Foundational Ideas

In this key section of the course, we dive into the essential machine learning concepts that consistently appear across all types of ML interviews. This foundation prepares us to explain not just the “what,” but the “why” behind our choices—something interviewers value highly.

• We review core ML paradigms like supervised, unsupervised, and reinforcement learning, along with real-world examples to explain their use cases clearly.
• We explore machine learning tasks such as classification and regression, and when to apply specific evaluation metrics like precision, recall, or RMSE.
• We gain a deep understanding of model performance concepts—bias/variance trade-off, underfitting/overfitting—and how to recognize and address them.
• We cover validation strategies, data leakage prevention, and the right use of metrics beyond just accuracy, such as AUC-ROC and F1 score.
• We enhance our skills in feature engineering, optimization algorithms (like SGD and Adam), model selection reasoning, and dimensionality reduction techniques like PCA and UMAP.
• We also touch on LLM fundamentals—such as Transformers and encoder-decoder architectures—to stay current with emerging trends.
• Most importantly, we practice explaining all of this in a way that connects to real-world problems and highlights our decision-making skills.

If you want to learn even more from Yayun:

• Book 1 on 1 career guidance with her here: https://calendly.com/your-friend-yayun/1on1
• Follow her on LinkedIn: https://www.linkedin.com/in/yayunjin/