In this video, " Top 10 Machine Learning Projects for 2023 " we will take you through the best ML Projects for your Resume. In this tutorial, we'll cover 10 Ml project ideas for 2023 that will help you make your Resume stand out. In addition, you will also get to know how these projects work, and you'll also get a reference for each ML Project Idea. Below are the topics we are going to discuss in this Machine Learning tutorial.
✅ 00:00 Top 10 Machine Learning Projects for 2023
✅ 00:38 Loan Approval Prediction System
✅ 01:09 Fake News Detection System
✅ 01:44 Personality Prediction System
✅ 02:26 Parkinson's Disease Prediction System
✅ 03:03 Text-to-Speech Converter System
✅ 03:45 Speech Recognition System
✅ 04:24 Sentiment Analysis
✅ 04:56 Image Classification Using CNN
✅ 05:32 Face Recognition System
✅ 06:02 Movie Recommendation System
Below are the references for Top 10 Machine Learning Projects for 2023
Loan Approval Prediction System - News Prediction System - Prediction System - Disease Prediction System - Converter System - Recognition System - Analysis - Classification Using CNN - Recognition System - Recommendation System - Free Machine Learning Course With Completion Certificate:
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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