Here’s a list of the top open source AI projects for beginners, along with practical hands-on implementations and real-life applications that can be built using these tools.

Open source AI provides a fantastic entry point for beginners, offering accessible, free-to-use tools that help build foundational skills in artificial intelligence. Unlike proprietary AI systems, open source AI promotes learning through hands-on experience and community-driven development. Beginners can explore real-world AI applications, study existing implementations, and modify projects to suit their needs. This enhances technical knowledge while fostering collaboration and innovation.

Open source AI projects are particularly beneficial for beginners due to the availability of vast resources, including documentation, tutorials, and forums where users can seek guidance. By engaging with open source AI, they gain practical exposure, allowing them to develop confidence and competence in AI development.

Getting started with open source AI

Before diving into open source AI, beginners should have a basic understanding of Python and AI-related libraries like TensorFlow, OpenCV, and Hugging Face. These tools serve as the backbone for many AI applications, from image recognition to natural language processing.

To find open source AI projects, explore platforms like GitHub, Kaggle, and the Hugging Face Model Hub. These repositories host numerous AI projects, datasets, and pretrained models that beginners can experiment with.

Contributing to open source AI can be as simple as fixing documentation errors, optimising model performance, or creating tutorials. Engaging with open source projects not only enhances learning but also builds credibility within the AI community.

Top 5 open source AI projects for beginners

Teachable Machine (Google)

A no-code AI platform that simplifies image, audio, and pose recognition. This tool is ideal for beginners as it enables them to build AI models without writing extensive code.

Mini project: Create an image classifier for hand gestures using Teachable Machine Pseudo-code:

Step 1: Visit https://teachablemachine.withgoogle.com/

Step 2: Upload hand gesture images (e.g., thumbs up, thumbs down).

Step 3: Train the model using Teachable Machine’s interface.

Step 4: Export the model and integrate it into a web or mobile application.

Hugging Face Transformers

Provides pre-trained NLP models for tasks like sentiment analysis and text classification. Beginners can use these models without deep AI expertise.

Mini project: Analyse sentiment from Twitter data using a pre-trained model Pseudo-code:

from transformers import pipeline
# Load pre-trained sentiment analysis model
sentiment_pipeline = pipeline(“sentiment-analysis”)
# Analyze sentiment of a tweet
tweet = “I love open-source AI!”
result = sentiment_pipeline(tweet)
print(result)

OpenCV for computer vision

A popular library for image and video processing, widely used in AI-driven applications.
Mini project: Face detection using OpenCV and Python Pseudo-code:

import cv2
# Load the pre-trained Haar Cascade face detector
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + “haarcascade_frontalface_default.xml”)
# Read image
img = cv2.imread(“face.jpg”)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Detect faces
faces = face_cascade.detectMultiScale(gray, 1.1, 4)

# Draw rectangles around detected faces
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
cv2.imshow(“Face Detection”, img)
cv2.waitKey(0)
cv2.destroyAllWindows()

FastAI for deep learning

Simplifies neural network training with pre-built modules, making it easier for beginners to train AI models with minimal coding.

Mini project: Train an image classifier in a few lines of code Pseudo-code:

from fastai.vision.all import *
# Load dataset
path = untar_data(URLs.PETS)/’images’
dls = ImageDataLoaders.from_name_func(path, get_image_files(path), lambda x: x[0].isupper(), item_tfms=Resize(224))
# Train model
learn = cnn_learner(dls, resnet34, metrics=accuracy)
learn.fine_tune(1)

Ludwig by Uber (No-code AI)

Enables AI model training without requiring extensive programming knowledge; perfect for beginners looking to explore AI without deep technical expertise.

Mini project: Predict customer churn using structured data Pseudo-code:

from ludwig.api import LudwigModel

import pandas as pd

# Load dataset

data = pd.read_csv(“customer_churn.csv”)

# Define Ludwig configuration

yaml_config = {

“input_features”: [{“name”: “customer_type”, “type”: “category”}],

“output_features”: [{“name”: “churn”, “type”: “binary”}]

}

# Train model

model = LudwigModel(config=yaml_config)

train_stats = model.train(dataset=data)

Real-life AI applications using easy tools

AI-powered chatbot with Rasa

Rasa is an open source conversational AI framework that allows developers to build intelligent chatbots. It supports both rule-based and machine-learning-driven approaches for natural conversations.

