June 3, 2025

Best Keras Project Ideas for Beginners

Keras is a beginner-friendly deep learning library that makes it easy to build and train neural networks. Starting with simple Keras projects helps beginners understand core machine learning concepts, improve coding skills, and gain confidence in building real-world AI applications

10 Beginner-Friendly Keras Project Ideas – Overview

Here’s an overview of the 10 best Kears Project Ideas for beginners:

S.No.	Project Title	Complexity	Estimated Time	Source Code
1	MNIST Handwritten Digit Classifier	Medium	4 hours	Get Started
2	Cats vs. Dogs Image Classifier	Medium	4 hours	Get Started
3	Fashion MNIST Classifier	Medium	4 hours	Get Started
4	IMDb Sentiment Analysis	Medium	5 hours	Get Started
5	House Price Prediction	Medium	4 hours	Get Started
6	Real-Time Object Detection (YOLO)	Hard	6 hours	Get Started
7	English to French Translator	Medium	5 hours	Get Started
8	Fake News Detection	Medium	4 hours	Get Started
9	Real-Time Image Caption Generator	Hard	6 hours	Get Started
10	Real-Time Emotion Detection from Text	Medium	6 hours	Get Started

Top 10 Keras Project Ideas for Beginners

Here are the top 10 Keras project ideas for beginners

1. MNIST Handwritten Digit Classifier

This is one of the keras based projects that involves building a digit classification model using the MNIST dataset and deploying it using a serverless architecture.

You will learn how to integrate machine learning with serverless computing for scalable and cost-effective model inference.

Duration: 4 hours

Project Complexity: Medium

Key Concepts Covered:

Serverless deployment
Image classification
Model inference

Implementation Steps:

Load and preprocess the MNIST dataset.
Train a CNN model for digit classification.
Export the trained model.
Deploy the model using a serverless platform (e.g., AWS Lambda, Google Cloud Functions).
Test with sample digit images via a REST API.

Required Pre-requisites:

Basic knowledge of Python
Understanding of neural networks
Familiarity with cloud platforms

Resources Required:

MNIST dataset
TensorFlow/Keras or PyTorch
AWS/GCP/Azure account
Serverless framework or CLI

Real-World Application:

Digit recognition in banking and form automation
Scalable handwriting recognition services

Get Started

2. Cats vs. Dogs Image Classifier

This is one of the simple Keras project ideas that builds a binary image classification model to distinguish between cat and dog images and deploys it using a serverless framework.

You will learn how to scale image classification tasks using serverless computing without managing servers or VMs.

Duration: 4 hours

Project Complexity: Medium

Key Concepts Covered:

Binary image classification
Model deployment
Serverless architecture

Implementation Steps:

Preprocess the Cats vs. Dogs dataset.
Train a CNN to classify images.
Export the trained model.
Deploy it using a serverless function with an API endpoint.
Accept and classify uploaded images via the API.

Required Pre-requisites:

CNN fundamentals
Python and TensorFlow/PyTorch
Basic serverless framework usage

Resources Required:

Cats vs. Dogs dataset
TensorFlow or PyTorch
Serverless framework or cloud CLI
Cloud function platform (AWS/GCP/Azure)

Real-World Application:

Pet recognition systems for shelters or apps
Automated tagging for pet photo platforms

Get Started

3. Fashion MNIST Classifier

This is one of the Keras mini projects that involves training a neural network to classify grayscale images of clothing from the Fashion MNIST dataset.

You’ll learn how to deploy scalable prediction models using serverless functions to handle real-time classification without maintaining infrastructure.

Duration: 4 hours

Project Complexity: Medium

Key Concepts Covered:

Multiclass image classification
Cloud model deployment
Event-driven serverless execution

Implementation Steps:

Load and preprocess the Fashion MNIST dataset.
Build and train a neural network.
Save the trained model for inference.
Deploy the model as a serverless function.
Trigger predictions via HTTP or event inputs.

Required Pre-requisites:

Deep learning basics
Familiarity with Python and Keras/TensorFlow
Intro to serverless deployment

Resources Required:

Fashion MNIST dataset
TensorFlow/Keras
Serverless CLI or cloud service
Cloud provider account (AWS/GCP/Azure)

Real-World Application:

Automated tagging for fashion e-commerce platforms
Smart wardrobe and retail inventory apps

Get Started

4. IMDb Sentiment Analysis

This project uses NLP techniques to classify movie reviews from the IMDb dataset into positive or negative sentiments.

