Beginner Roadmap: From High School Student to Machine Learning Explorer

Eight-week plan for high school students to start learning machine learning basics. Includes math review, Python skills, data work, a real ML project, and sharing work. Good for curious beginners.

Weekly Hours: 10
Estimated Weeks: 8

Phases

Foundations: Math and Python

Start with math and the basics of programming. You will review simple algebra, statistics, and learn core Python with hands-on exercises.

2 weeks

Review math basics needed for machine learning
Install Python and a code editor
Write and run simple Python scripts
Understand basic data types and logic

Solve linear equations (e.g., 2x + 1 = 9)
Calculate mean and median (find average height in class)
Write Python loops (print numbers 1 to 10)
Use lists and dictionaries (store groceries in a list)

Basic Algebra Guide
Intro Python Tutorials
Beginner Coding Exercises
Free Python Editor

Complete a short math quiz (10 questions, score at least 8)
Write and run 3 simple Python scripts (calculator, list sorter, guessing game)

Data Work: Handling and Exploring Data

Learn how to work with data in Python. You will clean, sort, and visualize data using simple tools.

2 weeks

Read and save data files (CSV format)
Clean messy data for use
Make basic charts and graphs
Summarize data patterns

Load data sets (open a CSV file in Python)
Remove wrong or missing values (drop empty rows)
Make line and bar charts (plot ages of students)
Calculate averages and totals (find total sales)

Data Handling Guides
Pandas Library Tutorial
Simple Charting Tools
Sample Data Sets

Upload, clean, and visualize 1 real data set (CSV of grades)
Create and share a Jupyter notebook showing steps and results

Machine Learning Basics

Get to know how machines can learn. Use easy ML tools to train your first model and test it.

2 weeks

Know what a machine learning model is
Use a Python ML library
Train a simple model on your data
Check your model's accuracy

Train a classifier (predict pass/fail from grades)
Split data for training and testing (use 80% and 20%)
Check accuracy (see if predictions match reality)
Explain results in plain words

ML for Beginners Guide
scikit-learn Intro
Beginner Projects
Python ML Tutorials

Build your first ML model (scikit-learn, pass/fail prediction)
Make a report with accuracy numbers and what they mean

Showcase Project: Student Score Predictor App

Make a simple app that predicts final exam scores from simple input. It includes a form, uses a trained model, saves results, and lets users try predictions.

2 weeks

Plan and build a small ML app
Let users enter data (via web form)
Connect app to your trained model
Test app with sample data

Make a web form (use Streamlit, input name and grades)
Link form to model (run prediction on button click)
Deploy app online (use Streamlit Cloud)
Write and run app tests (create test examples)

Streamlit Tutorials
ML Deployment Guide
App Testing Basics
Sample Student Data

Deploy working app online (Streamlit URL public)
Pass all sample test cases (predict within 10% accuracy)
Add a README file with demo steps and screenshots

Share and Reflect

Present your project. Write what you learned and get feedback. Plan your next step in ML.

1 weeks

Showcase your app to someone (teacher or friend)
Explain how it works
Share code online (GitHub or similar)
Reflect on learning and next goals

Present project (explain app to others in simple way)
Write a project summary (one page, what worked, what was hard)
Upload code and README (share GitHub link)
List next learning steps (pick 1-2 new ML topics)

Guide to Presenting Projects
Writing a Summary
Intro to GitHub
Learning Roadmaps

Get project reviewed by at least 1 person (written or verbal)
Post app link and summary on a student forum
Make a list with 2 future ML goals

Weekly Plan

Week	Focus	Why	Tasks	Deliverables
1	Review math basics and install coding tools	You need math and tools as your foundation	Solve basic equations (worksheet or online quiz), Practice statistics (mean, median on sample numbers), Install Python and a simple editor (Thonny or VSCode), Write first Python program (print your name), Try simple variables and print statements	Completed math worksheet (score at least 8/10), First Python script output file
2	Learn Python basics	Programming is key for machine learning	Write scripts using loops (count from 1 to 5), Use if-statements (guess a number, hot/cold game), Create and use lists (grocery list), Make a simple calculator (input two numbers, add them), Explore error messages and debug simple bugs	Two Python scripts (calculator, guessing game), Short notes on learning points
3	Handle and clean data files	You need clean data to train ML models	Download a CSV data file (sample grades dataset), Load CSV in Python (use pandas), Remove empty or wrong rows (clean invalid data), Save the cleaned file, Write code to show first five rows of data	Cleaned data file, Python notebook with cleaning steps
4	Visualize and summarize data	See patterns to understand your data	Plot line charts (grades over tests), Draw bar charts (number of students per grade), Calculate averages and medians (scores per test), Write summary paragraph (describe findings), Save and show charts in notebook	Notebook with at least two charts, One-paragraph data summary
5	Train your first ML model	Building models is the heart of ML	Install scikit-learn library, Split grades data into training and testing sets, Pick a simple model (Logistic Regression in scikit-learn), Train model on training data, Use model to predict results on testing data	Notebook with trained model, Report accuracy number
6	Test and explain your model	Good ML means checking and explaining results	Test model on new data (use test set), Record true and predicted values, Calculate accuracy score (percentage correct), Write a plain description of results, Save all steps in a notebook	Notebook showing test results, Paragraph explaining accuracy
7	Build and deploy your Student Score Predictor App	Show what you have made to others	Learn Streamlit basics (follow intro tutorial), Make a web form for inputs (name, homework scores), Load your trained model in app, Connect form to prediction (show result on screen), Deploy app to Streamlit Cloud (get public link)	Live app link, Link to code with README
8	Present and reflect on your project	Sharing helps you learn and find next steps	Show app to teacher or friend and get feedback, Write a one-page summary (what went well, what was hard), Upload project to GitHub (code, README, screenshots), Post link and summary on student forum, List two new ML things you want to learn	Feedback notes, GitHub project link

Daily Plan

Monday

Watch or read lesson (math, Python, or ML basics)
Try one hands-on example (code or math problem)

Tuesday

Do a coding or data task (solve a set exercise)
Check results and fix any errors

Wednesday

Practice with new tool (like pandas or Streamlit)
Apply it to your own data or code

Thursday

Work on project file or notebook
Write summary or notes explaining your work

Friday

Test or review your code or app
Share progress with someone for feedback