Sprint: Python ML Basics Mastery - Real World Projects

Goal: Build a first-principles mental model of practical machine learning in Python, from raw tables to validated models you can defend in a review or interview. You will understand not only what to run, but why each step exists: data representation, exploratory analysis, preprocessing, model fitting, and honest evaluation. The sprint is designed so every project produces observable output and forces contact with common failure modes such as leakage, weak baselines, and misleading metrics. By the end, you will be able to design and execute a reproducible classical ML workflow for tabular problems using NumPy, pandas, visualization tools, and scikit-learn.

Introduction

Python ML basics means learning the core tabular ML workflow that most teams still rely on for forecasting, classification, scoring, and decision support.

What this guide solves:

You stop treating ML as magic API calls.
You learn to debug data and models systematically.
You learn to communicate results with evidence, not intuition.

What you will build:

A vectorized classifier from scratch (conceptual baseline).
A complete EDA report with defensible findings.
Regression and classification pipelines with preprocessing and validation.
A reproducible baseline workflow with artifacts and quality checks.

In scope:

Tabular data, supervised learning, feature preprocessing, evaluation discipline.

Out of scope:

Deep learning frameworks, distributed training, LLM fine-tuning, advanced MLOps infrastructure.

                     Python ML Basics System View

Raw CSV/Parquet
      |
      v
+------------------+      +-------------------------+
| Data Semantics   | ---> | Cleaning and Typing     |
| (columns, units) |      | (missing, categories)   |
+------------------+      +-------------------------+
          |                           |
          +-------------+-------------+
                        v
                +---------------+
                | EDA and Plots |
                | (patterns,    |
                | outliers, bias)|
                +-------+-------+
                        |
                        v
               +--------------------+
               | Feature Pipeline   |
               | (scale, encode,    |
               | split, transform)  |
               +---------+----------+
                         |
                         v
               +--------------------+
               | Model Fit/Evaluate |
               | (metrics, CV,      |
               | error analysis)    |
               +---------+----------+
                         |
                         v
               +--------------------+
               | Decision/Iteration |
               | (keep, improve,    |
               | reject model)      |
               +--------------------+

How to Use This Guide

Read ## Theory Primer first. Do not skip directly to projects.
Pick one learning path in ## Recommended Learning Paths.
Complete projects in order unless you already have prior ML experience.
For each project, finish the #### Thinking Exercise before implementation.
Use #### Definition of Done as a gate; do not self-grade by “it runs”.
Keep a lab notebook (date, hypothesis, what changed, result).

Prerequisites & Background Knowledge

Essential Prerequisites (Must Have)

Python basics: functions, loops, list/dict, modules, virtual environments.
Basic algebra and percentages (mean, variance, ratio interpretation).
Comfort with terminal workflows and CSV files.
Recommended Reading: “Fluent Python, 2nd Edition” by Luciano Ramalho - Ch. 1-3, 7.

Helpful But Not Required

Probability intuition (conditional probability, distributions).
SQL basics for filtering and aggregating tabular data.
Git basics for experiment tracking.

Self-Assessment Questions

Can you explain the difference between an integer column and a categorical column, and why that affects modeling?
If a model gets 95% accuracy, can you name two reasons this can still be bad?
Can you describe what changes between training and testing data flows?

Development Environment Setup Required Tools:

Python 3.11+
numpy 2.x
pandas 3.x
matplotlib 3.10+
seaborn 0.13+
scikit-learn 1.8+
JupyterLab (or VS Code notebooks)

Recommended Tools:

uv or pip-tools for dependency locking
ruff for notebook/script linting
make for repeatable local commands

Testing Your Setup:

$ python -m pip show numpy pandas scikit-learn seaborn matplotlib
Name: numpy
Version: 2.x.x
...
Name: scikit-learn
Version: 1.8.x
...

Time Investment

Simple projects: 4-8 hours
Moderate projects: 10-20 hours
Complex projects: 20-40 hours
Total sprint: 2-4 months (part-time)

Important Reality Check ML progress is nonlinear. Most time is spent cleaning data, validating assumptions, and debugging evaluation. If you only optimize models and ignore data quality, you will produce fragile results.

Big Picture / Mental Model

The most useful mindset is to treat ML as controlled experimentation under uncertainty.

            Experiment Loop for Practical ML

Hypothesis -> Data Audit -> Baseline -> Evaluation -> Decision
    ^                                               |
    |-----------------------------------------------|

Invariant 1: Test data remains untouched until final evaluation.
Invariant 2: Transformations are learned on train folds only.
Invariant 3: Every metric result is tied to a dataset version.

A model is not the product; the product is reliable decision support. Your workflow must be reproducible, interpretable, and bounded by clear quality criteria.

Theory Primer

Concept 1: Data Representation and Vectorized Computing (NumPy + pandas)

Fundamentals Data representation is the substrate of every ML workflow. NumPy provides fixed-type n-dimensional arrays (ndarray) optimized for contiguous memory operations, while pandas provides labeled tabular abstractions (Series, DataFrame) built for heterogeneous datasets and index-aware transformations. The key principle is that computational shape and semantic shape are different: a matrix can be mathematically valid while semantically wrong (for example, mixing normalized and raw units in one column family). Vectorization matters because CPU-efficient batch operations reduce Python loop overhead and enforce consistent transformations across whole columns. If you do not understand dtypes, missing values, and alignment semantics, downstream models will silently learn artifacts instead of signal.
Deep Dive In practical ML, representation quality dominates algorithm choice more often than beginners expect. Two teams can run the same estimator and get drastically different outcomes because one team represented data coherently and the other mixed incompatible meanings. The first layer is dtype discipline. Numeric columns should preserve numeric meaning (integers vs floats), categorical columns should preserve category boundaries, and date/time columns should remain parseable as temporal objects rather than strings. If a numeric feature is accidentally coerced to string due to one malformed row, aggregations and scaling will fail silently or produce misleading casts.

The second layer is memory and vectorization. NumPy arrays are designed for bulk arithmetic; operations are applied across contiguous memory blocks when possible. This lets you perform transformations such as centering, scaling, clipping, and distance calculation across entire datasets with predictable behavior. The important invariant is shape agreement: operations should be dimensionally explicit (samples x features) so broadcasting does what you intend. Broadcasting is powerful but dangerous when dimensions are ambiguous; a one-dimensional array might align along features when you intended alignment along samples.

The third layer is pandas alignment semantics. Unlike raw arrays, pandas aligns by index labels during many operations. That is a feature, not a bug, but it means you must deliberately control indexes after merges, filters, and shuffles. If two tables are joined with inconsistent keys or duplicated indexes, your target column can drift away from your features without obvious exceptions. Good practitioners use explicit key checks (unique constraints, row counts before/after joins, null deltas) as safety gates.

Missingness is the fourth layer. Missing values are not merely blanks; they are signals about data generation processes. Some are random (sensor glitch), some are structural (not applicable for a subgroup), and some are policy-driven (redacted fields). Imputation should be strategy-aware. Mean/median imputation is acceptable for stable numeric distributions, but for skewed data or high-cardinality categories, naive filling can erase informative structure. You should evaluate missingness patterns before picking imputation rules.

Fifth, feature typing affects downstream preprocessing contracts. Tree-based models often tolerate unscaled features but still require consistent encoding for categories. Linear or distance-based models are sensitive to scale and one-hot dimensional explosion. Representing categories with stable vocabularies and unknown-token handling prevents production-time crashes when new categories appear.

