate and feature scales before changing algorithms.

Learn training, validation, and test sets early. Bias, variance, learning curves, baseline performance, and error analysis help you decide whether to add features, collect data, regularize, simplify, or change the model.

Neural networks introduce layers, activations, forward propagation, backpropagation, and TensorFlow basics. Decision trees and ensembles introduce splitting, overfitting, random forests, and boosting-style thinking.

Study clustering, K-means behavior, anomaly detection thresholds, collaborative filtering, content-based filtering, cold-start problems, and reinforcement learning concepts at the level supported by the official specialization.

Mix practice across algorithms. For each scenario, ask: what task is being solved, what evidence points to the failure, is the issue training or evaluation, and what diagnostic should be checked first?