Machine learning is feeding technological items some sort of thinking ability, such as tablets and computers, which can learn something from programming and other data. Although it appears to be a future notion, most individuals utilize this level of technology on a daily basis. A good example of this is speech recognition.
Learning, thinking, problem-solving, and perception are all goals of these intelligent creatures. Many ideas, methodologies, and technologies are used in AI. Machine learning, neural networks, deep learning, cognitive computing, computer vision, and natural language processing are just a few of the subfields.
Youtube spam remover AI
YouTube’s success attracted not just real viewers, but also spammers. As a result, the number of undesirable spam videos and comments has increased. Here’s where an AI-based YouTube spam comment detection algorithm comes in handy. You will use text and words to identify online comments as spam or not spam in this AI project. Bag-of-words and random forest approaches can be used to create a spam detection model. Your bot needs google account access to work properly. Permission to read youtube channel data.
Movie recommender for people
Almost everyone nowadays watches movies and television shows using technology. While deciding what to watch next might be difficult, recommendations are frequently made based on a viewer’s past viewing habits and interests. This is accomplished by machine learning, and it may be a fun and simple project for novices. New programmers may get some practice by writing code in Python or R and using data from the Movielens Dataset. Movielens now has over 1 million movie ratings for 3,900 films, thanks to the contributions of over 6,000 individuals.
Food quality predictor
This AI-powered initiative categorizes a wide range of foods into cuisines and flavors. We use a multi-scale convolutional network to build a deep learning model. Yummly48k is a food attribute dataset extracted from the Yummly website. In addition to the multi-scale convolutional network, the model is created using Negative Log-Likelihood (NLL).
Artificial neural networks can be used to create a handwritten digit recognition system that can accurately interpret digits drawn by a person. A convolution neural network (CNN) is used to recognize digits on paper in this case. The HASYv2 dataset for this network contains 168,000 pictures from 369 different classifications.
A handwritten digit recognition system can interpret mathematical symbols and handwriting styles from pictures, touchscreen devices, and other sources, in addition to papers. This software can be used to authenticate bank checks, read filled forms, and take brief notes, among other things.
Real state price predictor
This concept is based on the way that real estate prices change over time. The goal is to use artificial intelligence to forecast house price variations. Machine learning models or other comparable techniques might be used to do this. You may get acclimated to enormous datasets and test web scraping techniques by working on this project. This project would need either getting a Kaggle public dataset or web scraping to create one’s own dataset. Following the acquisition of the required dataset, it must be cleaned by locating anomalies, null values, duplicate entries, and computing different important histograms. The data is then trained and tested, after which we construct a model, define a performance metric, and develop a model.
Plagiarism is common on the internet. Content abounds on the internet, and it can be accessed on millions of different websites. At times, determining which information is plagiarized and which is not might be difficult. Authors of blog posts should double-check to determine if their work has been plagiarized and published elsewhere. News companies should look into if a content farm has copied and repackaged their stories as their own. The work is difficult. What if you had your own program for detecting plagiarism? AI has created this chance.