Finally, representation is where reproducibility starts. Store schema metadata: column names, dtypes, unit definitions, and accepted ranges. When a new dataset violates schema, fail early. This prevents expensive debugging at model evaluation time and avoids shipping brittle pipelines.

How this fit on projects Project 1 emphasizes vectorized distance computation. Project 2 stresses tabular typing and null logic. Projects 3-6 depend on stable schema handling, encoding, and feature transformations.
Definitions & key terms
ndarray: Fixed-type multidimensional numerical array.
DataFrame: Labeled 2D table with potentially mixed column types.
vectorization: Applying operations to whole arrays/columns at once.
broadcasting: Shape-compatible implicit expansion during array math.
schema: Explicit contract for columns, dtypes, ranges, and meanings.
Mental model diagram ```text Representation Safety Stack

Business Meaning -> Column Contract -> Storage Type -> Operation Semantics | | | | v v v v “Age in years” age: float64 contiguous array vectorized ops “Plan tier” tier: category dictionary-encoded groupby/encode

If any layer is wrong, model learns noise.

- **How it works**
1. Define schema before feature engineering.
2. Load data and enforce dtypes explicitly.
3. Audit nulls, duplicates, and out-of-range values.
4. Apply vectorized transformations by column group.
5. Validate row/column counts and index integrity after joins.
6. Freeze representation assumptions for downstream modeling.

Failure modes:
- Silent dtype coercion (numeric to string).
- Index misalignment after merge/sort.
- Broadcasting along wrong axis.

- **Minimal concrete example**
```text
PSEUDOCODE
input_table: 10000 rows x 12 columns
schema_check(input_table)
X_num = select numeric columns -> shape (10000, 7)
X_cat = select categorical columns -> shape (10000, 5)
X_num_scaled = (X_num - mean_train) / std_train
X_final = concatenate(X_num_scaled, one_hot(X_cat))
assert rows(X_final) == rows(target)

Common misconceptions
“If it runs, the data is fine.” Correction: many representation errors are logically valid but semantically wrong.
“pandas always keeps rows in expected order.” Correction: merges and alignment can reorder or duplicate rows.
Check-your-understanding questions
1. Why is index integrity a modeling concern and not just a data-engineering concern?
2. What can go wrong if you rely on implicit dtype inference?
3. Why can broadcasting produce correct-looking but wrong results?
Check-your-understanding answers
1. Misaligned rows corrupt feature-target pairing, invalidating training signals.
2. Incorrect dtypes can change operations, aggregations, and preprocessing paths.
3. Axis ambiguity can apply transforms across unintended dimensions.
Real-world applications
Credit scoring feature tables.
Manufacturing sensor pipelines.
Healthcare operations dashboards (claims, scheduling, throughput).
Where you’ll apply it
Project 1, Project 2, Project 3, Project 5, Project 6.
References
NumPy user guide: What is NumPy?
NumPy broadcasting: Broadcasting
pandas docs: Getting started
“Python for Data Analysis” by Wes McKinney - Ch. 3-8
Key insights Good models start with explicit data contracts, not estimator selection.
Summary Representation governs correctness, speed, and reproducibility. Treat schema, shapes, and typing as first-class design decisions.
Homework/Exercises to practice the concept
1. Create a schema checklist for a public CSV and identify three violations.
2. Simulate an index misalignment bug and detect it.
3. Compare loop-based vs vectorized column transforms in runtime and clarity.
Solutions to the homework/exercises
1. Validate expected dtype/range for each column and document anomalies.
2. Shuffle features without shuffling target; detect via impossible correlations.
3. Vectorized version should be shorter, more consistent, and faster.

Concept 2: Exploratory Data Analysis and Visualization as Model Risk Control

Fundamentals EDA is the disciplined process of turning raw tables into hypotheses and risk flags before modeling. Visualization is not decoration; it is a diagnostic interface for data quality, distribution shape, outliers, leakage clues, and segment behavior. In Python workflows, pandas provides aggregation/query primitives, while Matplotlib and Seaborn map patterns into visual checks. Good EDA connects each chart to an operational question: “What might cause prediction errors here?” rather than “Can I make a pretty plot?”. The objective is to reduce modeling surprises by discovering issues early: skew, class imbalance, target leakage proxies, and unstable subgroup behavior.
Deep Dive Most beginner workflows fail because they treat EDA as optional. In practice, EDA is where you learn the data-generating process. If your project asks for churn prediction, fraud detection, or price estimation, you are dealing with historical measurements collected under policies, operational constraints, and sometimes biased sampling. EDA is how you inspect those constraints before you encode them into models.

Start with structural profiling: row counts, column cardinalities, null rates, duplicate keys, and temporal coverage windows. Then perform univariate analysis for each feature family. Numeric variables require distribution checks (central tendency, spread, skew, heavy tails, spikes at sentinel values). Categorical variables require frequency concentration and rare-category inspection. For targets, you need base rate clarity. A 2% positive class requires different evaluation thresholds than balanced labels.

Bivariate and conditional analyses are the next layer. Relationship plots between key features and target reveal monotonic trends, nonlinear regions, and interaction hints. Segment-level plots matter because global averages hide subgroup failures. For example, a model can look strong overall while systematically underperforming for a specific geography, device type, or income segment. In production, those hidden segments become incident tickets.

Visualization design choices affect interpretation quality. Histograms show shape; boxplots expose outliers and spread; heatmaps communicate correlation structure but can be abused when users infer causality from correlation. Pair plots provide intuitive structure for small feature sets but become noisy in high dimensions. Always annotate assumptions: time windows, filters, and transformations used before plotting.

EDA is also where leakage detection begins. If a feature has suspiciously high correlation with the target and is generated after the event being predicted, you have a leakage candidate. Similarly, ID-like columns with many unique values may encode target-adjacent logic (for example, ticket status codes). The invariant is chronological and causal validity: features available at prediction time only.

Another critical dimension is missingness visualization. Missingness heatmaps and null-rate by segment can reveal policy or workflow artifacts. If one region has much higher null rates for income, your model may underperform in that segment unless imputation is segment-aware.

Finally, EDA outputs should be decision-ready artifacts, not screenshots. Convert findings into actionable statements: “Feature X is right-skewed; log transform candidate,” “Category Y has sparse support; group into ‘other’,” “Target ratio drift after 2024-Q3; time-aware split required.” These statements become preprocessing and evaluation requirements in subsequent projects.

How this fit on projects Project 2 is EDA-heavy. Projects 3-6 use EDA outputs to define preprocessing, splits, and metric interpretation.
Definitions & key terms
EDA: Exploratory Data Analysis.
class imbalance: Uneven label distribution across classes.
leakage: Using information unavailable at real prediction time.
segment analysis: Comparing behavior across meaningful subgroups.
drift: Distribution changes over time.
Mental model diagram ```text EDA as a Risk Firewall

Raw Data -> Profile -> Distributions -> Relationships -> Segment Checks -> Rules | | | | | | v v v v v v schema nulls skew/outliers target links fairness prep plan

- **How it works**
1. Profile structure and quality.
2. Analyze univariate distributions.
3. Analyze feature-target relationships.
4. Add segment-level diagnostics.
5. Convert findings into explicit preprocessing and evaluation constraints.

Failure modes:
- Over-interpreting correlations as causal.
- Ignoring subgroup behavior.
- Performing EDA after heavy preprocessing that hides raw issues.

- **Minimal concrete example**
```text
PSEUDOCODE EDA REPORT
question: "Does ticket class influence survival?"
compute survival_rate by class
plot bar chart with confidence intervals
check sample counts per class
report: "Class 3 has lower survival and higher variance due to larger sample and missing age"
action: "Impute age with class-aware medians"

