Beginner Roadmap to Your First Job in AI/ML

Step-by-step guide for students to learn AI/ML basics, build a project, and show employers your skills.

Weekly Hours: 10
Estimated Weeks: 8

Phases

Get Comfortable with Python and Data Basics

Learn Python, simple math, and how to work with data. This will set a strong foundation for AI and machine learning.

2 weeks

Write simple Python programs
Understand numbers, lists, and code flow
Work with data tables (using pandas)
Read simple graphs

Write Python scripts (print, loops)
Read and plot data (matplotlib)
Clean up data (pandas dropna)
Solve math problems (mean, median)

Python Beginner Tutorial
Pandas Documentation
Khan Academy: Intro to Statistics

Complete a data cleanup task with pandas
Plot a basic chart using matplotlib
Share a simple Jupyter Notebook with working code

Learn Core AI/ML Concepts

Understand what machine learning is and how it's used. Try simple models using libraries. Start thinking of a small project to build.

2 weeks

Explain machine learning in simple terms
Use scikit-learn for a tiny ML task
Pick a real-world problem for a mini project
Understand model basics: train, test, predict

Train a simple model (scikit-learn’s LinearRegression)
Split data (train_test_split)
Test predictions (accuracy score)
Describe datasets (features, labels)

Scikit-learn Official Tutorial
Google Machine Learning Crash Course

Run a regression model on test data
Summarize results in a short report
Choose your showcase project topic and dataset

Build and Polish Your Showcase Project

Create a small but complete AI project. Cover all steps: loading, cleaning, training, testing, and deployment (putting it online). Document your work.

3 weeks

Clean and prepare your data
Train and tune one model
Test model and review results
Share your project online

Build a full data pipeline (cleaning to prediction)
Write reusable functions (Python)
Test code and results
Deploy a simple web app (Streamlit)

Streamlit Beginner Guide
GitHub Basics
Scikit-learn Model Guides

Share project on GitHub with README and sample data
Deploy project as a mini web app (Streamlit)
Write basic tests for model functions
Earn CI badge (GitHub Actions passing)

Showcase, Apply, and Reflect

Make your project easy to show and explain to others. Practice talking about it. Start applying to jobs and asking for feedback.

1 weeks

Polish GitHub repo: clear README, instructions
Prepare a 2-minute project summary
Practice explaining your work
Send first job application

Write short project summaries
Present project in 2 minutes
Apply to entry-level jobs online

Sample ML Project Repos
Tips for Technical Interviews
Resume Examples (Entry Level)

Publish project repo and web app link on resume
Create a 2-minute project demo video
Apply to at least three entry-level AI/ML jobs

Weekly Plan

Week	Focus	Why	Tasks	Deliverables
1	Learn Python and Working with Data	You need Python skills to do AI and ML work.	Install Python and Jupyter Notebook, Write basic Python scripts (Jupyter), Try loops and functions, Load CSV data files (pandas), Plot simple charts (matplotlib)	Python scripts showing loops and lists, Jupyter Notebook loading and plotting data
2	Clean Data and Understand Statistics	AI models only work with good data.	Explore real data (Kaggle Datasets), Clean data (drop missing values with pandas), Calculate averages and medians, Create summary charts, Write a short data report	Notebook showing cleaned data steps, Charts and one-paragraph report
3	Discover Machine Learning Basics	Learn what an AI model is and how to build one.	Read intro to ML (Google ML Crash Course), Use scikit-learn to train a simple model, Split data into train and test sets, Make predictions and check accuracy, Describe what you learned	Notebook running a simple regression or classification, Short summary of model and results
4	Plan and Design Your Project	Having a goal helps you focus your effort.	Pick a dataset for your project, Describe the problem in plain words, Sketch project steps (paper or doc), Make a list of needed features (e.g., input, output)	Project plan (1 page, clear steps and goals), Chosen dataset link
5	Build a Data Pipeline and Baseline Model	Get a simple working version of your project.	Write code to load and clean your dataset, Split data into train/test sets, Train basic model (scikit-learn), Measure results (accuracy or error rate)	Code loading, cleaning, and modeling data, Notebook showing baseline results
6	Polish, Test, and Document Your Code	Good projects need clear code and instructions.	Refactor code into functions, Write simple code tests (pytest), Write instructions (README.md), Organize files (GitHub repo)	Repo with functions and tests passing, README with setup steps and usage
7	Deploy Your Project Online	Showcasing your work online impresses employers.	Create a simple web app (Streamlit), Connect model to web app, Test the web app works, Deploy app (Streamlit Share or HuggingFace Spaces)	Live project link, GitHub repo updated with deployment instructions
8	Showcase and Apply for Jobs	Share your work and take first job steps.	Make a 2-minute video demo or slides, Add project link to your resume and LinkedIn, Write and send job applications, Ask a friend for project feedback	Project demo video, Proof of at least three job applications