Machine learning (ML) is an essential application of artificial intelligence technology and has enormous potential in a variety of fields, including healthcare, business, education, etc. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an Original Essay The fact that ML is still in its infancy and has several imperfections/flaws can make it difficult to understand its fundamentals. However, studying and working on a few basic projects on the same topic can be of great help. So here are a few to get you started. Stock Prices Predictor A system that can know how a company is performing and predict future stock prices is not only a great application of ML, but also has real-world value and purpose. Before continuing, be sure to familiarize yourself with the following: Statistical modeling: constructing a mathematical description of a real-world process that accounts for the uncertainty and/or randomness involved in that system. Predictive analysis: It uses several techniques such as data. mining, artificial intelligence, etc. to predict the behavior of certain outcomes. Regression Analysis: It is a predictive modeling technique that learns the relationship between a dependent variable, i.e. the target, and one or more independent variables, i.e. i.e. the predictor. For example, understanding the impact of annual experience on salary. Action Analysis: Analyze actions performed by the techniques mentioned above and integrate the feedback into machine learning memory. The first thing you need to start is to select the data types to be used such as current prices, EPS ratio, volatility indicators, etc. Once this is set, you can select the data sources. For example, Quandl offers curated financial and economic data. Here you can download stock market data of several thousand companies in several formats such as xml, csv, etc. Likewise, Quantopian offers excellent trading algorithm development support that you can check out. Now you can finally plan how to backtest and create a business model. Note that you should structure the program so that it is able to validate forecasts quickly, because financial markets are usually quite volatile and stock prices can change several times per day. What you want to do is connect your database to your machine learning. system that regularly receives new data. A running cycle can compare the stock prices of all companies in the database over the last 15 years or so and predict the same in the near future i.e. 3 days, 7 days, etc. ., and report on the screen. Sentiment Analyzer A sentiment analyzer learns about the “feeling” behind a text (think emails, instant messages, social media posts, etc.) through machine learning and predicts the same thing. help from AI. The technology is increasingly used on social media platforms such as Facebook and Twitter to learn user behavior, as well as by businesses that want to automate lead generation by determining the likelihood that a prospect will be able to do business with them by reading their emails. What you will need to learn about in this project are classifiers. However, you can choose any model.