Common misconceptions
“EDA is just plotting.” Correction: EDA is hypothesis formation plus risk detection.
“One global metric is enough.” Correction: subgroup behavior can contradict aggregate results.
Check-your-understanding questions
1. Why can a strong overall metric hide operational risk?
2. How do you distinguish a useful proxy from leakage?
3. Why should EDA precede model choice?
Check-your-understanding answers
1. Subgroup failures are averaged out in global metrics.
2. Verify feature availability timestamp and causal direction.
3. Model choice depends on data shape, noise, sparsity, and imbalance discovered in EDA.
Real-world applications
Churn and retention analysis.
Quality control anomaly triage.
Revenue forecasting sanity checks.
Where you’ll apply it
Project 2, Project 3, Project 4, Project 5, Project 6.
References
pandas docs: User Guide
Seaborn docs: Tutorial
Matplotlib docs: Pyplot tutorial
“Practical Statistics for Data Scientists” by Bruce, Bruce, Gedeck - Ch. 1-3
Key insights EDA converts unknown data risk into explicit modeling decisions.
Summary Without EDA, you are guessing. With EDA, you build constraints and hypotheses that make model training meaningful.
Homework/Exercises to practice the concept
1. Build an EDA checklist for a dataset with mixed numeric/categorical features.
2. Identify one likely leakage feature candidate and justify your claim.
3. Produce one chart that changes your preprocessing plan.
Solutions to the homework/exercises
1. Include null profile, skewness, rare categories, target balance, segment cuts.
2. Candidate features are those generated after the target event or directly derived from it.
3. A heavy-tailed feature histogram often justifies log transform or robust scaling.

Concept 3: Supervised Learning Pipelines (Splits, Preprocessing, Fit/Predict Contracts)

Fundamentals A supervised ML pipeline is a deterministic sequence that transforms raw features into model-ready inputs, learns parameters on training data, and evaluates on unseen data. The core contract in scikit-learn is consistent object behavior: fit, transform, predict, and estimator composition. A robust pipeline prevents leakage by ensuring learned transformations (for example, means, standard deviations, vocabularies) come only from training partitions. Pipelines also make experimentation safer by packaging preprocessing and model logic into one artifact. This is critical for reproducibility and deployment alignment.
Deep Dive Beginners often think modeling starts when selecting an algorithm. In production-grade workflows, modeling starts with split strategy and transformation boundaries. If data has temporal order, random splits can inflate metrics by leaking future patterns into training. If classes are imbalanced, naive splitting distorts class ratios and destabilizes performance estimates. Thus, split design must match business reality: random stratified, grouped, or time-based.

After splitting, preprocessing strategy should be feature-family specific. Numeric features may need scaling, clipping, missing-value handling, and optional nonlinear transforms. Categorical features require encoding with explicit handling of unseen categories. Text-like identifiers may require pruning if they encode leakage. The key invariant is that any statistic learned from data (mean, median, category map, scaling factor) is fit on training data only.

The scikit-learn pipeline abstraction enforces this boundary when used correctly. A column-wise transformer can apply separate logic to numeric and categorical subsets, then pass the concatenated result to an estimator. This is not merely convenient; it prevents subtle bugs where transformations are applied inconsistently between training and inference.

Model families behave differently under preprocessing regimes. Distance-based methods (like KNN) are highly sensitive to feature scales; linear models rely on stable encoding and scaling for coefficient interpretability; tree-based methods are more scale-agnostic but can overfit sparse high-cardinality features. Pipeline design should therefore include model-aware preprocessing defaults.

Hyperparameter tuning is another stage where novices accidentally leak information. If you tune hyperparameters directly on the test set, your final metric is optimistic and unusable. Correct practice uses cross-validation inside training data, then one final evaluation on untouched test data. This preserves a clean estimate of generalization.

Error analysis closes the loop. Aggregate metrics are necessary but insufficient. Investigate where predictions fail: specific ranges, segments, or classes. For regression, inspect residual plots for heteroscedasticity and systematic bias. For classification, inspect confusion matrices and threshold behavior, especially when business costs differ for false positives and false negatives.

Pipelines also support reproducibility. Save preprocessing and model objects together, record versions, and document input schema expectations. If inference data shifts or schema drifts, fail with clear diagnostics rather than silent coercion.

In summary, pipeline design is risk management. It creates boundaries that preserve validity from experimentation to deployment. When learners internalize this, they stop chasing isolated model improvements and start building trustworthy systems.

How this fit on projects Projects 3-6 use full pipeline contracts. Project 1 builds intuition for distance-based behavior that motivates scale-aware preprocessing.
Definitions & key terms
fit: Learn parameters from data.
transform: Apply learned mapping to features.
pipeline: Chained preprocessing + estimator workflow.
data leakage: Unintended information flow from evaluation data.
cross-validation: Repeated train/validation splits for robust estimation.
Mental model diagram ```text Train data –> [fit preprocessors] –> [fit model] –> artifact Test data –> [transform only] –> [predict] –> metrics

Rule: test data never participates in fitting.

- **How it works**
1. Define split policy aligned with use case.
2. Define numeric/categorical preprocessing separately.
3. Build one composed pipeline.
4. Fit on train only.
5. Validate via CV within train set.
6. Evaluate once on held-out test set.
7. Perform segment-level error analysis.

Failure modes:
- Scaling/encoding before splitting.
- Hyperparameter tuning on test set.
- Pipeline not reused at inference.

