There are many different applications and uses for the Python programming language - web development, gaming, software development, AI, machine learning, and language development to name a few. The top 3 uses, however, are web development (including app-building), data science, and scripting.
Machine learning includes computer tasks such as speech recognition ("Hey Google") and the algorithm that generates and presents YouTube or Netflix recommendations. In fact, YouTube was written with the Python programming language, and the Google search system is also based on it. It is often used in scientific computing, analysis, and reporting, and is a key component of applications that process scientific data.
When it comes to machine learning, there are various Python libraries and frameworks available. Some of the most widely-used are TensorFlow and scikit-learn, while Django, Flask, Falcon, and Pyramid are also very popular. Scikit-learn has some top machine learning algorithms built-in, while TensorFlow can be used to custom build algorithms. For anyone starting out on a machine learning project, scikit-learn is a good place to start. Python is the primary language behind machine learning. Teaching machines to learn is a relatively new, not to mention revolutionary, process. Machine learning based on this language teaches computers to recognize patterns and learn from them. Artificial intelligence built with this coding language can even perform tasks such as making predictions and estimations. Data science professionals who work in machine learning generally favor this option as their coding language thanks to its simplicity, independence of platform, and wide range of available libraries to work from.
The next use, data analysis and visualization is used to present data (information) in a visual way, often in order to draw conclusions and make decisions based on the information. Python is ideal for creating graphs such as bar charts and line charts, thus representing details in an image. The details or data that is presented are often numerical and can be based on a given time period. There may be multiple factors involved and charts can be as simple or as complex as needed. For data analysis and visualization, one of the most popular libraries for this particular coding language is Matplotlib - this library is straightforward to use and many other data-centric libraries are based on it, so it provides a great foundation or starting point for data analysis with Python in general.
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Also, our Data Analytics Bootcamp covers this language, along with Git, MySQL, and statistical analysis, all of which are important for understanding, analyzing, and working with data. In a nutshell, Python is great for AI creation and teaching machines, data analysis - especially when it comes to large amounts of data - and is ideal for scripting. If you need to focus on any of these tasks, it is the best programming language to use. It is used by new developers and experienced developers alike and is a flexible, highly functional code to use for a wide range of tasks.
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