Use cases

• Automates customer support by answering FAQs.
• Provides personalised recommendations based on user queries.
• Enhances business communication with real-time chat assistance.

Basic Rasa chatbot code:

# Install Rasa
pip install rasa
# Initialize Rasa project
rasa init
# Train the chatbot model
rasa train
# Run the chatbot
rasa shell

Speech-to-text converter with OpenAI Whisper

Whisper is an advanced speech recognition model by OpenAI that converts spoken language into text. It is useful for transcription services, voice assistants, and accessibility tools.

Use cases

• Converts interviews or meetings into text automatically.
• Provides real-time subtitles for videos.
• Helps in accessibility solutions for hearing-impaired individuals.

Code example

import whisper
# Load the Whisper model
model = whisper.load_model(“base”)
# Transcribe an audio file
result = model.transcribe(“audio.mp3”)
# Print the text output
print(result[“text”])

Resume` screening AI

Resume` screening AI uses natural language processing (NLP) to analyse job applications and match them to job descriptions. It ranks resumes` based on skills, experience, and relevance.

Use cases:

• HR departments can automate candidate shortlisting.
• Reduces bias in the hiring process by focusing on qualifications.
• Saves time by filtering out irrelevant applications.

Code for resume` screening using NLP

import spacy

# Load the English NLP model

nlp = spacy.load(“en_core_web_sm”)

# Example resume text

resume_text = “Experienced software engineer skilled in Python, AI, and machine learning.”

# Process the text

doc = nlp(resume_text)

# Extract key skills (example using named entities)

skills = [ent.text for ent in doc.ents]

print(“Extracted Skills:”, skills)

AI for medical diagnosis

AI is widely used in medical image analysis for detecting diseases such as cancer, pneumonia, and COVID-19. Deep learning models (CNNs, ResNet, U-Net, etc) are trained on medical imaging datasets.

Use cases

• Helps radiologists detect abnormalities in X-rays and MRIs.
• Enhances early disease detection and diagnosis accuracy.
• Reduces workload in hospitals by automating analysis.

Example: Using TensorFlow for medical image classification

import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
# Load a pre-trained model
model = load_model(“medical_diagnosis_model.h5”)
# Load and preprocess the image
img = image.load_img(“chest_xray.jpg”, target_size=(224, 224))
img_array = image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
# Make a prediction
prediction = model.predict(img_array)
print(“Diagnosis:”, “Positive” if prediction[0] > 0.5 else “Negative”)

AI-based language translation

MarianMT and OpenNMT are open source machine translation models that allow real-time language translation for various applications.

Use cases

• Businesses use it for multilingual customer support.
• Travellers use AI-powered translators for communication.
• Websites provide automatic content localisation.

Example: Translate text using MarianMT (Hugging Face)

from transformers import MarianMTModel, MarianTokenizer
# Define source and target language (English to French)
model_name = “Helsinki-NLP/opus-mt-en-fr”
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
# Translate text
text = “Hello, how are you?”
encoded_text = tokenizer(text, return_tensors=”pt”, padding=True, truncation=True)
translated = model.generate(**encoded_text)
output = tokenizer.decode(translated[0], skip_special_tokens=True)
print(“Translated Text:”, output)

Application	Tool used	Key benefit
Chatbot	Rasa	Automates customer support and FAQs
Speech-to-text	OpenWhisper	Converts audio to text for transcription and accessibility
Resume` screening	NLP	Automates candidate shortlisting for HR
Medical diagnosis	TensorFlow (CNN)	AI-assisted disease detection from images
Language translation	MarianMT	Provides real-time multilingual translations

 

Open source AI provides an excellent starting point for beginners to learn, experiment, and contribute to the growing AI community. By leveraging platforms like GitHub and Kaggle, learners can access real-world projects, collaborate with experts, and enhance their AI proficiency. The next step is to explore these projects, experiment with AI tools, and actively participate in the open source AI movement.