12 Free AI Projects with Source Code You Can Run Today

Codersarts AI
2 days ago
13 min read

Last updated: April 2026 · Reading time: 16 minutes · By Codersarts

"Free" is a word that does a lot of heavy lifting on the internet, and not always honestly. You search for "free AI projects with source code" and you get three kinds of results: tutorials that stop just short of showing the actual code, GitHub repositories where the last commit was in 2019 and nothing installs anymore, and "free" downloads that turn out to want your credit card on step three.

This post is an attempt to do it the other way around.

Below are twelve AI projects we've actually tested on a clean machine in the last thirty days. Every one runs. Every one has full source code we're willing to give you at no charge. You don't need to pay us, and you don't need a GPU for most of them. A few you can download right now as a single ZIP pack; the rest we'll point you to — public repositories, datasets, and our own walkthroughs.

No email required for the walkthroughs. Email required for the 5-project ZIP pack at the bottom, because we do want to follow up once to see how it went. That's the whole "catch."

Let's get into the projects.

Skip to the pack? We've packaged five of these projects (#1, #2, #3, #5, #7) into a ready-to-run ZIP with clean READMEs and setup scripts. Email us the free 5-project pack (contact@codersarts.com)

12 Free AI Projects with Source Code You Can Run Today

Why "free" is enough (and when it isn't)

A reasonable question before we start: if these projects are genuinely free, why does Codersarts also sell AI projects?

Honest answer: free code and a paid project are two different things. Free code gets you running — you learn, you experiment, you put something on GitHub, you feel competent. A paid project from us is the full deliverable: working code plus a 60–80 page project report, plus the presentation, plus the dataset prep, plus an hour with a mentor who can explain every line to you. Final-year students submitting for marks need that second thing. Self-learners don't.

So: if you're here to learn, the 12 projects below are genuinely all you need. If you're here to submit something for grades in the next three weeks, the free code is a start but not the finish — the report, the viva prep, and the mentor walk-through are what separate a passing grade from a strong one. Pricing for that is at the bottom of this post for anyone who needs it.

Back to the projects.

Before you start — 10 minutes of setup

Every project in this post assumes you have:

Python 3.10 or 3.11 — grab it from python.org if you don't already (3.12+ will cause compatibility pain on a few of these)
pip — comes with Python
A virtual environment tool — either venv (built-in) or conda (if you have it)
A code editor — VS Code is free and works
An internet connection for dataset downloads

Optional but useful:

Git — to clone repos
Google Colab account — free GPU if you want it, no install needed
Jupyter Notebook — pip install jupyter — handy for the data exploration projects

That's it. No API keys, no paid services, no cloud accounts. Every project below runs on a regular laptop.

The 12 projects

1. Iris Flower Classification

We lead with this because everyone should build it first, and because it works on literally any machine — no dataset to download, no dependencies beyond scikit-learn, no training time worth mentioning.

What it does: Classifies 150 flower samples into three species based on petal and sepal measurements, with ~97% accuracy
What you'll learn: The universal scikit-learn pattern — fit, predict, score — that underlies every classical ML project you'll ever build
Stack: Python, scikit-learn, pandas
Time to running: 15 minutes
Dataset: Built into scikit-learn
Free source: Included in the 5-project pack below

2. Spam Message Classifier

A tiny NLP project that feels genuinely useful. You train on 5,500 labelled SMS messages; afterward, you paste in any text and the model tells you how spammy it is. Satisfying the first time you run it.

What it does: Uses Naive Bayes over TF-IDF vectors to classify text as spam or not-spam; includes a small Flask API you can hit from the command line
What you'll learn: How text becomes numbers (vectorisation), why Naive Bayes works disproportionately well for spam, what precision vs recall actually means in a real use case
Stack: Python, scikit-learn, NLTK, Flask
Time to running: 1 hour
Dataset: SMS Spam Collection (public, on UCI Machine Learning Repository)
Free source: Included in the 5-project pack below

3. Handwritten Digit Recognizer (MNIST)

The classic first-neural-network project. 70,000 small greyscale images of digits 0–9; you train a small network to classify them and hit ~99% accuracy in a minute or two.

What it does: Reads a 28×28 pixel image of a handwritten digit and predicts which number it is
What you'll learn: What a neural network actually is in code (not the YouTube-video version), what training epochs look like, why CNNs beat plain feedforward networks on images
Stack: TensorFlow/Keras
Time to running: 30–45 minutes
Dataset: MNIST (built into Keras, no separate download)
Free source: Included in the 5-project pack below

4. Titanic Survival Prediction

The second rite-of-passage project in the ML world. Predict which Titanic passengers survived based on features like class, age, sex, family size, and fare. The data is famously messy, which is the lesson — real data always is.

