Learning Paths: From Zero to Solving Real Industry Problems πŸ›€οΈ

Resources
Learning Paths
Use Cases
Step-by-step paths for eight industrial use cases, from churn and fraud to predictive maintenance and visual inspection, with open datasets, metrics, an animated roadmap and a worked business example.
Author

MLStation

Published

October 1, 2026

Most courses are organised by tool: a week of pandas, a week of regression. Companies hire you to solve problems: β€œWhich customers will leave?”, β€œHow much stock do we need next week?”, β€œIs this machine about to fail?”. These learning paths are organised by problem. Each one tells you what to learn, in what order, on which open dataset, and how to measure success in business terms. 🏭

Tip🎯 What you will get
  • The shared foundation every path builds on
  • A six-stage workflow used across industry projects
  • An animated roadmap for eight use cases, each with an open dataset and metric
  • A skills profile per use case
  • A worked example that turns a churn model into dollars saved
NoteπŸ“š The Resources hub

🧭 Overview: where to find resources Β· πŸ›€οΈ Learning paths Β· πŸ“ Cheat sheets Β· πŸ“– Glossary Β· πŸ—„οΈ 20 open data sources Β· πŸ“„ Paper notes (coming soon)

🧱 The shared foundation

Every industrial path starts on the same base. You do not need to master it all before starting a project; learn each piece when a project needs it.

Step Learn here on MLStation Outside reference
πŸ“Š Statistics p-values, normal distribution, linear regression ISLP
πŸ€– Core ML logistic regression, gradient descent, k-means Kaggle Learn
🧠 Deep learning ANN to DNN, CNNs, object detection fast.ai
πŸš€ Deployment Deployment series Docker docs

πŸ”„ The six-stage industry workflow

Industry projects follow a loop very close to CRISP-DM, the cross-industry standard process for data mining. Notice that modelling is only one of six stages.

flowchart TB
  A["πŸ’Ό 1. Business<br/>understanding"] --> B["πŸ” 2. Data<br/>understanding"]
  B --> C["🧹 3. Data<br/>preparation"]
  C --> D["πŸ€– 4. Modelling"]
  D --> E["βœ… 5. Evaluation<br/>in business terms"]
  E --> F["πŸš€ 6. Deployment<br/>and monitoring"]
  E -. not good enough .-> A
  F -. new data, drift .-> B

NoteπŸ“ Note

In real projects, stages 1 to 3 often take more than half the time. A model that answers the wrong business question is worthless, however accurate it is.

πŸ—ΊοΈ Eight industrial paths

Pick a use case. The roadmap animates through the six stages with the concrete task for each.

πŸ•ΈοΈ Which skills does each path stress?

Each use case leans on a different mix of skills. Use the menu to switch use cases. Scores are an editorial judgement on a 1–5 scale.

Show the code 
import plotly.graph_objects as go
from ms_charts import COLORS, PLOTLY_CONFIG
skills = ["SQL and data wrangling", "Statistics", "Classical ML", "Deep learning", "Time series", "Deployment", "Business framing"]
profiles = {
    "πŸ“‰ Customer churn":        [4, 3, 4, 1, 2, 3, 5],
    "πŸ“¦ Demand forecasting":    [4, 4, 3, 2, 5, 3, 4],
    "πŸ’³ Fraud detection":       [4, 3, 5, 2, 2, 5, 4],
    "βš™οΈ Predictive maintenance": [3, 3, 4, 4, 5, 4, 3],
    "🧩 Customer segmentation": [4, 4, 3, 1, 1, 2, 5],
    "🎫 Ticket triage (NLP)":   [2, 2, 3, 5, 1, 4, 3],
    "πŸ” Visual inspection":     [1, 2, 2, 5, 1, 5, 3],
    "πŸ›’ Recommendations":       [4, 3, 4, 3, 2, 5, 4],
}
names = list(profiles)
fig = go.Figure()
for i, (n, v) in enumerate(profiles.items()):
    fig.add_trace(go.Scatterpolar(r=v + [v[0]], theta=skills + [skills[0]], fill="toself", name=n, visible=(i == 0),
                                  line=dict(color=COLORS["indigo"], width=2.5), fillcolor="rgba(99,102,241,0.25)"))
fig.update_layout(
    height=460, showlegend=False, polar=dict(radialaxis=dict(range=[0, 5], tickvals=[1, 2, 3, 4, 5])),
    updatemenus=[dict(x=0, y=1.15, xanchor="left", buttons=[dict(label=n, method="update", args=[{"visible": [j == i for j in range(len(names))]}]) for i, n in enumerate(names)])])
fig.show(config=PLOTLY_CONFIG)
Figure 1: Skill profile by use case (1 = light, 5 = heavy). Editorial estimates to help plan your learning.

