BC Skills Academy

Machine Learning Associate

The hands-on core of classical machine learning — algorithms, evaluation, and judgement

Key facts

About the Machine Learning Associate certification

An associate-level certification that takes you from a conceptual understanding of AI into the working toolkit of a practising machine-learning engineer. You will master the major supervised algorithms — linear and logistic regression, decision trees, random forests, gradient boosting (XGBoost), k-nearest neighbours, support vector machines, and naive Bayes — and the unsupervised workhorses of k-means, hierarchical clustering, and PCA. Just as importantly, you will build the judgement that separates a model that demos well from one that survives production: reading the bias–variance tradeoff, diagnosing overfitting and applying L1/L2 regularization, designing honest train/validation/test splits and cross-validation, choosing the right evaluation metric (accuracy, precision, recall, F1, ROC-AUC, RMSE, MAE), engineering and scaling features, tuning gradient descent, handling class imbalance, and spotting data leakage before it wrecks your results. The certification is built around real scenarios and trade-offs, not rote recall, and prepares you for any deeper, model-specific AI certification.

Course outline

The Machine Learning Associate — Illustrated Study Guide course covers:

  1. The ML Workflow & Learning Paradigms — The end-to-end machine-learning workflow and the supervised vs unsupervised divide that frames every project. (3 lessons)
  2. Supervised Algorithms — Linear and logistic regression, decision trees, ensembles (random forests, gradient boosting), kNN, SVM, and naive Bayes. (3 lessons)
  3. Unsupervised Learning & Dimensionality Reduction — Clustering with k-means and hierarchical methods, plus dimensionality reduction with PCA. (3 lessons)
  4. Bias, Variance, and Regularization — The bias–variance tradeoff, diagnosing overfitting and underfitting, and taming complexity with L1/L2 regularization and cross-validation. (3 lessons)
  5. Evaluation Metrics & Real-World Pitfalls — Choosing the right metric — accuracy, precision, recall, F1, ROC-AUC, RMSE/MAE — and avoiding class imbalance and data leakage traps. (3 lessons)

Prerequisites

Frequently asked questions

Is the Machine Learning Associate certificate verifiable?
Yes. Every certificate issued by BC Skills Academy carries a unique credential ID that anyone can check on the public verification page.
How is the Machine Learning Associate exam structured?
It is a timed, randomized multiple-choice exam of 53 questions in 75 minutes, graded instantly; you need 70% to pass.
Can I get a refund?
Full refund within 7 days of purchase if you have not started the exam. Once the exam has been started or completed, the purchase is no longer refundable.

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