- **Minimal concrete example**
```text
PSEUDOCODE
split(data, strategy="stratified")
preprocess = column_transformer(
  numeric=[impute_median, scale_standard],
  categorical=[impute_mode, one_hot_unknown_safe]
)
pipeline = chain(preprocess, logistic_regression)
cv_score = cross_validate(pipeline, train_data, k=5)
fit(pipeline, train_data)
test_predictions = predict(pipeline, test_data)
report(metrics(test_predictions, test_targets))

Common misconceptions
“The model is the only thing that matters.” Correction: preprocessing and split strategy usually drive more variance than model swaps.
“One test evaluation is enough for tuning.” Correction: tuning on test data contaminates the final estimate.
Check-your-understanding questions
1. Why is fitting scalers on full data a leakage problem?
2. When should you prefer time-based splits?
3. Why package preprocessing and estimator together?
Check-your-understanding answers
1. Test statistics influence train transforms, inflating performance.
2. When prediction happens on future records and temporal drift exists.
3. To guarantee identical transformations between training and inference.
Real-world applications
Demand forecasting baselines.
Customer churn scoring.
Fraud pre-screening models.
Where you’ll apply it
Project 3, Project 4, Project 5, Project 6.
References
scikit-learn docs: Getting Started
scikit-learn docs: Pipelines and composite estimators
“Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow” by Aurelien Geron - Ch. 2-3
Key insights Pipelines are validity boundaries, not convenience wrappers.
Summary Treat preprocessing, split policy, and estimator as one integrated system to avoid hidden leakage and deployment mismatch.
Homework/Exercises to practice the concept
1. Design a split strategy for a monthly churn dataset and justify it.
2. Write a train/test boundary checklist for preprocessing steps.
3. Compare one model with and without scale-aware preprocessing.
Solutions to the homework/exercises
1. Use time-aware split with last period held out to reflect real deployment.
2. Any step learning statistics must fit on train folds only.
3. Distance/linear models typically improve after proper scaling.

Concept 4: Evaluation, Generalization, and Decision Thresholds

Fundamentals Evaluation answers one question: can this model generalize to new data under real operating conditions? Generalization is not guaranteed by high training performance. Robust evaluation combines appropriate metrics, unbiased validation design, and explicit decision rules. For regression, error distributions matter more than one average number. For classification, threshold choice and class imbalance often dominate practical utility. The goal is decision-quality evidence, not leaderboard optimization.
Deep Dive A common anti-pattern is chasing one metric without context. In classification, accuracy can be misleading when classes are imbalanced. Suppose only 5% of events are positive; a model predicting all negatives gets 95% accuracy and zero practical value. Precision, recall, F1, ROC-AUC, and PR-AUC each answer different operational questions. If false negatives are costly (fraud missed, disease missed), recall may be prioritized. If false positives are costly (manual review overload), precision becomes critical.

Thresholding converts probabilistic outputs into decisions. The default 0.5 cutoff is arbitrary unless tied to business costs. A better approach is to define expected cost per false positive and false negative, then choose threshold minimizing expected loss on validation data. This makes model behavior explainable to stakeholders.

For regression, MAE, MSE, RMSE, and R-squared measure different aspects. MAE is robust and interpretable in original units. MSE/RMSE penalize large errors more heavily, useful when outliers are operationally expensive. Residual analysis is mandatory: plot residuals against predictions and key features. Patterns indicate model misspecification, missing features, or nonlinearity.

Cross-validation improves estimate stability by reducing dependency on one random split. But CV is not a free pass: fold design must respect grouping or time structure when needed. If the same entity appears in train and validation folds inappropriately, leakage persists.

Calibration is another overlooked dimension. A classifier with strong ranking can still produce miscalibrated probabilities. If downstream decisions require probability estimates (for triage queues or resource planning), calibration checks (reliability curves, Brier score) matter.

Evaluation should include baseline comparisons. If your complex model barely beats a naive baseline, complexity is unjustified. Baselines include mean predictor for regression, majority-class or simple heuristic for classification, and often a simple linear/logistic model. Strong teams require baseline beat criteria before approving upgrades.

Finally, evaluation is incomplete without error slicing. Analyze performance across segments (region, tier, demographic proxy, channel). This catches fairness and robustness issues hidden in aggregate metrics. Add confidence intervals or bootstrap ranges for key metrics when possible to communicate uncertainty.

The operational output of evaluation is a decision: deploy, iterate, or reject. Attach evidence: metrics by split, threshold rationale, segment behavior, drift sensitivity assumptions, and failure modes. This discipline turns ML from experimentation into accountable engineering.

How this fit on projects Projects 3-6 require metric choice, threshold reasoning, baseline comparison, and segment-aware error analysis.
Definitions & key terms
generalization: Performance on unseen data.
precision/recall: Positive prediction quality vs coverage.
MAE/RMSE: Absolute vs squared error emphasis.
calibration: Agreement between predicted probabilities and observed frequencies.
baseline: Minimal reference model to beat.

Mental model diagram

Model outputs -> Metric lens -> Threshold/cost lens -> Segment lens -> Decision
    |               |                  |                |            |
    v               v                  v                v            v
 probabilities     ranking/error      action policy     risk gaps    go/no-go

How it works
1. Pick metrics aligned to business costs.
2. Validate with split strategy matching deployment.
3. Compare against baselines.
4. Tune thresholds on validation data.
5. Slice errors by segment and report uncertainty.
6. Make explicit deployment decision with rationale.

Failure modes:

Accuracy-only reporting for imbalanced tasks.
Threshold chosen arbitrarily.
Ignoring subgroup degradation.

Minimal concrete example

PSEUDOCODE DECISION LOG
task: churn classification
baseline_recall = 0.41 at precision 0.20
candidate_recall = 0.62 at precision 0.27
threshold chosen = 0.34 based on review capacity
segment check: SMB recall drop -0.09 vs enterprise
decision: iterate before deployment

Common misconceptions
“Higher AUC means production-ready.” Correction: operational threshold and segment stability may still fail.
“One split is enough evidence.” Correction: split variance can distort conclusions.
Check-your-understanding questions
1. Why is threshold selection a product decision, not only a modeling decision?
2. When is MAE preferable to RMSE?
3. Why must baselines be reported?
Check-your-understanding answers
1. Threshold controls operational workload and error trade-offs.
2. When outliers should not dominate penalty and interpretability in original units is desired.
3. Baselines reveal whether complexity delivers meaningful value.
Real-world applications
Lead scoring systems.
Default-risk estimation.
Capacity planning forecasts.
Where you’ll apply it
Project 3, Project 4, Project 5, Project 6.
References
scikit-learn docs: Model evaluation
scikit-learn docs: Cross-validation
“An Introduction to Statistical Learning” by James, Witten, Hastie, Tibshirani - Ch. 5
Key insights Evaluation is a decision framework, not a score-reporting exercise.
Summary Generalization quality comes from aligned metrics, valid validation, and explicit threshold/cost reasoning.
