Machine learning (ML) is a key application of artificial intelligence technology and has huge potential in many fields such as healthcare, business, education and more.
The fact that ML is still in its infancy and has several flaws / shortcomings may make it difficult to turn the head around its fundamentals. However, researching and doing a few basic projects on the same topic can be a great help. So here are a few starting points:

1. Stock Price Forecaster
A system that can learn about a company’s performance and predict future stock prices is not only a great application for machine learning (ML), but it also has value and purpose in the real world. Before you continue, be sure to read the following:

• Statistical modeling: A mathematical description of the real-life process is constructed that takes into account the uncertainty and / or randomness associated with the system.
• Proactive analysis: It uses several techniques such as data mining, artificial intelligence, etc. to predict the behavior of certain results.
• Recession analysis: It is a predictive modeling technique that learns about the relationship between a dependent, i.e., object, and independent variables, i.e., a predictor. For example, understand the impact of annual experience on salary.
• Activity analysis: Analyzing the functions performed by the above techniques and incorporating feedback into machine learning.

First, you need to start by choosing the types of data that will be used, such as current prices, EPS ratio, volatility indicators, etc. Once this is done, you can select the data sources. For example, Quandl provides organized financial and economic information. Here you can download stock information for several thousand companies in several formats like xml, csv etc. Similarly, Quantopian provides excellent support for the development of trading algorithms that you can check. Now you can finally plan how to test and build your trading model. Note that you need to build the program so that it can validate forecasts quickly financial markets are usually quite volatile and stock prices can change several times a day.
You just want to connect the database to a machine learning system where new information is entered regularly. The current cycle can compare the stock prices of all companies in the database over the last 15 years and predict the same for the near future i.e. 3 days, 7 days etc. and report on the screen.

2. Sentiment Analyzer
The tunnel analyzer uses machine learning to learn the “feeling” behind the text (think emails, instant messages, social media messages, etc.) and predict the same using Artificial intelligence (Artificial intelligence). Technology is increasingly being used on social media platforms, such as Facebook and Twitter, to learn user behavior, and also for companies looking to automate lead generation by determining how likely an opportunity to do business with them reads in their emails.
One innovation you need to learn in this project is classifiers. However, you can choose any particular model that you are comfortable with, such as Maximum Entropy Classifier or Naïve Bayes Classifier.
You can proceed with the project the way you want. Ideally, however, you should classify the texts into three categories – positive, neutral, and negative. You can decompose different texts for a particular keyword and perform a classification for each to get the labels. For features, you can use charts or even dictionaries to improve accuracy.

3. Sports match predictor
Using basic work model of machine learning You can also create a system that can predict the results of sports competitions such as cricket, football, etc.
The first thing you need is to create a database for the sport of your choice. Regardless of your choice, you will likely need to find records of points, performance data, etc. by yourself, manually. However, using Json for this can be a good idea, as it can easily capture the advanced parameters associated with the game and help make more accurate predictions. If you are familiar with Python, Scikit-Learn is the best bet for creating a system. It provides a variety of tools for data mining, regression analysis, classifications, etc. You can use human analysis, such as Vegas lines, as well as some advanced parameters, such as Dean Oliver’s four factors, for the best forecast results.

There are many beginner machine learning projects such as the ones you can study. However, it helps if you first read the following:

Machine learning tools: An environment that provides ML tools for data preparation, different ML algorithms, and is able to present the results of programs can be a good starting point when you want to get to the core of ML and understand how different modules work together. For example, Weka, waffles, etc. are great environments at first.

Machine learning datasets: Artificial intelligence and ML use different materials. However, you can select only one and choose the algorithm that best suits it. You can then use the ML environment to monitor it closely. You can also change the algorithms to see how they affect the data. Machine learning can only be managed a lot by experimenting and practicing. While deepening into theory can certainly help, it is the application that will make your progress the most.