Personal Learning Roadmap

Your path to
Machine Learning

2 hrs / day
6–8 months to ML-ready
Python · Pandas · SQL · ML
Starting level: Python basics
6-phase roadmap
Estimated timeline at 2 hrs/day
Month 1 Month 2 Month 3 Month 4 Month 5–6 Month 7–8+
1
Fill Python gaps
~2–3 weeks
Close the gaps from CS50 — file handling, error handling, and OOP basics. End with a real project.
File I/O Error handling OOP basics Project: expense tracker
↓
2
Data tools (ML prerequisites)
~5–6 weeks
Core data skills every ML engineer needs daily. SQL and EDA are treated as tools, not a DA career track.
NumPy Pandas SQL basics Matplotlib Seaborn EDA
↓
3
Applied statistics for ML
~3–4 weeks
Only the statistics ML actually uses. No heavy theory — distributions, probability, correlation, and Bayes.
Distributions Hypothesis testing Correlation Probability & Bayes
↓
4
Core ML
~6–8 weeks
Scikit-learn is your main tool here. Build, evaluate, and tune real models on real datasets.
Scikit-learn Regression Classification Model evaluation Feature engineering Pipelines
↓
5
Deep learning intro
~6–8 weeks
Neural networks and PyTorch. Start competing on Kaggle. This is where portfolios get built.
Neural networks PyTorch CNNs NLP basics Kaggle competitions
↓
6
Specialize
~3–6 months · ongoing
Pick your lane based on what excites you most. All three paths share the same foundation below.
ML Engineer Data Scientist Data Engineer
specialization paths
Path 01
ML Engineer
Build and deploy ML systems in production. Strong Python + engineering skills rewarded.
  • PyTorch / TensorFlow
  • Docker + FastAPI
  • MLflow, model serving
  • CI/CD for ML pipelines
Recommended starting target
Path 02
Data Scientist
Research-heavy. Design experiments, build models, interpret results for business decisions.
  • Advanced statistics
  • R or Python notebooks
  • A/B testing + experimentation
  • Business communication
Often needs strong portfolio
Path 03
Data Engineer
Build the pipelines that feed data to ML systems. More backend + cloud oriented.
  • Apache Spark + Kafka
  • Airflow, dbt
  • AWS / GCP / Azure
  • Advanced SQL
Good if you enjoy infra
Advice
You don't need to decide now. Phases 1–5 are identical regardless of specialization. As you build projects, you'll develop a natural feel for which lane excites you most. ML Engineer is the safest first target — it doesn't require a degree and rewards the exact skills you're building.
Generated for your personal learning journey
2 hrs/day · Lagos, Nigeria · 2026