How to Prepare for the Machine Learning Specialization

Use the DeepLearning.AI, Coursera, or Stanford Online specialization page to confirm the current course sequence. Do not use CS229 as the main checklist; it is better treated as optional advanced reading after the specialization topics are clear.

Practice functions, loops, conditionals, arrays, indexing, vectorized operations, and simple plots. You do not need to be a software engineer first, but you should be able to trace how data moves through a training loop or model-evaluation notebook.

Useful math preparation includes algebra, functions, graphs, vectors, matrices, and the intuition of derivatives. The practical goal is to understand what cost, gradient, parameter, and prediction mean when you see them in code.

For every model result, ask whether the problem is bias, variance, data leakage, poor feature scaling, class imbalance, threshold choice, or metric mismatch. Before changing the algorithm, inspect the evidence.

After a lesson or notebook, answer a focused set of DotCreds questions. For each miss, write the category: algorithm selection, objective function, gradient descent, regularization, metric, data split, neural network structure, tree behavior, clustering, anomaly detection, or recommender workflow.

Do not repeatedly use the test set for model selection. Cross-validation can improve model selection discipline, but it does not prove future performance. Do not trust accuracy alone on imbalanced data. Do not treat high training performance as proof that the model generalizes.

Small datasets make ML behavior visible. Try changing the learning rate, adding a polynomial feature, increasing regularization, shifting a classification threshold, or changing K-means initialization. Seeing the failure mode makes the concept easier to remember.