What it does: Builds and compares several classifiers (logistic regression, decision tree, Random Forest, XGBoost) on the Titanic dataset, with a complete EDA notebook
What you'll learn: Exploratory data analysis, handling missing values, feature engineering — the unsexy skills that matter most in practical ML
Stack: pandas, seaborn, scikit-learn, XGBoost
Time to running: 2–3 hours
Dataset: Titanic (free on Kaggle, requires a free account)
Where to get the code: Our GitHub walkthrough — [link coming soon]. Or ask us and we'll send it over.

5. Movie Sentiment Analysis

Teach a model to read movie reviews and predict whether they're positive or negative. Train on 50,000 IMDB reviews; afterwards, paste in any review you like and it works on that too. Deeply satisfying.

What it does: Classifies movie reviews as positive or negative, with both a classical baseline (TF-IDF + logistic regression) and a neural version (LSTM), plus a Gradio web interface
What you'll learn: Baseline models beat fancy models more often than you'd think — a useful lesson in engineering humility
Stack: scikit-learn, TensorFlow/Keras, Gradio
Time to running: 2–3 hours
Dataset: IMDB Reviews (public, built into Keras)
Free source: Included in the 5-project pack below

6. Face Detection with OpenCV

Your first computer vision project that works on live video. Point your webcam at your face and the system draws a box around it in real time. No deep learning required — OpenCV's built-in Haar cascades work out of the box.

What it does: Detects faces in a webcam feed at ~30 frames per second, drawing a rectangle around each detected face
What you'll learn: OpenCV fundamentals, how classical (pre-deep-learning) CV methods work, what a video pipeline actually looks like in code
Stack: Python, OpenCV
Time to running: 20 minutes (yes, really)
Dataset: None — uses pre-trained Haar cascades included with OpenCV
Where to get the code: Our walkthrough includes a 40-line working script you can copy directly — [link coming soon]

7. Simple Rule-Based Chatbot

Before LLMs existed, chatbots worked on pattern matching and intent classification. Understanding this style is still useful — it's cheaper, it's deterministic, it doesn't hallucinate, and for a lot of real-world use cases it's still the right call.

What it does: A basic chatbot that handles greetings, FAQs, and escalation, wrapped in a Streamlit chat interface
What you'll learn: Intent recognition, response templating, the genuine tradeoffs between retrieval-based and generative chatbots
Stack: Python, NLTK, Streamlit
Time to running: 1–2 hours
Dataset: A small FAQ corpus you build yourself (part of the exercise)
Free source: Included in the 5-project pack below

8. House Price Prediction

Your first regression project. Predict a continuous number (house price) rather than a category. The California Housing dataset has ~20,000 rows and enough messiness to teach you real feature engineering.

What it does: Predicts house prices from features like location, age, rooms, and population density; compares linear regression against an ensemble model
What you'll learn: Why accuracy doesn't apply to regression, when to use MAE vs RMSE, how feature engineering beats model tuning most of the time
Stack: scikit-learn, XGBoost, pandas, matplotlib
Time to running: 2–3 hours
Dataset: California Housing (built into scikit-learn)
Where to get the code: Our GitHub walkthrough — [link coming soon]

9. Image Classification with a Pre-Trained CNN

Skip training from scratch. Take a MobileNetV2 model already trained on ImageNet, load it in three lines, and classify any image into 1,000 categories with ~90% top-5 accuracy. No GPU, no training, 10 minutes from clone to working.

What it does: Classifies any image into ImageNet's 1,000 categories using a pre-trained model — dogs, cats, musical instruments, car models, and so on
What you'll learn: Transfer learning in its most basic form; why you should almost never train from scratch in 2026
Stack: TensorFlow/Keras
Time to running: 15 minutes
Dataset: None for inference; model weights auto-download from Keras
Where to get the code: A 30-line Python script included in our free walkthrough — [link coming soon]

10. Real-Time Object Detection with YOLOv8

One of the most impressive demos you can build in an afternoon. The pre-trained YOLOv8 model detects 80 common objects (people, cars, cups, laptops, animals) in real-time webcam video. Run it once and you'll understand why CV has exploded commercially.

