Backend Engineer to ML Engineer Roadmap

A step-by-step plan to help backend engineers start a career in machine learning by building skills, applying knowledge, and creating a showcase ML project.

Weekly Hours: 10
Estimated Weeks: 5

Phases

Foundations of Machine Learning

Learn basic ML concepts and key math ideas used in machine learning. Start by understanding how algorithms learn from data and why math matters in ML.

1 weeks

Understand what machine learning is
Know types of ML: supervised, unsupervised, reinforcement
Review key math: linear algebra, statistics
Explore real ML problems

Describe ML in simple terms (example: predicting house prices)
Explain supervised vs unsupervised (example: classifying emails, grouping images)
Use basic math for ML (example: compute averages, solve equations)

Intro to Machine Learning courses
Basic Linear Algebra and Statistics books
Online math tutorials

Write short notes explaining ML types
List 3 real-world problems suitable for ML
Solve 3 practice math exercises for ML

Python and ML Libraries

Learn Python, the main language for ML, and key ML libraries. Practice using code to handle data, run simple models, and use ML tools.

1 weeks

Write basic Python scripts
Use NumPy and pandas for data handling
Try simple models with scikit-learn
Read and plot small datasets

Clean data with pandas (example: fix missing values)
Run simple regression in scikit-learn (example: predict sales)
Use NumPy for math (example: calculate matrix products)

Python for Beginners
Pandas official guides
Scikit-learn tutorials

Clean a sample dataset using pandas
Train a linear regression model in scikit-learn
Create and save a simple plot of results

Core ML Concepts and Feature Engineering

Deepen machine learning knowledge, focusing on how to prepare real data for ML models and evaluate how well models work.

1 weeks

Understand overfitting and underfitting
Prepare data using feature engineering
Apply train/test splits to validate results
Evaluate models with metrics

Split data for testing (example: use train_test_split from scikit-learn)
Build new features from raw data (example: extract time from dates)
Evaluate accuracy (example: use confusion matrix for classifications)

Online ML concept lessons
Feature Engineering tutorials
Evaluation metrics guides

Create and explain a train/test data split
Build and document at least 2 engineered features
Run and explain model metrics on test data

Showcase ML Project: Job Resume Classifier

Build, test, and deploy a simple project that takes resumes and predicts job categories, using all skills learned. Document and test everything to show your ML abilities.

2 weeks

Develop an end-to-end ML pipeline
Clean and process text resume data
Train and test a ML classifier
Deploy the model as an API
Write clear README and tests

Process text data (example: transform and vectorize resumes)
Build and train a classifier (example: logistic regression in scikit-learn)
Write API endpoints (example: FastAPI to serve predictions)
Test endpoints (example: use pytest or Jest)

Sample resume datasets
FastAPI documentation
Deployment tutorials

Demo: ML API predicts job category from resume text
Test: Model passes automated input tests (with pytest or Jest)
Docs: README includes setup instructions and usage examples
CI badge: Setup GitHub Actions to auto-run tests on push

Weekly Plan

Week	Focus	Why	Tasks	Deliverables
1	Learning ML basics and math	Build the right mental models before starting ML code.	Read ML introduction (Intro to Machine Learning), List types of ML with examples, Work through online math exercises (Linear Algebra, Statistics), Write notes on how ML works, Review real ML use cases (online articles)	Short summary of ML types, Notebook with solved math problems
2	Practicing Python and ML libraries	Python is the main language for machine learning work.	Set up Python and Jupyter Notebook, Try data cleaning tasks (pandas), Run NumPy demos for math, Load and plot dataset (matplotlib), Fit basic regression with scikit-learn	Jupyter notebook with Python and pandas code, Plot image output from sample data
3	Digging into core ML processes	Solid ML depends on getting data and evaluation right.	Split data into train/test sets (scikit-learn), Create custom features from sample data, Train classification model, Calculate evaluation metrics (accuracy, confusion matrix), Compare model results	Notebook with train/test splits and results, Short document comparing metrics
4	Building the resume classifier pipeline	A showcase project proves your ML engineer abilities.	Fetch and clean sample resume texts, Convert text into ML features (CountVectorizer), Train classifier model (logistic regression), Test predictions, save model artifact, Write README explaining setup	Working resume classifier script, Draft README document
5	Deploying, testing, and documenting your project	Deployment and testing show readiness for ML engineer roles.	Build API to predict from resumes (FastAPI), Write automated tests (pytest or Jest), Deploy API to free cloud service (Render, Heroku), Setup GitHub Actions for automatic tests, Polish and update full documentation	Live API demo link, Auto test badge in README