Homework/Exercises to practice the concept
1. Define metric and threshold policy for a high-imbalance fraud dataset.
2. Compare two regression models where MAE and RMSE disagree; explain why.
3. Draft a go/no-go model review template.
Solutions to the homework/exercises
1. Optimize recall at a precision floor tied to analyst capacity.
2. RMSE sensitivity to large errors highlights outlier behavior differences.
3. Include baseline delta, segment metrics, threshold rationale, and residual risks.

Glossary

Feature: Input variable used by the model.
Target: Outcome variable the model predicts.
Leakage: Information in training that would be unavailable at real prediction time.
Generalization: Model performance on unseen data.
Stratification: Preserving class ratios across splits.
Pipeline: Reproducible chain of transformations and estimator steps.
Residual: Difference between actual and predicted value.
Calibration: Quality of predicted probabilities vs observed frequencies.
Drift: Change in data distribution over time.
Baseline model: Simple benchmark model used for comparison.

Why Python ML Basics Matters

Modern teams still solve the majority of business ML tasks with tabular pipelines.
Python remains the common implementation language for analytics and ML workflows.

Real-world statistics and impact:

Stack Overflow Developer Survey 2025 reports Python at 53.55% for both all respondents and professional developers in the language section (2025, Stack Overflow).
GitHub’s 2024 year-in-review reports Python as the fastest-growing major language and the most used language on GitHub in 2024 (GitHub, published December 2024).
PyPIStats recent API snapshot (captured February 2026) reports large package demand: NumPy 705,809,255 downloads/month and pandas 504,936,087 downloads/month.

Legacy Analytics Path                  Modern Python ML Path

CSV -> Spreadsheet edits ->            CSV/DB -> Versioned preprocessing ->
manual chart screenshots ->            reusable EDA + pipeline ->
one-off decision                       measured model + repeatable decision

Weakness: hard to reproduce            Strength: reproducible + testable

Context and Evolution (brief):

Early analytics tooling separated statistics, scripting, and visualization.
Python’s ecosystem unified these into interoperable libraries and a shared API style.
scikit-learn standardized fit/transform/predict conventions that still shape production baselines.

Concept Summary Table

Concept Cluster	What You Need to Internalize
Data Representation and Vectorization	Schema contracts, shape/dtype discipline, index alignment, and vectorized transformations are the correctness foundation.
EDA and Visualization as Risk Control	EDA must produce explicit modeling constraints, leakage checks, and segment-aware hypotheses before fitting models.
Supervised Learning Pipelines	Split strategy, preprocessing boundaries, and composed fit/transform/predict flows prevent leakage and improve reproducibility.
Evaluation and Generalization	Metrics, thresholds, baselines, and segment error analysis are required for trustworthy deployment decisions.

Project-to-Concept Map

Project	Concepts Applied
Project 1	Data Representation and Vectorization
Project 2	Data Representation and Vectorization, EDA and Visualization as Risk Control
Project 3	Supervised Learning Pipelines, Evaluation and Generalization
Project 4	EDA and Visualization as Risk Control, Supervised Learning Pipelines, Evaluation and Generalization
Project 5	Data Representation and Vectorization, Supervised Learning Pipelines, Evaluation and Generalization
Project 6	All four concept clusters

Deep Dive Reading by Concept

Concept	Book and Chapter	Why This Matters
Data Representation and Vectorization	“Python for Data Analysis” by Wes McKinney - Ch. 3-8	Practical mastery of pandas/NumPy for robust tabular workflows.
EDA and Visualization as Risk Control	“Practical Statistics for Data Scientists” - Ch. 1-3	Connects exploratory checks to decision-quality analysis.
Supervised Learning Pipelines	“Hands-On Machine Learning” by Aurelien Geron - Ch. 2-3	End-to-end workflow design with preprocessing and model APIs.
Evaluation and Generalization	“An Introduction to Statistical Learning” - Ch. 5	Cross-validation, model assessment, and overfitting control.

Quick Start: Your First 48 Hours

Day 1:

Read Theory Primer concepts 1 and 2.
Start Project 1 and finish vectorized distance and voting pseudocode.
Start Project 2 data audit checklist.

Day 2:

Finish Project 2 EDA report with one segment-level finding.
Read Theory Primer concepts 3 and 4.
Start Project 3 baseline pipeline and initial metric report.

Recommended Learning Paths

Path 1: The Data Analyst Transitioning to ML

Project 2 -> Project 3 -> Project 4 -> Project 6

Path 2: The Software Engineer New to Data Work

Project 1 -> Project 2 -> Project 3 -> Project 5 -> Project 6

Path 3: Interview-Focused Learner

Project 1 -> Project 3 -> Project 4 -> Project 6

Success Metrics

You can explain, without notes, why and where leakage occurs in a tabular workflow.
You can produce a complete model report with baseline comparisons and threshold rationale.
You can reproduce the same pipeline output from clean environment setup.
You can defend metric choices for both regression and classification tasks.

Project Overview Table

#	Project	Main Focus	Difficulty	Time
1	Vectorized KNN From First Principles	NumPy vectorization and shape discipline	Level 2: Intermediate	1 weekend
2	Titanic EDA Storybook	Data auditing, visualization, hypothesis testing	Level 1: Beginner	1 weekend
3	California Housing Regression Pipeline	End-to-end regression workflow	Level 2: Intermediate	1 weekend
4	Iris/Wine Classification Benchmark	Classification metrics and confusion analysis	Level 2: Intermediate	1 weekend
5	Feature Engineering and Model Selection Lab	Transformation design + CV-based comparison	Level 3: Advanced	1-2 weeks
6	Reproducible ML Baseline and Reporting System	Reproducibility, decision logs, deployment readiness	Level 3: Advanced	1-2 weeks

Project List

The following projects guide you from array-level ML intuition to reproducible end-to-end tabular ML delivery.

Project 1: Vectorized KNN From First Principles

File: LEARN_PYTHON_ML_BASICS.md
Main Programming Language: Python
Alternative Programming Languages: Julia, R
Coolness Level: Level 3: Genuinely Clever
Business Potential: 1. The “Resume Gold”
Difficulty: Level 2: Intermediate
Knowledge Area: Numerical Computing / Classification Fundamentals
Software or Tool: NumPy
Main Book: “Python for Data Analysis” by Wes McKinney

What you will build: A from-scratch K-Nearest Neighbors classifier using only vectorized array operations.

Why it teaches Python ML basics: It exposes distance calculation, feature scaling sensitivity, and majority-vote mechanics before abstraction layers hide them.

Core challenges you will face:

Distance matrix design -> vectorization and broadcasting correctness
Neighbor selection -> ordering/argsort behavior and ties
Prediction stability -> handling scale and class imbalance

Real World Outcome

$ ml-lab knn-demo --dataset toy_2d.csv --k 5 --point "2.4,1.7"
Loaded 240 samples, 2 features, 3 classes
Distance computation mode: vectorized
Nearest labels: [A, B, B, B, C]
Vote distribution: A=1 B=3 C=1
Predicted class: B
Confidence proxy (majority ratio): 0.60

A successful result includes deterministic output for fixed dataset ordering, explicit tie-handling behavior, and visible effect of feature scaling in before/after runs.

The Core Question You Are Answering

“How do array operations become a classifier, and what breaks when feature geometry is wrong?”

Concepts You Must Understand First

Vectorized arithmetic and broadcasting
- Can you predict result shapes before running operations?
- Book Reference: “Python for Data Analysis” - Ch. 4
Distance metrics and feature scales
- Why can one large-scale feature dominate Euclidean distance?
- Book Reference: “Hands-On Machine Learning” - Ch. 2
Class imbalance and voting behavior
- Why can majority vote mislead in skewed neighborhoods?