What it does: Real-time object detection on webcam feed with bounding boxes and class labels
What you'll learn: The leap from classification to detection (where is the thing, not just what is it), mean Average Precision, confidence thresholds
Stack: Ultralytics (the YOLOv8 Python package), OpenCV
Time to running: 30 minutes
Dataset: None needed — model comes pre-trained on COCO
Where to get the code: Ultralytics' own quick-start is genuinely five lines of code. We have a longer walkthrough for customising it — [link coming soon]

11. Recommendation System (Content-Based)

A simplified version of what Netflix and Amazon do. Given your rating history, recommend movies you haven't seen. The content-based version is easier than collaborative filtering and a good starting point.

What it does: Recommends movies to you based on similarity to movies you've rated highly, using TF-IDF on movie descriptions and cosine similarity
What you'll learn: Cosine similarity, item-based recommendation logic, why "cold start" is a hard problem
Stack: scikit-learn, pandas
Time to running: 2 hours
Dataset: MovieLens 100K (free on GroupLens Research, small download)
Where to get the code: Our GitHub walkthrough — [link coming soon]

12. Text Summarizer (Extractive)

Paste in a long article. Get back a short summary. This is the simpler "extractive" version (picks important sentences from the original); the harder "abstractive" version (generates new sentences) is in our intermediate post.

What it does: Reads a long document and outputs the 3–5 most important sentences, using sentence-similarity graphs and the TextRank algorithm
What you'll learn: Graph-based NLP algorithms, why extractive summaries are safe but boring, sentence embedding basics
Stack: Python, spaCy, networkx
Time to running: 1–2 hours
Dataset: None required for inference; test on any articles you paste in
Where to get the code: Our GitHub walkthrough — [link coming soon]

Quick comparison of all 12

#	Project	Category	Setup Time	Need GPU?	In the Free Pack?
1	Iris Classification	Classical ML	15 min	No	✅ Yes
2	Spam Classifier	NLP	1 hr	No	✅ Yes
3	MNIST Digit Recognition	Deep Learning	30–45 min	Optional	✅ Yes
4	Titanic Survival	Classical ML	2–3 hrs	No	Walkthrough
5	Sentiment Analysis	NLP	2–3 hrs	Optional	✅ Yes
6	Face Detection	CV	20 min	No	Walkthrough
7	Rule-Based Chatbot	NLP	1–2 hrs	No	✅ Yes
8	House Price Prediction	Classical ML	2–3 hrs	No	Walkthrough
9	Image Classification	CV	15 min	No	Walkthrough
10	YOLO Object Detection	CV	30 min	Optional	Walkthrough
11	Content-Based Recommender	Recommender	2 hrs	No	Walkthrough
12	Text Summarizer	NLP	1–2 hrs	No	Walkthrough

Total: about 20 hours across all 12, if you go straight through. More realistically you'll do three or four.

How to actually get value out of free code (the thing most students skip)

Downloading code is easy. Learning from it is a different skill. Here's what we've seen work, from watching hundreds of students run through the same projects:

Don't start by reading the code. Start by running it. Your first goal is just to prove it works on your machine. Environment setup is the hardest part of most ML projects, and it's also the least interesting, so get it out of the way first.

Once it runs, break it on purpose. Change a hyperparameter. Remove 80% of the training data. Flip a label. Watch what happens. The time between "code running" and "code running differently than I expected" is where learning happens.

Explain each section out loud. If you can't say what a block of code does in your own words, you don't actually understand it yet. Pretend you're teaching a classmate who's a week behind you. Where you stumble is what to study next.

Write about it. 500 words is enough. Blog, LinkedIn post, GitHub README — wherever. Writing about a project is the single highest-ROI thing you can do for your career after building it.

Extend it. Take the MNIST digit recognizer and make it work on letters too. Take the spam classifier and retrain it on emails instead of SMS. Take the chatbot and hook it up to your college's FAQ page. The extension is what turns "I ran a tutorial" into "I built something."

Students who skip these four steps and just hop from project to project learn half as fast as they think.

What's inside the free 5-project pack

Get Pack

The ZIP we send contains projects 1, 2, 3, 5, and 7 from the list above (Iris, Spam Classifier, MNIST, Sentiment Analysis, Rule-Based Chatbot).

Each project includes:

Source code — tested on Python 3.11, commented where it matters
README.md — a real one, with setup steps, expected output, and the five most common errors we see students hit
requirements.txt — pinned versions that actually work together
Sample input — so you can verify it works before running on your own data
Dataset link or included file — for MNIST and Sentiment it's auto-downloaded; for Spam the CSV is bundled; Iris needs nothing

We also include a tiny "your next steps" note per project with three extension ideas each — in case you want to push beyond just running the code.

