Roadmap to Your First Machine Learning Engineer Job

Start as a student and learn what you need to become a machine learning engineer. Build one main project and get job-ready skills in 8 weeks.

Weekly Hours: 10
Estimated Weeks: 8

Phases

Getting Started with Python and Data

Build a strong base with Python and basic data handling. You will write simple programs, load data, and do basic math needed for machine learning.

2 weeks

Understand simple programming ideas
Work with numbers and lists
Read and write simple code
Use data tools to open and view files

Write Python code (hello world, lists, loops)
Use NumPy to do math (arrays)
Read data files (CSV with pandas)
Use Jupyter Notebook for code

Python Beginner Tutorial
NumPy Guide
Pandas Basics
Jupyter Notebook Starter

Finish a small Python script (loads and summarizes a CSV file)
Show code as a notebook (Jupyter Notebook link)

Intro to Machine Learning Concepts

Learn what machine learning is and how it works. You will try simple models to see how predictions happen.

2 weeks

Know what machine learning does
Try basic models on data
Measure if a model works
Understand simple prediction tasks

Train a model (scikit-learn LinearRegression)
Split data into train and test sets
Measure performance (accuracy, mean absolute error)
Make predictions from data

scikit-learn Quickstart
Intro to Machine Learning Video
Hands-On ML Book (first chapters)

Create a project folder with a reproducible notebook
Submit a notebook that predicts house prices (with results, graphs)
Add test data and report model score

Building and Testing a Real-World ML Project

Pick one project idea and see it through. Gather data, clean it, build a working model, and explain your steps.

3 weeks

Plan a full project (problem, steps)
Gather and clean data
Test more than one model
Check results and fine-tune

Data cleaning (drop missing, handle outliers in pandas)
Test different models (LogisticRegression, DecisionTree in scikit-learn)
Show results with charts (matplotlib)
Write explanations for your work

Public Data Sources List
Data Cleaning Tutorials
scikit-learn Model Selection Guide
matplotlib Intro

Finish a working notebook for one real dataset (e.g., Titanic survival)
Upload project to GitHub with code, charts, and explanations
Write a README explaining your approach

Deployment and Application

Make your machine learning project available for others. Share your work online and show how it can be used.

1 weeks

Save your model for reuse
Make a simple web form to use your model
Write clear instructions for others
Check that everything works

Save models (joblib or pickle)
Create a web form (Streamlit or Flask)
Write usage docs (Markdown file)
Test deployed app for inputs

Streamlit Quickstart
Flask Simple App Example
Intro to Pickle/joblib

Share a live app link (Streamlit Cloud or PythonAnywhere)
Provide usage docs with screenshots
Show app tested with sample data

Weekly Plan

Week	Focus	Why	Tasks	Deliverables
1	Learn Python basics and Jupyter Notebooks	You need to write code for any ML work.	Install Python and Jupyter Notebook, Complete basic Python exercises (variables, loops, functions), Try out NumPy with math operations, Load a CSV file using pandas	Jupyter notebook showing Python and NumPy exercises
2	Practice working with data files	All ML starts with raw data.	Read datasets (pandas), Summarize data (describe, info, value_counts), Handle missing data (dropna, fillna), Visualize simple plots (histograms, matplotlib)	Notebook summarizing and plotting a real dataset
3	Apply your first machine learning model	Hands-on practice builds confidence with ML tools.	Split data into train and test sets (scikit-learn train_test_split), Train a simple model (LinearRegression or LogisticRegression), Make predictions on test data, Check performance (scikit-learn metrics)	Notebook with model results and performance score
4	Explore different ML models and evaluation	Trying various models shows strengths and weaknesses.	Test at least two models (e.g., DecisionTree, KNeighbors), Plot and compare their results, Analyze which works better and why, Write notes on what you learn	Comparison notebook (models, results, short explanations)
5	Start your main showcase ML project	A deep project helps you learn and show your skills.	Pick a public data set (e.g., Titanic, Iris), Describe project goal and steps in a README, Plan data cleaning and model testing, Create new GitHub repository for the project	Project plan and GitHub repo with README
6	Build and refine your main project	Iterating makes the project stronger.	Clean and prepare data for modeling, Test multiple ML models (scikit-learn), Visualize results with charts, Document your workflow in notebook	Updated notebook with full code and charts
7	Deploy your model as a web app	Deployment makes your project useful and visible.	Export ML model (joblib or pickle), Build a web app (Streamlit or Flask form), Add input fields for new data, Test locally with example inputs	Live or demo-ready web app for your model
8	Polish, document, and share your project	Clear docs and sharing prove you are job-ready.	Write detailed instructions (Markdown README), Add screenshots of your app, Test everything (check input/output), Submit project link and code to peers/mentors	Complete GitHub repo with docs and usage guide

Daily Plan

Monday

Review weekly goals
Read one new concept or tutorial

Tuesday

Write code for current topic
Try one practice exercise

Wednesday

Continue coding or fix bugs
Test your code with new inputs

Thursday

Summarize your progress in notes
Share question or insight in a forum

Friday

Polish or finish main tasks
Backup code and update your project repo