You’ll learn how to deploy sentiment analysis models with serverless computing to perform real-time predictions on user-generated text without managing backend servers.

Duration: 5 hours

Project Complexity: Medium

Key Concepts Covered:

Text preprocessing and tokenization
Binary sentiment classification
Serverless API deployment

Implementation Steps:

Load and clean the IMDb review dataset.
Tokenize and vectorize text data.
Train an LSTM or similar model.
Deploy the model using a serverless function.
Handle user input and return sentiment predictions via HTTP.

Required Pre-requisites:

NLP basics
Python with TensorFlow/Keras
Intro to serverless APIs

Resources Required:

IMDb review dataset
TensorFlow/Keras
Cloud provider (AWS/GCP/Azure)
Serverless deployment tools

Real-World Application:

Real-time sentiment feedback in review platforms
Brand monitoring tools for social media

Get Started

5. House Price Prediction

This project involves building a regression model to predict house prices based on various features like area, location, and number of rooms.

You’ll learn how to deploy predictive models using serverless computing to provide scalable and cost-efficient real estate insights.

Duration: 4 hours

Project Complexity: Medium

Key Concepts Covered:

Regression modeling
Feature engineering
Serverless deployment pipelines

Implementation Steps:

Load and preprocess housing data.
Train a regression model using scikit-learn.
Save the trained model for inference.
Deploy the model via a serverless function.
Create an endpoint to receive input and return price predictions.

Required Pre-requisites:

Basics of regression
Python and scikit-learn
Intro to cloud deployment

Resources Required:

Housing dataset (e.g., Kaggle)
scikit-learn
Serverless framework or CLI
Cloud function service (AWS Lambda/GCP Functions)

Real-World Application:

Real estate pricing tools
Online mortgage or property value estimators

Get Started

6. Real-Time Object Detection (YOLO)

This project uses the YOLO (You Only Look Once) algorithm to detect objects in real-time from image or video input.

You’ll learn how to integrate high-performance computer vision models with serverless computing to process and return results on demand.

Duration: 6 hours

Project Complexity: Hard

Key Concepts Covered:

Object detection with YOLO
Model optimization
Serverless computer vision

Implementation Steps:

Set up and test the YOLO model locally with sample inputs.
Convert or export the model to an optimized format (e.g., ONNX or TensorRT).
Deploy it in a serverless function with appropriate memory and timeout configs.
Create an API to accept image input and return detections.
Optimize for latency and cold start time.

Required Pre-requisites:

Computer vision fundamentals
Experience with YOLO and OpenCV
Knowledge of serverless deployments

Resources Required:

Pretrained YOLO model
OpenCV or image processing library
Cloud provider with GPU/serverless support
API Gateway and function hosting

Real-World Application:

Smart surveillance and monitoring systems
Real-time image processing in mobile or IoT apps

Get Started

7. English to French Translator

This project involves building a sequence-to-sequence (Seq2Seq) model that translates English sentences into French.

You’ll learn how to deploy NLP-based translation services using serverless computing to enable low-latency, scalable language translation.

Duration: 5 hours

Project Complexity: Medium

Key Concepts Covered:

Sequence-to-sequence modeling
NLP translation techniques
Serverless text processing

Implementation Steps:

Preprocess parallel English-French sentence data.
Train or use a pretrained translation model (e.g., LSTM/Transformer).
Package and optimize the model for inference.
Deploy it as a serverless API function.
Accept user input and return translated output in real time.

Required Pre-requisites:

Basics of NLP and machine translation
Python with TensorFlow/Keras or HuggingFace
Serverless deployment understanding

Resources Required:

English-French parallel corpus
Pretrained translation model
Cloud provider with function support
Minimal frontend (optional)

Real-World Application:

Live translation in chat or email systems
Multilingual support tools for customer service

Get Started

8. Fake News Detection

This project focuses on building a binary text classifier that identifies whether a news article is real or fake.

You’ll learn how to deploy serverless NLP models that can handle real-time classification tasks with scalable efficiency.