πŸ’° Worked example: turning a churn model into dollars

Industry cares about value, not AUC. Here is the key step most tutorials skip. We simulate 10,000 subscribers, train a model, and ask: β€œIf we call the riskiest customers with a retention offer, how many should we call?”

Assumptions (edit them in the code): a saved customer is worth $300, each call plus offer costs $25, and an offer saves 35% of customers who would otherwise leave.

Show the code 
import numpy as np, pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
rng = np.random.default_rng(10)
n = 10_000
df = pd.DataFrame({
    "tenure_months": rng.integers(1, 72, n),
    "monthly_charge": rng.normal(70, 25, n).clip(15, 150),
    "support_calls": rng.poisson(1.5, n),
    "month_to_month": rng.binomial(1, 0.55, n),
})
logit = -1.2 - 0.04 * df.tenure_months + 0.012 * df.monthly_charge + 0.35 * df.support_calls + 1.1 * df.month_to_month
df["churn"] = rng.binomial(1, 1 / (1 + np.exp(-logit)))
X, y = df.drop(columns="churn"), df.churn
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.5, random_state=0)
p = LogisticRegression(max_iter=1000).fit(Xtr, ytr).predict_proba(Xte)[:, 1]
auc = roc_auc_score(yte, p)

VALUE, COST, SAVE = 300, 25, 0.35
order = np.argsort(-p)
churners = yte.values[order]
k = np.arange(1, len(order) + 1)
profit = SAVE * VALUE * np.cumsum(churners) - COST * k
best = int(np.argmax(profit))
share = k / len(k) * 100

fig = go.Figure()
fig.add_scatter(x=share, y=profit, line=dict(color=COLORS["teal"], width=3.5), name="Profit")
fig.add_scatter(x=[share[best]], y=[profit[best]], mode="markers+text", marker=dict(size=14, color=COLORS["amber"]),
                text=[f"best: contact top {share[best]:.0f}%"], textposition="top right", name="Best")
fig.add_hline(y=0, line_dash="dash", line_color=COLORS["slate"])
fig.update_layout(height=380, xaxis_title="% of customers contacted (riskiest first)", yaxis_title="Campaign profit ($)", showlegend=False)
fig.show(config=PLOTLY_CONFIG)
Figure 2: Simulated profit of a retention campaign vs how many top-risk customers we contact. Calling everyone loses money; calling the right 20–30% maximises profit.

The model has an AUC of 0.76, but the useful answer is: contact the riskiest 62% of customers for a simulated profit of about $81,015 on this group, while contacting everyone would earn $60,325. That one chart is what a manager remembers. πŸ’‘

πŸ“‹ All eight paths at a glance

Table 1: Baselines and traps
Use case Baseline first Then try Watch out for
πŸ“‰ Churn Logistic regression Gradient boosting Leakage from β€œcancellation” fields
πŸ“¦ Forecasting Seasonal naive (same week last year) LightGBM with lags Random splits on time data
πŸ’³ Fraud Rules + logistic regression Gradient boosting Accuracy on 99.8% legit data
βš™οΈ Maintenance Threshold on one sensor Random forest, LSTM Engines split across train and test
🧩 Segmentation RFM quantile buckets k-means, GMM Segments nobody can act on
🎫 Ticket triage Keyword rules TF-IDF + linear, transformer Personal data in text
πŸ” Inspection Pre-trained CNN features Anomaly detection Lighting changes on the line
πŸ›’ Recommendations Most popular items Matrix factorisation Popularity bias, cold start
Important⚠️ Always beat a simple baseline first

If gradient boosting cannot clearly beat β€œsame week last year” or β€œmost popular items”, the problem is in the data or the framing, not the model.

🧩 Summary

  • Learn by problem, not by tool: pick one use case and follow its six stages.
  • Each path has an open dataset, a model metric and a business KPI; report all three.
  • Start with a simple baseline, then improve.
  • Translate model output into money or time saved, as in the churn profit curve.
  • Next: keep the cheat sheets πŸ“ open while you work.
Back to top