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AI Projects: Beginner to Advanced Levels
Welcome to our AI Projects repository! Whether you're an AI enthusiast just starting or an industry professional seeking to enhance your portfolio, you're in the right place. If you've made it to this page, you're taking things seriously, want to actually learn about artificial intelligence, and even learn about how it's playing out in the real world. By creating the projects, you not only build your existing knowledge, you also have tangible products on your resume that can help you get your dream AI job.
Achieve Your AI Goals with Our Projects
- Industry-Relevant Projects: Gain exposure to projects designed by experts to reflect real-world challenges.
- End-to-End Solutions: Get access to detailed instructions, datasets, and source codes to guide you through every step.
- Skill Enhancement: Practice with projects that improve your proficiency in machine learning, deep learning, natural language processing, and computer vision.
Let's dive into some exciting projects you can explore!
AI Projects for Beginners
Starting your journey in AI? Here are some beginner-friendly projects to help you build a strong foundation:
1) BigMart Sales Prediction ML Project in Python
This big mart sales prediction is the best example of how data science methods can be applied to real-life sales data in retail. You will be using a dataset from Kaggle containing some rigorous features like product type, item exposure, your store's location as well as customer information to create a perfect sales prediction model.
2) Leaf Disease Detection Using Deep Learning
Our "Leaf Disease Detection Using Deep Learning" project uses advanced CNN models to spot plant diseases directly from leaf images. With image enhancements and data augmentation, we've achieved high accuracy, making it a reliable solution for healthier crops and a great example of deep learning's potential in agriculture.
Leaf Disease Detection Project
3) Build Regression (Linear, Ridge, Lasso) Models in NumPy Python
Using Python and NumPy this project introduces Linear Regression, Ridge, and Lasso Regression. We will also understand how these models can forecast outcomes and determine the correlation between variables. Regardless of your experience with machine learning this project simplifies the concept making it very easy to understand.
Build Regression (Linear, Ridge, Lasso) Models Project
4) NLP Project for Beginners on Text Processing and Classification
In this project, you will delve into the machine's ability to easily read and understand text and classify it most appropriately. You will learn what natural language processing (NLP) is and the processes involved in preparing the raw text for further analysis. Libraries such as NLTK and Scikit-learn will be used to quantify the text into figures that will be used by the machine learning models.
5) Build a Customer Churn Prediction Model using Decision Trees
Predict customer churn with Decision Trees! Learn data cleaning, SMOTE, and model evaluation using Python. Compare Decision Tree and Logistic Regression models to find the best approach in this hands-on, beginner-friendly project.
Customer Churn Prediction Project
Intermediate AI Projects
For those with some experience, these projects provide an opportunity to tackle more complex challenges:
6) Predictive Analytics for Sales Forecasting
This project teaches Deep Neural Networks (DNNs) using a dataset of 86,000 businesses. Participants will learn key concepts and use Python libraries like pandas, numpy, and TensorFlow for data analysis, cleaning, model building, and tuning.
7) Time Series Forecasting with ARIMA and SARIMAX Models in Python
Build and assess time series forecasting models such as ARIMA, ARIMAX, and SARIMAX using real-world data from sectors like Healthcare and Banking for precise predictions.
Time Series Forecasting Project
8) Build A Book Recommender System With TF-IDF And Clustering(Python)
Create a book recommendation system with machine learning using TF-IDF, KMeans clustering, and cosine similarity for accurate, data-driven suggestions
Book Recommendation System Project
Advanced-Level Projects
For professionals ready to tackle more complex challenges:
Recommendation Systems
Build a collaborative filtering and content-based recommendation system to suggest products or movies. Learn advanced techniques like matrix factorization and hybrid models.
Voice Cloning Application Using RVC
In this project, you will experience the process of making a voice cloning tool using RVC technology. The platform for this tutorial would be Google Colab, so you don't need to worry about any troublesome installations and just follow the steps.
Voice Cloning Application Using RVC
Chatbots with Generative AI Models
This project aims at the development of the latest generative AI-powered chatbots. These chatbots are capable of having conversations with a user like a real human assistant using GPT-3.5-turbo and GPT-4. In this project, you will learn everything from the code setup to using OpenAI API for creating the chatbot.
Chatbots with Generative AI Models
How to Start Your AI Project Journey
- Choose a Project: Select a project according to your skill level and interests.
- Access Resources: Download datasets, source codes, and documentation bases for each project.
- Follow Step-by-Step Guides: Implement solutions through our in-depth directions.
- Experiment and Optimize: Modify models and improve performances.
- Showcase Your Work: Add completed projects to your portfolio.
Frequently Asked Questions (FAQs)
Q1. Can these projects be done by beginners? Our beginner-level projects are made available with step-by-step instructions to train the newbies.
Q2. Do you provide source codes? Absolutely! Downloadable source codes are present for all projects to simplify implementation.
Q3. Would you use these projects on your resume? Completing these projects will show you that you have these skills and also help you to improve your resume.
Q4. In which projects, what tools and libraries are used? Popular tools and libraries such as TensorFlow, PyTorch, OpenCV, NLTK, and scikit-learn are used for projects.
Q5. Are datasets included? Yes, there are datasets for every project and instructions on how to use them.
Q6. Can the code be modified according to my needs? Of course! But these projects are fully customizable, so you can experiment and innovate.