Plan for Getting Your First Machine Learning Engineer Job

A simple roadmap to help students start in machine learning. It covers skills, a real project, and how to show your work to employers.

Weekly Hours: 10
Estimated Weeks: 8

Phases

Foundations of Programming and Math

Start with basic Python programming and simple math. This helps you understand how to build and test your own code in machine learning.

2 weeks

Learn Python basics
Understand math for ML
Practice writing simple programs
Solve basic math problems

Write Python scripts (hello world, basic calculator)
Use variables and loops (for calculating averages)
Apply linear algebra (vectors, matrices)
Work with data files (CSV import)

Python Beginner Course
Math for Machine Learning
Python Playground
Free Math Worksheets

Finish 5 basic Python programs with comments
Submit 10 solved math practice problems
Import a CSV and process data (Python code)
Document steps in a short PDF

Learn Machine Learning Basics

Explore what machine learning is and use simple tools. Build your first tiny model, and learn to judge if it works.

2 weeks

Understand types of machine learning
Try simple ML models
Work with popular tools
Read ML datasets

Train a linear regression model (scikit-learn on housing prices)
Split data into train and test sets
Measure accuracy (calculate score)
Plot results (matplotlib charts)

Intro to Scikit-learn
Basic ML Tutorial
Dataset Repository
Jupyter Notebooks

Code a simple ML model on real data
Evaluate and explain model accuracy
Share graphs of model results
Write a short Model Report

Project: House Price Predictor

Build and improve a machine learning app that predicts house prices. Add features, test changes, and make it user-friendly. You will also show others how it works and why it is useful.

3 weeks

Create a working ML app
Add features like user input
Test and debug your code
Prepare demo and README

Build a command-line app (Python CLI for price prediction)
Handle user input and outputs (ask for data, print prediction)
Add data checks (handle missing values)
Write automated tests (pytest scripts)

Python CLI Example
Pytest Docs
ML Project Starter Template
Best README Examples

Finish House Price Predictor with user input
Pass 5 automated tests on edge cases
Record a 2-min demo video
Publish final code and README on GitHub

Showcasing and Applying

Get your project ready to share. Make your code easy to find, clear, and tested. Use your project to apply for jobs.

1 weeks

Polish your code and documentation
Set up automated tests
Write a clear project summary
Apply for entry-level roles

Set up GitHub Actions for testing (automatic test runs)
Write a project summary section (in your README)
Share your link on a job form
Send a short project email

GitHub Actions Guide
Job Portal
Sample Project Summaries
Simple Portfolio Template

Earn a 'build passing' badge on GitHub
Submit at least 3 job applications
Share project link with a mentor for review

Weekly Plan

Week	Focus	Why	Tasks	Deliverables
1	Start with Python and math basics	You need these skills for every machine learning job.	Set up Python and Jupyter (Anaconda), Write 3 simple Python programs (sum, average, hello world), Review basic algebra (solve linear equations), Complete 5 practice problems from Math for ML	PDF of Python code files, List of solved math exercises
2	Practice data skills and simple code	Data handling is essential for machine learning tasks.	Import a CSV using pandas, Clean dataset (remove missing values), Answer 3 questions about data, Write a program to compute mean and median	CSV import code sample, Short data analysis report
3	Learn and try out ML basics	You will use these models in your project.	Read about types of ML (supervised, unsupervised), Train linear regression on a small set (scikit-learn), Plot results with matplotlib, Evaluate accuracy (compare predictions)	Jupyter notebook with ML code, PDF report with graphs
4	Apply ML to a simple project	Project work gets you noticed by employers.	Download house prices dataset, Train a new regression model, Explain your findings in comments, Plan project features in notes	First version of project code, Project ideas document
5	Add features and user interactions	Real apps need user inputs and error handling.	Add user input (ask for house details in CLI), Check for missing or wrong data, Print predictions to the screen, Test the program with 3 examples	Improved project app, Demo screenshots
6	Testing and improving your project	Testing shows your code is reliable.	Write 5 automated tests (pytest), Fix bugs found in testing, Improve prediction accuracy (try new features), Update project documentation (add usage steps)	Test report (pytest results), Updated README
7	Prepare your project for sharing	Clear, clean projects impress recruiters.	Format code (use Black or autopep8), Set up GitHub Actions for CI/testing, Make a 2-minute screen recording of the app, Add a summary section in README	GitHub link with test badge, Project demo video
8	Apply for jobs and get feedback	You need to show your work to land interviews.	Send project link to a mentor or job board, Apply for 3 entry-level ML jobs online, Update your resume with project details, Review and reply to feedback (if you get any)	3 job applications sent, Short list of feedback or reply from mentor