Tech moves fast. Memorizing every algorithm is a losing game.
Instead, get good at:
- Problem decomposition – turn “predict customer churn” into tidy sub-problems.
- Resource hunting – know where to look when the docs fail.
- 80/20 filtering – ignore the blog-post-of-the-week until you actually need it.
If you can do those three things, the rest is just execution.
Step 1: Python & the Notebook Habit (Week 1–2)
Start here because you’ll see results today.
Install Python and JupyterLab.
Then cover the absolute basics:
| Topic | Why it matters | One-liner test |
|---|---|---|
| Variables & types | You’ll use these every day. | age = 32; type(age) |
| Lists/dicts | Every dataset becomes one of these. | df['income'].mean() |
| Functions | Keeps code reusable. | def clean(df): return df.dropna() |
pandas |
Excel on steroids. | df.groupby('state').sales.sum() |
Do: One beginner course—pick any you like.
Don’t: finish three courses “just to be safe.” One is enough; the rest you’ll pick up by building.
Step 2: Your First Real Data Project (Week 3–4)
Pick a CSV you care about—your Spotify history, city bike-share logs, anything.
Goal: load → clean → plot → tell a story.
Checklist:
-
Import with
pandas.read_csv() - Clean nulls, units, outliers.
-
Explore correlations (
df.corr()). -
Visualize with
matplotlib/seaborn. -
Present as a 5-slide Jupyter slideshow (
jupyter nbconvert slides.ipynb --to slides --post serve).
Push the repo to GitHub. Congratulations—you now have a portfolio piece.
Step 3: The Math You Actually Need (Week 5–8)
Skip the proofs; learn the intuition.
| Area | Core Concepts | Time Budget | Free Resource |
|---|---|---|---|
| Stats & Probability | mean, variance, distributions, Bayes | 2–3 weeks | Khan Academy Stats |
| Linear Algebra | vectors, dot products, matrix multiply | 1 week | Khan Academy Linear Algebra |
| Calculus | derivatives, gradient intuition | 1 week | Khan Academy Calculus |
Pro tip: after each new concept, reopen your earlier project and ask a question that needs it (e.g., “How does standard deviation change if I drop outliers?”). Learning sticks when it solves a problem you already have.
Step 4: Core ML Algorithms (Week 9–12)
Resist the urge to binge-watch every neural-network hype video.
Instead, lock in these five:
- Linear regression – the mother of all models.
- Logistic regression – classification gateway drug.
- Decision trees – white-box, human-readable.
- Random forest & gradient boosting – practical powerhouses.
- k-means clustering – when you don’t have labels.
Resources:
- Book (free PDF): An Introduction to Statistical Learning
- Companion videos: StatLearning YouTube series
- Code-along: scikit-learn’s official tutorial
Work each algorithm three ways:
- From scratch (plain Python)
- With scikit-learn (one-liner
.fit()) - On your own dataset
That’s where real understanding happens.
Step 5: Second Project—End-to-End ML (Week 13–16)
Choose a Kaggle dataset that’s not Titanic (every recruiter has seen it).
Follow this loop:
- EDA with
pandas - Feature engineering
- Baseline model (linear regression)
- Iterate (random forest, XGBoost)
- Validate properly (train/validation/test split)
- Write a README with results & business takeaway.
Push to GitHub and link it on your résumé.
A small, clean project beats a half-finished “Uber-for-ML” behemoth every time.
Step 6: Collaborate & Ship (Week 17+)
Learning alone is slow. Speed it up:
- Pair program—find a buddy on Discord/Reddit.
- Code review—ask for PR feedback in Kaggle kernels or GitHub.
- Talk about it—present your findings to a local meetup or on Zoom.
These interactions teach you the unspoken rules: how to structure repos, write readable code, and explain models to non-technical stakeholders.
Step 7: Advanced Topics—Only When You Need Them
| Topic | Learn When | Starter Resource |
|---|---|---|
| Deep learning (CNNs, RNNs, Transformers) | Your tabular data hits a ceiling | Andrej Karpathy “Neural Networks: Zero to Hero” |
| Model deployment | Someone asks for an API | FastAPI + Docker tutorial |
| MLOps | Your models break in production | Made With ML course |
Follow the need-not-FOMO rule: if a technique doesn’t solve a problem you have, bookmark it and move on.
Quick Dos & Don’ts
| Do | Don’t |
|---|---|
| Build one small project to completion | Chase every new framework release |
| Read the scikit-learn docs | Copy-paste code you don’t understand |
| Ask “why am I learning this?” every hour | Memorize hyper-parameters by heart |
| Share your work publicly | Learn in isolation |
Timeline at a Glance
| Weeks | Focus | Deliverable |
|---|---|---|
| 1–2 | Python + Jupyter | “Hello, data” notebook |
| 3–4 | Data project | GitHub repo + slideshow |
| 5–8 | Math foundations | Updated project with stats |
| 9–12 | Core ML algorithms | Algorithm comparison notebook |
| 13–16 | End-to-end ML project | Kaggle/GitHub repo + README |
| 17+ | Collaborate & specialize | Peer-reviewed contributions |
Lastly
You won’t feel “ready.” No one does.
But after ~4–6 months of consistent work you’ll have:
- At-least three solid GitHub projects
- A grasp of the math that matters
- A network of peers who push you forward.
That combination is what hiring managers actually care about—not the certificate that claims you finished a 3-month bootcamp in record time.
Now close this tab, open Jupyter, and start with Step 1.
Top comments (0)