Machine learning (ML) is a fundamental application of artificial intelligence technology and has enormous potential in a variety of industries including healthcare, business, education, and more. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay The fact that machine learning is still in a nascent stage and has several imperfections/flaws can make it difficult to understand its fundamentals. However, studying and working on some basic projects on the same can be of great help. So here are some to get you started. Stock Price Predictor A system that can know a company's performance and predict future stock prices is not only a great application of machine learning, but also has real-world value and purpose. Before proceeding, be sure to familiarize yourself with the following: Statistical modeling: Constructing a mathematical description of a real-world process that takes into account the uncertainty and/or randomness involved in that system. Predictive analytics: uses different techniques such as data mining, artificial intelligence, etc. to predict the behavior of certain results. 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. the predictor. For example, understand the impact of annual experience on salary. Action Analysis: Analyze the actions performed by the above-mentioned techniques and incorporate the feedback into machine learning memory. The first thing you need to start is selecting the types of data to be used such as current prices, EPS ratio, volatility indicators, etc. Once this has been resolved, you can select your data sources. For example, Quandl offers organized financial and economic data. From here you can download stock data of several thousand companies in different formats like xml, csv, etc. Likewise, Quantopian offers excellent support for developing trading algorithms that you can check out. Now you can finally plan how to backtest and build a trading model. Keep in mind that you need to structure the program in such a way that it is able to quickly validate predictions as financial markets are generally quite volatile and stock prices can change several times a day. What you want to do is connect your database to your machine learning system which receives new data on a regular basis. A running loop can compare the stock prices of all the companies in the database for the last 15 years or so and predict the same for the near future, such as 3 days, 7 days, etc., and report it on the display. Sentiment Analyzer A sentiment analyzer learns about the “sentiment” behind a text (think emails, instant messages, social media posts, etc.) through machine learning and predicts the same using artificial intelligence . The technology is increasingly being used on social media platforms like Facebook and Twitter to learn user behavior, and also by companies that want to automate lead generation by determining the likelihood of a potential customer doing business with them by reading their emails. One innovation you will need to know about in this project is classifiers. You can, however, choose any particular model you feel comfortable with, such as the Maximum Entropy Classifier or the Naïve Bayes Classifier. You can approach the project in your own way. However, ideally you should classify texts into three categories: positive, neutral and negative. You can extract the different texts for a particular word.