5 AI & ML Projects for Resume

If you are aiming for a career in Artificial Intelligence and Machine Learning, it’s important to work on some hands-on projects that can help you show your skills in solving real-world problems. So, if you are looking for some AI & ML project ideas to get started, this article is for you. In this article, I’ll take you through a list of 5 AI & ML projects you should try to boost your resume.

5 AI & ML Projects for Resume

Below are 5 AI & ML projects you should try to boost your resume. All the projects mentioned below are solved and explained in detail using Python.

Building a Smart Loan Recovery System

Financial institutions often face high rates of loan defaults which impact their profitability. The challenge is to predict and manage loan defaults effectively to minimize financial loss. This project would involve creating a Machine Learning model to predict the likelihood of loan defaults and devise strategies for recovery.

Examples of companies that work on problems like building a smart loan recovery system:

1. Fiserv: Provides financial services technology, including tools for risk management and loan servicing.
2. Kabbage: Now part of American Express, focuses on automated underwriting and loan management.

Find a solved and explained example of Building a Smart Loan Recovery System using Python here.

Building a Next Word Prediction Model

It helps in enhancing user experience in typing or text input for applications like smartphones, messaging apps, or assistive technologies by predicting the next word or phrase. It involves training a model on vast text corpora to predict the next word based on the context of the preceding words.

Examples of companies that work on problems like building a next word prediction model:

1. Google: Their Gboard keyboard uses predictive text based on Machine Learning.
2. Microsoft: Integrates similar technology in Windows and Office for text prediction.

Find a solved and explained example of Building a Next Word Prediction Model using Python here.

Building a Generative AI Model to Generate Synthetic Data

Privacy regulations like GDPR have made it harder for companies to use real user data for testing or training new models. Companies need high-quality synthetic data that mimics real-world data distributions without privacy concerns. This project would focus on creating models that can generate data which statistically matches the real data while ensuring no personal information is replicated.

Examples of companies that work on problems like building a Generative AI model to generate synthetic data:

1. NVIDIA: Works on AI for generating synthetic data for various applications, including autonomous driving simulation.
2. Hazy: Specializes in AI to generate synthetic data for enterprises.

Find a solved and explained example of Building a Generative AI Model to Generate Synthetic Data using Python here.

Building a Demand Forecasting Model to Plan and Optimize Inventory

Retail, manufacturing, and supply chain sectors struggle with overstock or stockouts due to unpredictable demand, leading to increased costs or loss of sales. A Machine Learning model could analyze historical sales data, seasonal trends, promotional calendars, and even external factors like economic conditions or weather forecasts to predict future demand accurately for optimizing inventory levels.

Examples of companies that work on problems like building a demand forecasting model to plan and optimize inventory:

1. Amazon: Uses predictive analytics for inventory management across its vast product lines.
2. Walmart: Employs AI to forecast demand, manage supply chains, and optimize stock levels.

Find a solved and explained example of Building a Demand Forecasting Model to Plan and Optimize Inventory using Python here.

Analyzing Large Text Documents using LLMs

The sheer volume of text data in legal documents, medical records, corporate reports, etc., makes manual analysis time-consuming and error-prone. This project would leverage LLMs to understand, summarize, classify, or extract insights from text documents, to speed up processes like contract review, compliance checks, or research analysis.

Examples of companies that work on problems like analyzing large text documents using LLMs:

1. IBM Watson Health: Uses AI to analyze medical literature and records for insights.
2. LexisNexis: Implements AI for legal document analysis and research.

Find a solved and explained example of Analyzing Large Text Documents using LLMs using Python here.

Summary

So, here are 5 AI & ML projects you should try to boost your resume:

1. Building a Smart Loan Recovery System
2. Building a Next Word Prediction Model
3. Building a Generative AI Model to Generate Synthetic Data
4. Building a Demand Forecasting Model to Plan and Optimize Inventory
5. Analyzing Large Text Documents using LLMs

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