Duration: 4 hours

Project Complexity: Medium

Key Concepts Covered:

Text classification
NLP feature extraction
Serverless inference deployment

Implementation Steps:

Preprocess the news dataset with tokenization and vectorization.
Train a binary classifier (e.g., Logistic Regression or LSTM).
Save and export the trained model.
Deploy the model to a serverless function endpoint.
Accept the article text and return the prediction (real/fake).

Required Pre-requisites:

NLP basics
Model training in scikit-learn or deep learning frameworks
Serverless deployment skills

Resources Required:

Fake/real news dataset (e.g., Kaggle)
Python, scikit-learn or TensorFlow
Cloud serverless compute (AWS Lambda, GCP Functions)

Real-World Application:

Automated content moderation systems
Browser extensions for credibility checks

Get Started

9. Real-Time Image Caption Generator

This project involves creating a deep learning model that generates descriptive captions for input images.

You’ll learn how to deploy multi-modal AI models in a serverless environment for low-latency image-to-text tasks.

Duration: 6 hours

Project Complexity: Hard

Key Concepts Covered:

CNN + RNN integration
Image feature extraction
Serverless model orchestration

Implementation Steps:

Extract image features using a pretrained CNN (e.g., InceptionV3).
Train a captioning model using RNN or Transformer decoder.
Export the complete model pipeline for inference.
Deploy the model on a serverless backend.
Accept image uploads and return auto-generated captions.

Required Pre-requisites:

Knowledge of CNNs and RNNs
Experience with TensorFlow/Keras
Serverless deployment and API basics

Resources Required:

MS COCO dataset (or similar)
Pretrained vision models
Python, TensorFlow
Cloud function platform

Real-World Application:

Visual assistance for the visually impaired
Automated content tagging in social platforms

Get Started

10. Real-Time Emotion Detection from Text

This project focuses on building a model to detect emotions like joy, anger, sadness, and fear from user-input text in real time.

Through this project, you will learn how to deploy emotion classification models on serverless platforms for scalable inference without managing backend infrastructure.

Duration: 6 hours

Project Complexity: Medium

Key Concepts Covered:

Text preprocessing & tokenization
LSTM-based emotion classification
Serverless deployment for real-time usage

Implementation Steps:

Collect or use an existing labeled emotion dataset
Preprocess the text (tokenization, padding)
Train an LSTM or GRU model for emotion classification
Deploy the model using a serverless function (e.g., AWS Lambda)
Create a basic frontend or API endpoint to test real-time predictions

Required Pre-requisites:

Basics of NLP with Keras
Understanding of LSTM/GRU networks
Familiarity with serverless platforms

Resources Required:

Emotion-labeled dataset (e.g., Emotion Dataset by Dair AI)
Keras or TensorFlow
Serverless framework (AWS Lambda, Google Cloud Functions)

Real-World Application:

Used in chatbots to detect and respond to emotional tone
Useful for mental health platforms to assess user sentiment in real-time

Get Started

Final Words

Keras projects are a great starting point for anyone new to deep learning. By working on beginner-friendly projects, learners can build a solid foundation in neural networks and gain hands-on experience.

Take the first step toward more advanced AI and machine learning applications!

Frequently Asked Questions

1. What are some easy Keras project ideas for beginners?

Some easy Keras project ideas for beginners include MNIST digit classification, binary image classification (cats vs dogs), and sentiment analysis using IMDb dataset.

2. Why are Keras project ideas important for beginners?

Keras project ideas are important for beginners because they offer hands-on experience with deep learning concepts using a user-friendly API, helping solidify foundational skills.

3. What skills can beginners learn from Keras project ideas?

Beginners can learn skills such as data preprocessing, neural network design, model training, evaluation, and deploying models for real-world applications.

4. Which Keras project is recommended for someone with no prior programming experience?

The MNIST handwritten digit classifier is highly recommended for complete beginners due to its simplicity, small dataset, and minimal coding complexity.

5. How long does it typically take to complete a beginner-level Keras project?

A beginner-level Keras project typically takes between 4 to 8 hours, depending on the complexity and the learner’s familiarity with Python and machine learning concepts.

Explore More Project Ideas

Thirumoorthy

Thirumoorthy serves as a teacher and coach. He obtained a 99 percentile on the CAT. He cleared numerous IT jobs and public sector job interviews, but he still decided to pursue a career in education. He desires to elevate the underprivileged sections of society through education