- Book Reference: “Introduction to Statistical Learning” - Ch. 4

Questions to Guide Your Design

Data representation
- How will you enforce shape (n_samples, n_features) consistently?
- What checks prevent target-feature row misalignment?
Prediction policy
- How do you break ties deterministically?
- Should you weight closer neighbors more heavily?

Thinking Exercise

Trace KNN by Hand on a Tiny Dataset

Compute distances from one query point to six labeled points.
Rank neighbors and evaluate k=1, k=3, and k=5 outcomes.

Questions to answer:

When does class prediction flip as k changes?
What happens after multiplying one feature by 100?

The Interview Questions They Will Ask

“Why is KNN sensitive to feature scaling?”
“What is the time complexity of brute-force KNN prediction?”
“When would you avoid KNN in production?”
“How do you handle ties in neighbor voting?”
“What changes when you switch to Manhattan distance?”

Hints in Layers

Hint 1: Start with shape assertions Use explicit shape checks before any distance operation.

Hint 2: Build deterministic sorting policy Sort by distance, then by stable index for tie resolution.

Hint 3: Pseudocode for vectorized distance

distances = sqrt(sum((X_train - x_query)^2 over feature axis))
nearest_idx = argsort(distances)[:k]
label = mode(y_train[nearest_idx])

Hint 4: Debug with scale stress test Multiply one feature by 100 and compare predictions.

Books That Will Help

Topic	Book	Chapter
NumPy arrays and vectorization	“Python for Data Analysis”	Ch. 4
Distance-based learning intuition	“Hands-On Machine Learning”	Ch. 2
Classification fundamentals	“Introduction to Statistical Learning”	Ch. 4

Common Pitfalls and Debugging

Problem 1: “Predictions change unpredictably across reruns”

Why: Non-deterministic tie handling or shuffled row order.
Fix: Use stable sorting and explicit tie policy.
Quick test: Run same query 10 times and diff outputs.

Problem 2: “One feature dominates every prediction”

Why: Scale mismatch across dimensions.
Fix: Apply train-only normalization.
Quick test: Compare distance contribution by feature.

Definition of Done

Vectorized KNN prediction works on reference dataset
Tie policy is documented and deterministic
Scale sensitivity is demonstrated with before/after comparison
Prediction logs include neighbor diagnostics

Project 2: Titanic EDA Storybook

File: LEARN_PYTHON_ML_BASICS.md
Main Programming Language: Python
Alternative Programming Languages: R, Julia
Coolness Level: Level 2: Practical but Forgettable
Business Potential: 1. The “Resume Gold”
Difficulty: Level 1: Beginner
Knowledge Area: Exploratory Data Analysis / Visualization
Software or Tool: pandas, Matplotlib, Seaborn
Main Book: “Python for Data Analysis” by Wes McKinney

What you will build: A narrative EDA notebook/report that explains survival patterns with reproducible data checks and plots.

Why it teaches Python ML basics: It trains the data-debugging mindset that precedes trustworthy modeling.

Core challenges you will face:

Missingness analysis -> imputation policy design
Segment comparisons -> avoiding misleading global averages
Visualization discipline -> each chart answers a concrete question

Real World Outcome

$ ml-lab eda-report --dataset titanic.csv --out reports/titanic_eda.md
Rows: 891 | Columns: 12
Missing values: age=177, cabin=687, embarked=2
Key finding #1: survival_rate(female)=0.742, survival_rate(male)=0.189
Key finding #2: class imbalance by ticket class impacts survival interpretation
Key finding #3: age missingness is non-random across classes
Wrote narrative report: reports/titanic_eda.md
Saved figures: reports/figures/*.png

The Core Question You Are Answering

“What does this dataset actually say, and which assumptions are unsafe before modeling?”

Concepts You Must Understand First

Null semantics and missingness patterns
- Book Reference: “Python for Data Analysis” - Ch. 7
Distribution and skew interpretation
- Book Reference: “Practical Statistics for Data Scientists” - Ch. 1
Segment-level analysis
- Book Reference: “Introduction to Statistical Learning” - Ch. 2

Questions to Guide Your Design

Which variables need subgroup-aware imputation?
Which plots are essential vs redundant?
How do you distinguish correlation from plausible causation stories?

Thinking Exercise

Pre-Model Risk Review

List five ways leakage could appear if you build a survival model immediately.

Questions to answer:

Which columns may encode post-event information?
Which subgroup has most fragile sample support?

The Interview Questions They Will Ask

“What are the first five checks you run in EDA?”
“How do you detect non-random missingness?”
“Why are subgroup plots important?”
“What’s a leakage warning sign from EDA?”
“How do you document EDA decisions for handoff?”

Hints in Layers

Hint 1: Start with profile table Generate one table with dtype, null rate, cardinality.

Hint 2: Build question-driven chart list One chart per hypothesis, not one chart per column.

Hint 3: Pseudocode report structure

for each hypothesis:
  compute grouped summary
  visualize with uncertainty/context
  write decision note (keep/transform/drop)

Hint 4: Include uncertainty statements Small subgroup counts require confidence caveats.

Books That Will Help

Topic	Book	Chapter
pandas profiling and transforms	“Python for Data Analysis”	Ch. 6-8
visual/statistical interpretation	“Practical Statistics for Data Scientists”	Ch. 1-3
framing predictive questions	“Hands-On Machine Learning”	Ch. 2

Common Pitfalls and Debugging

Problem 1: “Charts contradict each other”

Why: Different filters/time windows applied inconsistently.
Fix: Standardize filter context and annotate each plot.
Quick test: Re-run all charts with one shared filtered dataset.

Problem 2: “Report is descriptive but not actionable”

Why: Findings not translated into preprocessing/modeling rules.
Fix: Attach one explicit action to each finding.
Quick test: Can another person derive pipeline steps from your report?

Definition of Done

Data profile table exists with quality checks
At least five hypothesis-driven charts with written interpretation
Leakage and missingness risks documented
Preprocessing recommendations are explicit and testable

Project 3: California Housing Regression Pipeline

File: LEARN_PYTHON_ML_BASICS.md
Main Programming Language: Python
Alternative Programming Languages: R, Julia
Coolness Level: Level 2: Practical but Forgettable
Business Potential: 1. The “Resume Gold”
Difficulty: Level 2: Intermediate
Knowledge Area: Regression / Pipeline Design
Software or Tool: scikit-learn, pandas
Main Book: “Hands-On Machine Learning” by Aurelien Geron

What you will build: A reproducible regression baseline with proper train/validation/test boundaries and metric reporting.

Why it teaches Python ML basics: It introduces the full fit-transform-predict contract under leakage constraints.

Core challenges you will face:

Split strategy -> prevent evaluation contamination
Preprocessing composition -> numeric/categorical pipeline boundaries
Metric interpretation -> MAE vs RMSE trade-offs

Real World Outcome

$ ml-lab train-regression --dataset california_housing.csv --target median_house_value
Split policy: random_state=42, test_size=0.20
Pipeline: impute + scale + linear baseline
Cross-validation (5-fold) RMSE: 0.742 +/- 0.031
Test MAE: 0.512
Test RMSE: 0.769
Residual warning: underprediction for top 10% target band
Saved model artifact: artifacts/regression_baseline.joblib
Saved report: reports/regression_baseline.md

The Core Question You Are Answering

“Can I build a baseline that is statistically honest, reproducible, and useful for iteration?”

Concepts You Must Understand First

Train/validation/test discipline - “Introduction to Statistical Learning” Ch. 5
Feature preprocessing contracts - “Hands-On Machine Learning” Ch. 2
Regression metric behavior - “An Introduction to Statistical Learning” Ch. 3

Questions to Guide Your Design

Which split policy mirrors real deployment timing?
Which features require scaling or transform and why?
Which metric best captures business error cost?

Thinking Exercise

