Of the many data science tools made available each year, tools used to create machine learning models are especially in-demand. While many people associate machine learning models and tools with deep learning and artificial intelligence (AI), these tools are also useful to data scientists that just want to know how to automate the data collection process. Learning more about the latest machine learning models and tools is essential to making the process of completing a data science project easier and more efficient!
What are Machine Learning Models?
Machine learning models are files or packages which include a set of criteria that teach a computer or machine how to recognize patterns or groupings within a dataset. These models are usually paired with an algorithm that gives the machine additional instructions on how to perform a series of tasks. In this sense, machine learning models are able to learn from a dataset how to recognize certain patterns and trends over time. Within the field of data science, machine learning models are commonly used to make predictions based on a dataset, as well as to automate specific tasks and processes. For example, data scientists can create a machine learning model to sort through a dataset, clean and organize portions of data, and forecast a future outcome based on specific conditions.
When working with big data, these models are especially useful because they can be used to automate a rote or routine process which can take an extensive amount of time and effort to complete. machine learning models fall into two distinct categories: supervised and unsupervised machine learning models. Supervised machine learning models require some type of oversight from the person that created them, while unsupervised machine learning models can run on their own without any extensive input from the creator. In this sense, unsupervised machine learning models are most commonly used during the process of automation. These models are also useful in the process of data visualization, as you can visually demonstrate how a decision was made or why a certain outcome is possible through a prototype or graph of a dataset.
Top 5 Tools to Build Machine Learning Models
There are many machine learning tools to choose from. The following list names on some of the most popular software and packages that are offered from a diverse set of companies and computer programming languages. While each of these tools can be used to build machine learning models, many of the tools specialize in specific types of machine learning algorithms that can be created with the software.
1. Microsoft Azure Machine Learning
With a focus on responsible machine learning, Microsoft Azure offers several features which simplify the process of building and deploying models within the platform. Through multiple collaborative tools, as well as Microsoft’s MLOps and DevOps, Microsoft has embraced the open-source movement while also ensuring that there is a high level of safety and security to data that is stored and analyzed with this platform.
2. Google TensorFlow
As a company, Google offers several tools to create machine learning models, such as GoogleCloud MLKit. However, TensorFlow is one of the most widely cited machine learning tools from the company, which has many uses for data scientists. TensorFlow is an open-source machine learning platform that can be used with Python or through the features embedded in the platform. As a tool for building machine learning models, this platform includes a robust machine library that offers algorithms for building recommendation systems, clustering algorithms, and deep learning models.
3. RapidMiner
Describing itself as “the best data science & machine learning platform,” RapidMiner is another data science tool that has multiple uses for individuals with varying levels of experience in data science and machine learning. The platform itself includes several features and tools which allow you to work with machine learning algorithms such as clustering and classification, through automated modeling and predictive modeling. In contrast to other machine learning platforms, RapidMinder has a visual desktop. This makes it easier to work with datasets. The platform is also useful for automating the processes of data cleaning and wrangling.
4. Apache Mahout
As part of the Apache Software Foundation, Apache Mahout is one of several products within this ecosystem that can be used to build machine learning models. Used for data mining, Mahout includes machine learning algorithms for regression, clustering, and recommendation systems. This tool is also unique in its use of distributed linear algebra algorithms which allow users to work with additional mathematical functions and graphs. The inclusion of this tool within the Apache community also means that there are regular updates and an abundance of resources on how to use this
5. Scikit-Learn
Built in conjunction with several Python libraries, Scikit-learn is one of the most popular open-source programming libraries for machine learning. This tool can be used to create an extensive amount of machine learning models, including but not limited to, regression models, classification systems, clustering, and dimensionality reduction. The large selection of machine learning models available within Scikit-learn also makes it a go-to resource for data scientists that are interested in predictive analytics and data forecasting.
Interested in learning more about machine learning models?
Data science tools that are capable of creating machine learning models are a necessity for students and professionals that are not only interested in algorithms and AI, but also anyone who wants to automate repetitive tasks, streamline the decision-making process and make more accurate predictions and forecasts. Noble Desktop offers machine learning courses that can teach you how to use predictive analytics and programming languages in your projects. In addition, any of Noble Desktop’s data science classes are an excellent addition to training in machine learning. The Data Science Certificate program offers instruction in employing machine learning algorithms with the Scikit-learn library.