BBA Graduate to Machine Learning Engineer — Career Path

Stage 1 — Daily Schedule (3 hours)

7:00 AM — Maths reading (30 min)
   Weeks 1-5: skip maths, focus Python
   Weeks 6-8: linear algebra
   Weeks 9-11: probability
   Weeks 12-16: calculus

8:00 AM — Coding practice (90 min)
   Write, not copy
   Small programs, applied to business data

Evening — One concept note (30 min)
   Write the concept in your own words

Rules:
✓ No deep learning, no neural networks yet
✓ No Kaggle until you can read a Pandas DataFrame
✓ Every maths concept → one Python implementation
✓ Every Python concept → one business example

What you can skip:
✗ Abstract algebra
✗ Real analysis
✗ Proof-based linear algebra
✗ Anything requiring a maths degree
You need working knowledge, not formal depth.

Weekly review — Stage 1:

Week 1-3 (Python):
□ Can I write a function without looking up syntax?
□ Can I read a file and process it?
□ Do I understand lists vs dicts vs sets?

Week 4-5 (NumPy/Pandas):
□ Can I vectorise a loop with NumPy?
□ Can I group and summarise a DataFrame?
□ Can I merge two DataFrames?

Week 6-8 (Linear algebra):
□ Do I understand a matrix as a transformation?
□ Can I compute a dot product by hand?
□ Can I code matrix multiplication?

Week 9-11 (Probability):
□ Can I explain Bayes theorem in plain words?
□ Do I know the difference between
  independent and conditional probability?
□ Can I interpret a p-value correctly?

Week 12-16 (Calculus):
□ Can I differentiate a simple function?
□ Do I understand the chain rule?
□ Can I explain gradient descent in words?

Project structure for ML portfolio:

1. Business question
   "Can we predict which customers will churn
    in the next 60 days, using only data we have
    at signup and in the first 30 days?"

2. Data
   Source, time range, size
   What you excluded and why

3. Features
   Which features you engineered
   Why these features make business sense
   Which you dropped and why

4. Models tried
   Baseline (logistic regression)
   At least two more (tree, ensemble)
   Comparison table with metrics

5. Evaluation
   Why precision/recall and not accuracy
   Confusion matrix
   Cost of false positives vs false negatives

6. Result
   What the model finds
   What decision it enables
   Which features drive it

7. Limitation
   What the model cannot tell you
   When it would fail

8. Next step
   What to measure in 30 days after deployment

Rule: the "why" matters more than the "how".
Interviewers ask about feature choices first.

Evaluation notes — the metrics that matter:

For every model, answer these:

☐ What's the baseline?
   (Majority class, previous rule, or random)
☐ What's the cost of a false positive?
☐ What's the cost of a false negative?
☐ Which metric captures that cost best?
☐ What's the precision-recall trade-off?
☐ How did you split the data (time vs random)?
☐ Did you do cross-validation?
☐ What's the variance across folds?
☐ What does the model get wrong, and why?

Common beginner mistakes:
✗ Reporting accuracy on imbalanced data
✗ Tuning on test set
✗ Ignoring data leakage
✗ Reporting one number and calling it done
✗ No baseline at all

Real precision matters more than high accuracy.
Interviewers test this specifically.

# app.py — a production-shaped model API
from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import numpy as np

app = FastAPI()

model = joblib.load("models/churn_v1.joblib")
FEATURES = ["tenure_months", "monthly_spend",
            "support_tickets", "plan_tier"]

class PredictionRequest(BaseModel):
    tenure_months: int
    monthly_spend: float
    support_tickets: int
    plan_tier: int

class PredictionResponse(BaseModel):
    churn_probability: float
    decision: str

@app.get("/healthz")
def health():
    return {"status": "ok"}

@app.post("/predict", response_model=PredictionResponse)
def predict(req: PredictionRequest):
    x = np.array([[req.tenure_months, req.monthly_spend,
                   req.support_tickets, req.plan_tier]])
    prob = float(model.predict_proba(x)[0][1])
    decision = "retention_offer" if prob > 0.6 else "no_action"
    return {"churn_probability": prob, "decision": decision}

# Why this shape matters:
# - /healthz for uptime checks
# - typed input, so bad requests fail cleanly
# - the model returns a decision, not just a number
# - this is exactly what an ML engineer ships

Deployment checklist for a model:

☐ Model version is explicit in the filename
☐ Training data version is recorded
☐ /healthz endpoint returns 200
☐ Input is validated (typed, non-null)
☐ Missing inputs are handled gracefully
☐ Errors return a clear message, not a stack trace
☐ Container runs with a non-root user
☐ Secrets are environment variables, not code
☐ Logs show request counts and errors
☐ A /metrics endpoint exists (even simple)
☐ A documented rollback path exists

Interview question, guaranteed:
"How would you deploy this?"
If you can't answer with a URL and a Dockerfile,
you'll lose the "engineer" part of the interview.

That's what makes this stage different from
Stage 2 — this is the actual job.

12-month planner for the BBA path:

MONTHS 1-4 (Foundation)
☐ Python: functions, files, Pandas
☐ Maths: linear algebra, probability, calculus
☐ 20 small programs
☐ 1 data analysis notebook

MONTHS 5-6 (ML Basics)
☐ 3 supervised models
☐ Evaluation metrics
☐ Feature engineering pipeline

MONTHS 7-8 (Projects)
☐ Churn prediction project
☐ Second project (recommendation or pricing)

MONTHS 9-10 (Engineering)
☐ FastAPI model endpoint
☐ Dockerised container
☐ Deployed to a cloud provider

MONTHS 11-12 (Portfolio)
☐ End-to-end deployed ML project
☐ README with trade-offs
☐ Resume rewrite with impact lines
☐ Apply + mock interviews

Target at the end:
☐ 1 deployed model with public URL
☐ 2 portfolio case studies with business framing
☐ 10 mock interviews done

Weekly log:

Week _____

Time spent:
  Maths: ___
  Python: ___
  ML: ___
  Engineering: ___

One thing I learned well:
_______________________________________________

One thing that still confuses me:
_______________________________________________

One thing I shipped:
_______________________________________________

Next week's single priority:
_______________________________________________

Rule: if there's nothing in "shipped", the week
was reading, not learning.
Shipped can be: a function, a plot, a test, a
README paragraph — but something real.

Skill matrix (rate 1-5 monthly):

FOUNDATION
Python basics            [ ]
NumPy / Pandas           [ ]
Linear algebra           [ ]
Probability              [ ]
Calculus (derivatives)   [ ]

ML
Supervised models        [ ]
Evaluation metrics       [ ]
Feature engineering      [ ]
Unsupervised / PCA       [ ]
Model interpretation     [ ]

ENGINEERING
APIs (FastAPI)           [ ]
Docker                   [ ]
Cloud deployment         [ ]
MLOps basics             [ ]
Git / versioning         [ ]

BUSINESS
Problem framing          [ ]
Stakeholder communication[ ]
Cost-benefit reasoning   [ ]

Target: 3+ in every row.
Anything at 1-2 = your focus next month.
Anything at 4-5 = your differentiator in interviews.