Residual Reasoning Drill

Sketch how underprediction at high target values affects business decisions.

Questions to answer:

If high-value homes are systematically underpredicted, what operational risk appears?
Which feature transforms might reduce that bias?

The Interview Questions They Will Ask

“Why separate validation from test data?”
“What does RMSE emphasize compared to MAE?”
“How do you check for heteroscedasticity quickly?”
“Why keep a simple linear baseline?”
“How do you detect train-test leakage in preprocessing?”

Hints in Layers

Hint 1: Baseline first Start with simple linear model before complex estimators.

Hint 2: Keep pipeline atomic Package preprocessing and estimator together.

Hint 3: Pseudocode training flow

split -> define column transforms -> cross-validate on train -> fit full train -> evaluate test

Hint 4: Add residual slicing Compare residual magnitude by target quantile bands.

Books That Will Help

Topic	Book	Chapter
end-to-end tabular ML workflow	“Hands-On Machine Learning”	Ch. 2
regression diagnostics	“Introduction to Statistical Learning”	Ch. 3
data prep implementation details	“Python for Data Analysis”	Ch. 7-8

Common Pitfalls and Debugging

Problem 1: “Validation score much better than test score”

Why: Hidden leakage or overfitting during tuning.
Fix: Audit split boundaries and tuning procedure.
Quick test: Re-run with stricter split and compare spread.

Problem 2: “Model output unstable across reruns”

Why: Random seeds/partitioning not fixed.
Fix: Set seeds and deterministic split config.
Quick test: Run same command twice and diff reports.

Definition of Done

Reproducible train/eval flow with fixed split policy
Baseline and at least one improved variant compared
MAE and RMSE reported with interpretation
Residual analysis included in final report

Project 4: Iris/Wine Classification Benchmark

File: LEARN_PYTHON_ML_BASICS.md
Main Programming Language: Python
Alternative Programming Languages: R, Julia
Coolness Level: Level 2: Practical but Forgettable
Business Potential: 1. The “Resume Gold”
Difficulty: Level 2: Intermediate
Knowledge Area: Classification / Metrics
Software or Tool: scikit-learn, Seaborn
Main Book: “Hands-On Machine Learning” by Aurelien Geron

What you will build: A benchmark comparing two classification models across confusion matrices, precision/recall trade-offs, and threshold behavior.

Why it teaches Python ML basics: It turns classification metrics from memorization into decision tools.

Core challenges you will face:

Metric selection -> align with class distribution and business costs
Threshold policy -> move beyond default 0.5
Error analysis -> class-specific failure diagnosis

Real World Outcome

$ ml-lab classify-benchmark --dataset wine.csv --target quality_label
Models: logistic_regression, random_forest
CV macro-F1:
  logistic_regression: 0.71 +/- 0.04
  random_forest:       0.76 +/- 0.03
Threshold policy chosen: 0.42 (precision floor 0.60)
Test confusion matrix saved: reports/figures/confusion_matrix.png
Segment warning: class "high_quality" recall=0.48
Final recommendation: random_forest + threshold 0.42

The Core Question You Are Answering

“Which model and threshold produce acceptable error trade-offs for real decisions?”

Concepts You Must Understand First

Precision/recall/F1 trade-offs - ISL Ch. 4
Confusion matrix interpretation - Hands-On ML Ch. 3
Class imbalance effects - Practical Statistics Ch. 4

Questions to Guide Your Design

What is the cost of false positives vs false negatives?
Should threshold vary by operational capacity?
Which classes need targeted improvement?

Thinking Exercise

Threshold Cost Table

Draft a table mapping threshold values to estimated review workload and missed positives.

Questions to answer:

Which threshold is feasible with current operations?
What metric would you optimize if manual review capacity doubles?

The Interview Questions They Will Ask

“Why is accuracy often misleading?”
“How do you select a classification threshold?”
“What does macro-F1 capture?”
“How do you compare two models fairly?”
“What if one class recall is critically low?”

Hints in Layers

Hint 1: Start with confusion matrix Make class-level failures visible first.

Hint 2: Add ROC and PR curves Use curves to inspect threshold-dependent behavior.

Hint 3: Pseudocode threshold selection

for threshold in candidate_grid:
  compute precision, recall, workload_proxy
choose threshold meeting precision floor with max recall

Hint 4: Validate threshold on held-out set only Do not tune threshold on final test set.

Books That Will Help

Topic	Book	Chapter
classification workflow	“Hands-On Machine Learning”	Ch. 3
statistical model assessment	“Introduction to Statistical Learning”	Ch. 4-5
decision metrics in practice	“Practical Statistics for Data Scientists”	Ch. 4

Common Pitfalls and Debugging

Problem 1: “Great AUC, poor real-world precision”

Why: Threshold and class prevalence not aligned to operations.
Fix: Choose threshold using cost/volume constraints.
Quick test: Simulate daily positive volume under chosen threshold.

Problem 2: “One class is never predicted”

Why: Severe class imbalance or poor feature signal.
Fix: Try class weights/resampling and improved feature engineering.
Quick test: Compare recall before/after balancing strategy.

Definition of Done

At least two classifiers evaluated with same split protocol
Confusion matrix and class-wise metrics reported
Threshold policy documented with rationale
Final recommendation tied to operational constraints

Project 5: Feature Engineering and Model Selection Lab

File: LEARN_PYTHON_ML_BASICS.md
Main Programming Language: Python
Alternative Programming Languages: Julia, R
Coolness Level: Level 3: Genuinely Clever
Business Potential: 2. The “Micro-SaaS / Pro Tool”
Difficulty: Level 3: Advanced
Knowledge Area: Feature Engineering / Cross-Validation
Software or Tool: scikit-learn, pandas
Main Book: “Hands-On Machine Learning” by Aurelien Geron

What you will build: A controlled experiment suite comparing feature transformations and model families with cross-validation.

Why it teaches Python ML basics: It demonstrates that feature strategy often outperforms random algorithm hopping.

Core challenges you will face:

Feature hypothesis design -> transformations with causal/semantic rationale
Experiment tracking -> fair comparisons across folds
Overfitting control -> preventing accidental tuning leakage

Real World Outcome

$ ml-lab feature-lab --dataset telco_churn.csv --target churned
Experiment matrix: 12 configs (3 feature sets x 4 estimators)
Top CV ROC-AUC: 0.842 (log-loss=0.391) with engineered tenure bins + interaction features
Baseline ROC-AUC: 0.781
Lift over baseline: +0.061
Top-3 configs exported: reports/feature_lab_rankings.csv
Risk note: highest model variance observed in rare-segment fold #4

The Core Question You Are Answering

“Which feature decisions actually create generalization gains, and which only memorize noise?”

Concepts You Must Understand First

Cross-validation variance - ISL Ch. 5
Feature transformations and interactions - Hands-On ML Ch. 2
Overfitting diagnostics - Practical Statistics Ch. 6

Questions to Guide Your Design

Which transformations have business/causal plausibility?
How will you rank models: primary vs secondary metrics?
What counts as meaningful lift vs noise?

Thinking Exercise

Feature Value Hypothesis Grid

For each candidate feature, write expected signal, risk, and failure mode.

Questions to answer:

Which engineered feature is most leakage-prone?
Which candidate may hurt model stability across segments?

The Interview Questions They Will Ask

“How do you evaluate whether feature engineering helped?”
“What is nested CV and when is it useful?”
“How do you avoid p-hacking model experiments?”
“Why can high-cardinality features hurt generalization?”
“What would you log for reproducible comparisons?”

Hints in Layers

Hint 1: Define experiment matrix first Lock candidate features/models before running.

Hint 2: Keep one untouched comparison protocol Same folds for all model variants.

Hint 3: Pseudocode comparison loop

