Machine Learning Study Guide
Supervised, unsupervised, and reinforcement learning — linear models, neural nets, evaluation, and modern deep learning.
Core topics in Machine Learning
- Linear Regression and Classification
- Decision Trees and Ensembles
- Neural Networks
- Backpropagation
- Regularization
- Evaluation Metrics
- Unsupervised Learning
- Transformers
Why students struggle
ML rewards understanding why methods work, not just how to call sklearn. Students who can fit models but can't diagnose failures (overfit, leakage, bad metric choice) stall in real projects.
How Fennie helps
Fennie generates diagnostic scenarios — given training curves and metrics, predict what's wrong before suggesting fixes.
How to study Machine Learning
- 01Implement linear regression and logistic regression from scratch once
- 02Master the bias-variance tradeoff with concrete examples
- 03Use Fennie for evaluation-metric selection problems
- 04Read papers — ML moves too fast for textbook-only study
Frequently asked questions
ML or data science?
ML focuses on methods and theory; data science emphasizes business application. Significant overlap.
Do I need linear algebra and stats?
Yes — both. Trying ML without them is rote sklearn-calling.
Does Fennie cover transformer architectures?
Yes — attention mechanisms, transformer blocks, and modern LLM intuition.
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Data Structures
Arrays, linked lists, trees, graphs, hash tables, heaps — and choosing the right structure for the problem.
Algorithms
Algorithm design and analysis — divide-and-conquer, dynamic programming, greedy, graph algorithms, and complexity theory.
Operating Systems
Processes, threads, synchronization, memory management, file systems, and virtualization.
Computer Networks
TCP/IP stack, application protocols, routing, security, and modern network architecture.