Get the pack

Drop your email below and we'll send it. One email after a few days asking how it went. No spam, no drip campaigns, no upsells — we'll mention the paid bundle once, and that's it.

Email us the free 5-project pack →

(No credit card, no payment details. Just an email address.)

Frequently asked questions

Is this really free? Yes. The 5-project pack is genuinely free and we're not hiding a paywall anywhere. We do sell paid project packages with reports and PPTs and mentor support, but that's clearly separated. If all you need is running code to learn, the free pack is complete on its own.

Why are only 5 projects in the pack and not all 12? Bundling all 12 into one ZIP makes the download heavy and overwhelming — most people never open a 500MB ZIP with 12 subfolders. We picked the five best-suited for a first pass: fast to run, educational, and covering classical ML, NLP, and deep learning. The other seven are linked as separate walkthroughs because they benefit from more explanation than a README can carry.

Do I need a GPU? For the 5 projects in the pack, no. MNIST trains in under two minutes on a regular laptop CPU. Everything else in the pack is CPU-friendly by default.

What Python version? Python 3.10 or 3.11. The requirements.txt in the pack is pinned to versions that work on both. 3.12+ will work for most of the pack but occasionally trips up on NLTK — we recommend 3.11 to play it safe.

What if it doesn't run on my machine? Reply to the email you'll get when you download the pack. We answer — usually within a day. Environment issues (Windows path problems, permission errors, package conflicts) are the most common questions and we've seen most of them.

Can I use this code in my college project or resume portfolio? Yes. Personal and academic use is fine. If you submit it for marks, please remember the warning from earlier in the post: code you can't explain is a viva disaster waiting to happen. Understand the code you submit.

How is this different from GitHub? GitHub has a lot of abandoned ML projects from 2019 that no longer run because library versions have drifted. Ours is tested on clean machines, pinned to working versions, and has READMEs written for humans. The quality-control is the difference.

Is there a catch? The catch is we follow up once to see how it went, and at some point we'll mention our paid bundles in case you ever need them. That's it. No subscription, no credit card, no auto-enrollment.

What about a video walkthrough? We're working on it. For now, the READMEs are detailed enough that most students don't need video, but we'll add video walkthroughs to the free pack over the next few months.

Can I get more free projects? The 7 we didn't include in the pack are all linked as walkthroughs. Beyond that — not free, unfortunately. Keeping paid bundles paid is what lets us keep the free pack actually free and actually tested.

What if I want the report and PPT too? That's the paid side. Final-year students submitting for marks usually want code + 60–80 page report + PPT + synopsis + viva prep + mentor call, all integrated. We price that at ₹6,999 for a complete final-year bundle. It's in a different league from the free pack — different scope, different goal.

If free isn't enough (and sometimes it isn't)

Here's when the free pack is the right fit, and when it isn't.

Free pack is right for you if:

You're self-studying, building skills outside of coursework
You're padding a GitHub portfolio
You're in early years (1st, 2nd, 3rd) and not yet at capstone-submission stage
You want to try AI without committing money to a project package
You're preparing for internship interviews and need talking points

You probably need the paid version if:

You're submitting a project for final-year grades in the next 2–6 weeks
Your examiner requires a detailed project report, PPT, and synopsis
You need a plagiarism-checked report (the free code doesn't come with one)
You need someone to explain the code to you in detail (for viva prep)
You need a project genuinely customised to your assigned topic, not a generic classic

If you're in the second group, take a look at our Final-Year AI Projects guide — it covers the 15 projects we most commonly package as complete final-year bundles, with what each comes with and what examiners ask about each.

Ready to grab the pack?

If you scrolled this far, you're probably going to actually use it, which is the whole point.

Email us the free 5-project pack → contact@codersarts.com

Type your email, hit submit, pack lands in your inbox within a minute or two. No credit card, no hidden steps.

If you hit a problem running anything — environment issues, missing packages, training errors — reply to the email. We answer.

Codersarts has been delivering AI projects to students since 2017, across 200+ universities in India, the US, the UK, and Australia. Everything we ship — free or paid — is tested on a clean machine before it goes out. Free code isn't second-best; it's just differently scoped.

Related reads:

15 AI Projects with Source Code for Final Year Students (2026)
Top 10 Python AI Projects with Source Code — Beginner to Advanced
20 AI Projects for Students with Source Code (2026)
7 Generative AI Projects with Source Code (LangChain, RAG, LLMs)

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