Python is known for many things, but one of the primary benefits of using this language is having access to its many open source libraries and communities. Regularly updated by multiple forums of dedicated users and contributors, Python’s data science libraries include a plethora of resources for students and professionals that want to create their own projects and products using this popular programming language. From mathematical libraries like NumPy to data visualization hubs like Matplotlib, there are many Python libraries that are especially useful for anyone interested in learning more about how to manipulate and manage information and data.
The language is also known for its fast-growing interest in Automated Machine Learning (AutoML) and the uses of artificial intelligence and algorithms within society. Some of the most popular Python libraries have a strong emphasis on automation and machine learning models and resources. Of all of the Python libraries, Scikit-learn is one of the most well-known libraries for machine learning, and it combines many of the benefits of other data science libraries within one online space.
What is Scikit-Learn?
Initially released in 2007, Scikit-learn is a machine learning library that includes an extensive catalog of resources that offer dozens of algorithms and statistical models that can be applied to a variety of projects. The resources in the library focus on algorithms and machine learning models which are useful to data scientists, such as regression, classification, and clustering. Scikit-learn is one of many open-source data science libraries which are compatible with Python. Many of the resources in the library are regularly updated by contributors in the Python community, many of whom are Developers and Data Scientists that are invested in the open-source movement, collaboration, and sharing.
Scikit-learn includes everything that you would need to get started with automation and artificial intelligence, with forums and blogs focused on introducing beginner data scientists on how to work with this library. New users of Scikit-learn can best utilize this library by calling specific functions, most of which focus on the training of machine learning models. By using this library to train and test machine learning models, any data science student or professional can employ this library to practice automating machine learning models and applying the skills that they have learned to other projects or platform development.
How Data Scientists Use Scikit-learn
Since Scikit-learn is built on several Python libraries (specifically NumPy, SciPy, and Matplotlib), this machine learning library can be used in conjunction with other data science libraries in order to develop data visualizations, machine learning algorithms, and predictive analytics. Each of these uses involves a variety of applications within data science, from image processing to model analysis.
Data Visualizations and Models
One of the many uses of Scikit-learn is the creation of data visualizations based on various machine learning models. The library includes resources to work with an application programming interface (API) which allows you to plot graphs and present a dataset through a set of commands and functions.
After importing one of the many data visualization models in the library, you can also plot various graphs. For data scientists, these visualization capabilities are especially useful when presenting findings and offering examples of how a model works or the ways that it can perform. This API can also be used when working with predictive analytics and algorithms.
Machine Learning Algorithms
In addition to Scikit-learn’s uses when working on data visualizations, the primary purpose of this library is working with machine learning algorithms. When browsing the catalog of resources in this library, all of the most commonly used algorithms and statistical models are available to use.
Each algorithm also includes applications, or examples, of how the algorithm can be used within a data science project. For example, data scientists working in business and finance who are interested in tracking the trends in stock prices can use one of the regression models in the Scikit-learn library. There are also algorithms that are useful for modeling consumer behavior, transforming textual data into numerical information, and creating more efficient and effective machine learning models.
Predictive Analytics
Predictive analytics are also commonly used in conjunction with algorithms and machine learning to make projections about the future. Specifically, predictive analytics is a form of data analysis that is based on data that can be generated from an automation that has been programmed to collect or sort through data over time or from a particular time period.
The machine learning algorithms available in the Scikit-learn library can be used by data scientists in order to create forecasts based on an imported dataset or a data collection tool. Similar to the other uses of Scikit-learn, this particular function is especially useful for data scientists that are working in business and finance, advertising and marketing, as well as any industry which focuses on tracking patterns of behavior or change over time.
Need to learn more Python Data Science Libraries?
As one of many Python data science libraries, Scikit-learn is a popular choice for anyone that is interested in learning more about predictive analytics, data visualization, and machine learning. Many of Noble Desktop’s Python classes include hands-on instruction in Scikit-learn, as well as many other data science libraries. The Python Machine Learning Bootcamp gives students the skills to work with Scikit-learn’s machine learning algorithms, such as regression and random forest models.
For students and professionals that want a more holistic approach to Python, the Data Analytics Certificate includes several bootcamps which offer instruction in some of the most popular data science libraries such as Scikit-learn, Pandas, and NumPy. Any student or professional will find Noble Desktop’s data science classes an excellent complement to their current skill set or to expand into a new career in the field.