Data science has moved from a focus on the collection and analysis of data to the use of information and data to streamline complex processes and to make predictions. Consequently, automation and machine learning are two of the most widely discussed applications of algorithms and artificial intelligence within the data science industry. Data Scientists can program algorithms in order to automate machine learning models that are used to make the process of completing projects and forecasts simpler and easier.

While there are many programming languages that data scientists can learn to work with algorithms, there are several reasons why you should learn Python for data science and machine learning. Python is associated with several libraries which specialize in data collection, analysis, visualization, and sharing. Through these libraries and many other unique features, Python is an excellent programming language to learn for Data Scientists who want to develop their skills in automation and machine learning.

What are Automation and Machine Learning?

Although it is common to hear or see the terms “automation” and “machine learning” placed together, automation and machine learning represent two distinct aspects of working with algorithms and artificial intelligence (AI). Automation is the process by which a system, method, or model decreases or eliminates its need for human input. In this sense, automation is what allows a machine to work on its own. In contrast, machine learning is a subset of artificial intelligence which allows a computer or technology to learn more from human beings. Therefore, machine learning models can map or imitate the things that people do so that computers and other machines can also do them.

Automation and machine learning are commonly discussed in the same breath because machine learning tends to be the means by which a process is automated. Through the use of statistical models and algorithms, a machine can learn how to perform a specific task which makes for the automation of a system over time. For example, recommendation algorithms within a social media platform allow computers to learn about users in a way that results in the system offering recommendations to the user without human input. Automation and machine learning are also very popular within the realm of robotics and engineering, where machines are taught to complete physical or mundane tasks, such as moving boxes or cleaning a room.

How is Python Used for Automated Machine Learning?

While there are many uses for automation and machine learning across industries, within the data science industry Python is most commonly used to work with algorithms that can create an automated process and/or facilitate machine learning during data collection and analysis as well as in creating data visualizations. Python is used in the combination of automation and machine learning under the auspices of Automated Machine Learning (AutoML).

AutoML is the use of specific tools and techniques to create predictive models and automate data science tasks through the use of algorithms and artificial intelligence. Python can be used to communicate and program algorithms using statistical models, such as linear regression and the creation of artificial neural networks, which are useful for engineering, mobile development, and data science projects. In addition, Python is known for its data science libraries which include information and algorithms that can be remixed and shared amongst users.

Python Machine Learning Libraries

As an open-source programming language, Python is known for its community of data scientists and developers who regularly update the libraries. The following data science libraries for Python are widely used for machine learning by giving data science students and professionals the resources to work with algorithms and artificial intelligence. Each of these libraries offers resources for data scientists that are interested in learning more about statistics and how to deploy and visualize machine learning models. These libraries are especially helpful when using Python to create models and visualizations for data science projects and presentations.

1. NumPY

Popular amongst multiple fields and industries from Economics to Neuroscience, NumPy is commonly used by data scientists that are working with Python and mathematical equations. Acting as the foundation for multiple other data science libraries, NumPy includes several visualization, algorithm, and machine learning tools. When using this library, data scientists will primarily employ statistical tools and techniques to create machine learning models.

2. Pandas

Known by the shorthand Pandas, the Python Data Analysis Library includes resources for working with automation and machine learning along with other data science tools. For data scientists, Pandas is known for its use of data frames and its data organization capabilities. When combined with automation and machine learning, Pandas can be used to help speed up the process of organizing a dataset or database management system.

3. Scikit-learn

As one of the Python libraries connected to NumPy, Scikit-learn includes an expansive catalog of algorithms that specialize in automation and machine learning. Whether you need to create a model for regression or a classification system, Scikit-learn offers several statistical models that can be used for the development of algorithms. Despite its similarities to other Python libraries, Scikit-learn differentiates itself by specializing in the development of predictive models which can be visualized through intriguing and aesthetically pleasing charts and graphs.

4. Auto-Sklearn

Acting as an almost meta-machine learning library, Auto-Sklearn was built from the Scikit-learn library, and it was created to ease the process of choosing a machine learning algorithm to use for data science projects. This unique library helps to eliminate the trial and error process of determining what algorithm to choose, by searching for the right algorithm and returning that information to a data science professional. The library automates the process of finding machine learning models, including over a dozen algorithms available within its catalog.

5. Keras

Keras is most useful for deep learning projects that require the creation of artificial neural networks. Offering short examples of code that are written in Jupyter Notebooks, this library is easily accessible to both students and professionals interested in automation and machine learning. Keras is also a part of the TensorFlow ecosystem, another popular machine learning platform with software that is compatible with various programming languages, such as JavaScript, and is widely used in the development of mobile applications.

Need to know more about Python for Machine Learning?

Knowledge of algorithms, automation, and machine learning are highly sought-after data science skills that can help boost your resume and career prospects in the field. Noble Desktop offers several data science classes, including bootcamps and certificate programs that introduce students to creating and deploying machine learning models as well as learning various programming languages. The Python Machine Learning Bootcamp focuses on building a background in statistics and algorithms in order to develop machine learning models.

The Machine Learning Bootcamp requires some background knowledge of working with different data science libraries and algorithms. Students and professionals that are beginners in the field can improve their knowledge of Python through any of Noble Desktop’s Python classes. Anyone who is interested in learning more about automation and machine learning has several courses, bootcamps, and certificate programs from which they can choose to move from a beginner to more advanced abilities.