A Machine Learning Engineer uses data and algorithms to build intelligent systems. If you are new to machine learning, you need to work on beginner-level machine learning projects to understand how to use machine learning algorithms on datasets to solve problems. If you are looking for some Machine Learning projects with Python to practice Machine Learning concepts, this article is for you. In this article, I will take you through some amazing Machine Learning projects with Python you can work on as a beginner in Machine Learning.
40+ Machine Learning Projects with Python
Classification:
End to End Chatbot
Loan Approval Prediction
Text Emotions Classification
Credit Score Classification
Ads Click-Through Rate Prediction
Consumer Complaint Classification
Password Strength Checker
Spam Comments Detection
Online Food Order Prediction
MNIST Digits Classification
Online Payments Fraud Detection
Classification with Neural Networks
Language Detection
News Classification
Iris Flower Classification
Sarcasm Detection
Social Media Ads Classification
Regression:
Dynamic Pricing Strategy
Retail Price Optimization
Food Delivery Time Prediction
Diamond Price Prediction
Salary Prediction
House Rent Prediction
Instagram Reach Prediction
Student Marks Prediction
Waiter Tips Prediction
Cryptocurrency Price Prediction
Stock Price Prediction
Health Insurance Premium Prediction
Time Series:
Netflix Subscriptions Forecasting
Currency Exchange Rate Forecasting
Instagram Reach Forecasting
Time Series Forecasting with ARIMA
Weather Forecasting
Website Traffic Forecasting
Business Forecasting
Recommendation Systems:
Music Recommendation System
Hybrid Recommendation System
News Recommendation System
Clustering:
Credit Scoring & Segmentation
App User Segmentation
Credit Card Clustering
Topic Modelling
Clustering Music Genres
Summary
I hope this list of Machine Learning projects will help you improve your practical implementation of all the concepts of Machine Learning. This list will keep updating with more projects. Please feel free to ask valuable questions in the comments section below.