for config in experiment_matrix:
  cv_metrics = run_cv(config)
  log(config, mean, std, notes)
rank by primary metric then stability

Hint 4: Add variance-aware ranking Prefer slightly lower mean if variance is much lower.

Books That Will Help

Topic	Book	Chapter
practical feature engineering	“Hands-On Machine Learning”	Ch. 2
model comparison principles	“Introduction to Statistical Learning”	Ch. 5
experiment discipline	“Practical Statistics for Data Scientists”	Ch. 6

Common Pitfalls and Debugging

Problem 1: “Best config collapses on test set”

Why: Over-tuning to CV quirks or leakage in feature construction.
Fix: Re-audit feature generation boundaries and use stricter validation.
Quick test: Compare fold variance and test delta.

Problem 2: “Results not reproducible after one week”

Why: Missing config/version metadata.
Fix: Log dataset hash, seed, package versions, and config IDs.
Quick test: Re-run top config in fresh environment.

Definition of Done

Experiment matrix and ranking table produced
Baseline and engineered variants compared fairly
Stability (mean and variance) included in ranking logic
Top configurations reproducible with fixed metadata

Project 6: Reproducible ML Baseline and Reporting System

File: LEARN_PYTHON_ML_BASICS.md
Main Programming Language: Python
Alternative Programming Languages: R, Julia
Coolness Level: Level 3: Genuinely Clever
Business Potential: 3. The “Service and Support” Model
Difficulty: Level 3: Advanced
Knowledge Area: Reproducibility / ML Delivery Basics
Software or Tool: scikit-learn, pandas, CLI tooling
Main Book: “Hands-On Machine Learning” by Aurelien Geron

What you will build: A repeatable command-driven baseline system that outputs artifacts, metric dashboards, and decision logs.

Why it teaches Python ML basics: It bridges notebook success to team-usable deliverables.

Core challenges you will face:

Run determinism -> seed and data-version control
Artifact integrity -> model + preprocessing + schema expectations
Decision communication -> clear go/no-go reports

Real World Outcome

$ ml-lab run-baseline --config configs/churn_baseline.yaml
Config hash: 9f5d1a2
Dataset hash: 72a83c1
Train complete: 00:01:14
Validation ROC-AUC: 0.836 +/- 0.018
Test ROC-AUC: 0.829
Threshold selected: 0.37 (precision>=0.65 policy)
Artifacts:
  artifacts/model.joblib
  artifacts/preprocess_schema.json
  reports/model_card.md
  reports/metrics.json
Exit code: 0

Failure demo:

$ ml-lab run-baseline --config configs/missing_target.yaml
ERROR: target column "churned" not found in dataset schema
Exit code: 2

The Core Question You Are Answering

“Can another engineer run this workflow tomorrow and reach the same decision-quality conclusion?”

Concepts You Must Understand First

Pipeline artifact boundaries - scikit-learn compose docs
Deterministic experiments - ISL Ch. 5
Model reporting discipline - Hands-On ML Ch. 2-3

Questions to Guide Your Design

Which metadata is mandatory for reproducibility?
How will schema drift be detected before training?
What must be in the final model report for stakeholders?

Thinking Exercise

Reproducibility Threat Model

List all reasons two team members may get different metrics from “same code”.

Questions to answer:

Which causes are data-related vs environment-related?
Which safeguards can fail-fast before expensive training?

The Interview Questions They Will Ask

“What makes an ML baseline reproducible?”
“How do you detect schema drift early?”
“What is a model card and why does it matter?”
“How do you communicate residual risk to product stakeholders?”
“What should trigger model retraining?”

Hints in Layers

Hint 1: Treat config as API All run behavior should be config-driven and logged.

Hint 2: Hash everything important Dataset snapshot, config, and dependency versions.

Hint 3: Pseudocode run contract

load config -> validate schema -> split -> train/evaluate -> export artifacts -> write report

Hint 4: Include failure scenarios in CI-style checks Missing target, unseen mandatory column, null explosion.

Books That Will Help

Topic	Book	Chapter
end-to-end baseline workflows	“Hands-On Machine Learning”	Ch. 2-3
data quality discipline	“Python for Data Analysis”	Ch. 7-8
software reliability mindset	“The Pragmatic Programmer”	Ch. 8-9

Common Pitfalls and Debugging

Problem 1: “Works on my machine” training mismatch

Why: Unlocked dependencies and hidden local defaults.
Fix: Pin environment and log package versions.
Quick test: Re-run in clean environment container/venv.

Problem 2: “Artifact predicts nonsense in inference”

Why: Preprocessing mismatch or schema drift.
Fix: Save and enforce preprocessing + schema contract.
Quick test: Run inference sanity suite with known fixtures.

Definition of Done

One-command reproducible training/evaluation pipeline
Artifacts include model, schema, metrics, and decision report
Failure-path behavior documented with non-zero exit codes
Re-run in clean environment reproduces metrics within tolerance

Project Comparison Table

Project	Difficulty	Time	Depth of Understanding	Fun Factor
1. Vectorized KNN From First Principles	Level 2	Weekend	High for geometry intuition	4/5
2. Titanic EDA Storybook	Level 1	Weekend	High for data reasoning	3/5
3. California Housing Regression Pipeline	Level 2	Weekend	High for workflow fundamentals	3/5
4. Iris/Wine Classification Benchmark	Level 2	Weekend	High for metric literacy	3/5
5. Feature Engineering and Model Selection Lab	Level 3	1-2 weeks	Very high for practical iteration	4/5
6. Reproducible ML Baseline and Reporting System	Level 3	1-2 weeks	Very high for production readiness	4/5

Recommendation

If you are new to Python ML: Start with Project 2, then Project 1, then Project 3.

If you are a software engineer moving into ML: Start with Project 1, then Project 3, then Project 6.

If you want interview-ready ML fundamentals: Focus on Project 1, Project 4, and Project 6.

Final Overall Project

The Goal: Combine Projects 2, 3, 5, and 6 into a single end-to-end churn-risk baseline package.

Produce EDA and risk notes for a churn dataset.
Build two baseline pipelines with feature variants and CV comparison.
Export one reproducible artifact set with decision report.
Present threshold policy and segment-level risk findings.

Success Criteria: A reviewer can run your workflow from a clean environment and reproduce your metrics/report with documented assumptions.

From Learning to Production

Your Project	Production Equivalent	Gap to Fill
Project 2 (EDA Storybook)	Data quality dashboard + profiling jobs	automated data contracts, alerts
Project 3 (Regression Pipeline)	Baseline batch scoring service	model registry, scheduled retraining
Project 4 (Classification Benchmark)	Risk scoring service	threshold governance, calibration monitoring
Project 6 (Reproducible Baseline)	Team ML platform baseline template	CI/CD, feature store integration, drift monitors

Summary

This learning path covers Python ML basics through six hands-on projects that move from numerical intuition to reproducible delivery.

#	Project Name	Main Language	Difficulty	Time Estimate
1	Vectorized KNN From First Principles	Python	Intermediate	Weekend
2	Titanic EDA Storybook	Python	Beginner	Weekend
3	California Housing Regression Pipeline	Python	Intermediate	Weekend
4	Iris/Wine Classification Benchmark	Python	Intermediate	Weekend
5	Feature Engineering and Model Selection Lab	Python	Advanced	1-2 weeks
6	Reproducible ML Baseline and Reporting System	Python	Advanced	1-2 weeks

Expected Outcomes

You can design leak-resistant train/evaluation workflows.
You can justify metric and threshold choices with business trade-offs.
You can deliver reproducible baseline artifacts suitable for team handoff.

Additional Resources and References

Standards and Specifications

Industry Analysis

Books

“Python for Data Analysis” by Wes McKinney - best for pandas/NumPy workflow discipline.
“Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow” by Aurelien Geron - practical model workflow patterns.
“An Introduction to Statistical Learning” by James, Witten, Hastie, Tibshirani - strong evaluation and model assessment grounding.