ML In Production And Capstone
Where to Go Next
This is a short, practical index — not new material. Its job is connecting this Machine Learning curriculum to the Deep Learning and NLP/LLM notes that follow,
JrCodex·6 min read
Jr Codex ML Notes
Level: Intermediate Prerequisites: Chapter 4: Capstone Project Time to complete: ~15 minutes
Table of Contents
- Purpose of This Chapter
- What This ML Curriculum Covered
- Mapping to the Deep Learning Notes
- Mapping to the NLP & LLM Notes
- What Carries Over Directly
- What's Genuinely New
- A Suggested Path Forward
- Closing
1. Purpose of This Chapter
This is a short, practical index — not new material. Its job is connecting this Machine Learning curriculum to the Deep Learning and NLP/LLM notes that follow, so the transition feels like a continuation, not a fresh start.
2. What This ML Curriculum Covered
The Nine Modules, In Review
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1. Foundations — paradigms, workflow, bias-variance,
evaluation discipline
2. Data Preparation — encoding, scaling, feature
engineering, imbalance
3. Regression — linear models, regularization,
non-linearity
4. Classification — logistic regression, KNN,
trees, SVM, Naive Bayes,
evaluation
5. Ensemble Learning — bagging, boosting,
XGBoost/LightGBM, stacking
6. Unsupervised Learning — clustering,
dimensionality reduction
7. Model Selection & Tuning — cross-validation,
grid/random/
Bayesian search
8. Reinforcement Learning — policy gradients,
actor-critic,
bridge to Deep RL
9. Production — pipelines,
deployment,
monitoring
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3. Mapping to the Deep Learning Notes
| This ML Curriculum | Resurfaces In the Deep Learning Notes |
|---|---|
| Module 3, Ch.1: Gradient Descent | DL Notes: Neural Networks from Scratch — the SAME optimization algorithm, now updating millions of parameters instead of a handful |
| Module 1, Ch.4: Bias-Variance Tradeoff | DL Notes: Training Deep Networks — overfitting/underfitting reappear directly, with new tools (dropout, batch normalization) |
| Module 4, Ch.1: Logistic Regression / Sigmoid | DL Notes: Neural Networks from Scratch — a single neuron with a sigmoid activation IS logistic regression; a network is many of these, layered |
| Module 5 (Ensemble Learning) | DL Notes: Generative Models — GANs' two-network adversarial setup echoes ensemble/adversarial dynamics |
| Module 8 (Reinforcement Learning) | Deep Q-Networks and deep policy gradients — direct continuations of Module 8's Chapter 4 bridge |
| Module 7 (Model Selection & Tuning) | Deep learning hyperparameter tuning (learning rate schedules, architecture search) — the same principles, more parameters to tune |
| Module 9 (Production) | DL Notes: PyTorch in Practice — the same deployment/monitoring concerns, adapted for neural network serving |
4. Mapping to the NLP & LLM Notes
| This ML Curriculum | Resurfaces In the NLP & LLM Notes |
|---|---|
| Module 4, Ch.5: Naive Bayes | NLP Notes: Classical NLP — Naive Bayes was a dominant historical text classification technique, directly covered there |
| Module 6, Ch.3-4: Dimensionality Reduction | NLP Notes: Word Embeddings — word embeddings are, conceptually, a learned dimensionality reduction from sparse word representations to dense vectors |
| Module 4, Ch.6: Evaluation Metrics | NLP Notes: NLP Evaluation — precision/recall/F1 reappear, alongside NLP-specific metrics (BLEU, ROUGE) |
| Module 8 (Reinforcement Learning) | NLP Notes: Fine-Tuning LLMs — RLHF directly applies Module 8, Chapter 3's actor-critic/PPO machinery to language model alignment |
| Module 9 (Production) | NLP Notes: LLMs in Production — the same monitoring/deployment discipline, adapted for LLM-specific concerns (cost, latency, safety) |
5. What Carries Over Directly
Skills You Won't Be Learning Cold
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- Gradient descent (Module 3, Ch.1) — the CORE training
algorithm for every neural network, unchanged in principle
- The bias-variance tradeoff (Module 1, Ch.4) — still the
central lens for understanding overfitting in deep networks
- Train/test splits, cross-validation discipline (Module 1,
Ch.5; Module 7) — identical requirements for deep learning
and LLM evaluation
- Precision/recall/F1/ROC-AUC (Module 4, Ch.6) — still the
standard classification metrics, regardless of whether the
classifier is logistic regression or a neural network
- Data preprocessing rigor (Module 2) — still mandatory,
though DEEP learning can sometimes learn useful features
directly from less-processed raw data (images, text) than
classical ML requires
- Hyperparameter tuning discipline (Module 7) — the same
principles apply, just to a larger, differently-structured
hyperparameter space
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6. What's Genuinely New
Concepts the Deep Learning Notes Introduce
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Backpropagation: the specific algorithm for computing
gradients through MANY layers — an
extension of Module 3, Ch.1's gradient
descent, but genuinely new machinery
Architectures: CNNs (for images), RNNs/Transformers
(for sequences) — structured ways of
connecting neurons that have NO
direct analog in Modules 3-6
GPU computation: deep learning's practical need
for specialized hardware — a
genuinely new infrastructure
concern beyond Module 9's scope
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Concepts the NLP/LLM Notes Introduce
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Attention mechanisms: how transformers weigh relevance
between words — no direct
predecessor in this ML curriculum
Pre-training + fine-tuning: the paradigm of learning general
patterns from massive unlabeled
text, then adapting to a
specific task — DIFFERENT from
this curriculum's typical
"train from scratch on labeled
data" approach
Prompt engineering: a genuinely NEW skill —
communicating with a
pre-trained model through
natural language instructions,
rather than training it directly
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7. A Suggested Path Forward
Recommended Order
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1. Deep Learning Notes FIRST — builds directly on this
curriculum's gradient descent (Module 3, Ch.1) and
bias-variance foundations (Module 1, Ch.4), extending
them to neural networks
2. NLP & LLM Notes SECOND — builds on the Deep Learning
Notes' neural network foundation (especially transformers/
attention), applying it specifically to language
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8. Closing
You've now completed the entire Machine Learning curriculum — foundations, data preparation, regression, classification, ensemble learning, unsupervised learning, model selection and tuning, reinforcement learning, and production deployment, capped with a full end-to-end capstone project. Every technique here was chosen specifically because it either directly supports or gets reused inside the Deep Learning and NLP/LLM notes that follow.
→ Continue to the Deep Learning Notes → Continue to the NLP & LLM Notes
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