The Top 5 Machine Learning Tools

CareerFoundry blog contributor Alex Williams

Machine learning is a method of data analysis that provides impressive results in areas like data analytics, data science, and even business operations. Machine learning makes complicated tasks simple and convenient. And, with the right machine learning tools, you can create algorithms, train your models, and discover new ways of implementing this innovative technology. 

Machine learning is a type of artificial intelligence that learns how to execute tasks without programming. Since the machine learns through experience, it can be taught to perform with training and modeling. If this sounds a little futuristic, it is. Machine learning is a revolutionary tool changing the landscape for organizations with automation and advanced analytics at record speeds. 

Many tools, software, and platforms are built to create machine learning algorithms and automate tasks. Each tool operates differently based on the type of tool, programming languages, and the hosting platform they operate on. 

Let’s take a closer look at five of the most popular machine learning tools data scientists use to develop new ways of collecting, interpreting, and reporting data. We’ll also discuss how to choose the best machine learning tool for your needs. 

  1. How to choose a machine learning tool
  2. The top 5 machine learning tools
  3. Final thoughts

Ready to get started? Let’s go!

1. How to choose a machine learning tool

There are several factors to consider when choosing the right machine learning tool. But before we get too far into the details, we first have to address your network implementation.

Most machine learning tools are built for cloud implementations. While some small organizations can get away with using machine learning on-premises, larger companies that have yet to migrate to the cloud should consider a move. If your dataset is too large and complex, you may need to run multiple machine learning instances in the cloud. 

Since machine learning and cloud computing go hand in hand, global cloud spending is projected to near $500 billion in 2022. This is important because it means more cloud service providers will be compatible with machine learning tools as technologies develop. 

However, programmers and data scientists will need to consider whether or not their implementation supports a particular machine learning tool that meets their overall goals and needs. They’ll also need to consider the nature of the machine learning projects that will be developed. Several types of machine learning tools can run autonomously, with supervision, or learn through reinforcement.  

Do you need to run analytics on simple data sets? Classification and regression tools might suit your needs. Do you want your tool to run on its own with little supervision? Density estimation, clustering, visualization, or projection tools might be right for you. Are data science, mathematics, and visualization crucial to your research? Supervised learning platforms may help with your project. 

Here is a brief overview of some of the different types of machine learning tools:

  • Supervised learning involves using models to learn how to map between input examples and the target variables. 
  • Unsupervised learning involves using a model to understand relationships in data. 
  • Reinforcement learning is where an agent learns to operate using feedback.

Many more machine learning tools are out there, with features overlapping these three main types. 

2. The top 5 machine learning tools 

Today, we’ll discuss five of the most popular machine learning tools available:

  1. PyTorch
  2. TensorFlow
  3. KNIME
  4. Apache Mahout
  5. Rapid Miner

1. PyTorch

Type of tool: PyTorch is a deep learning framework that makes use of GPU. Deep learning frameworks are machine learning tools that offer teams the building blocks they need to design, train, and validate deep neural networks through a high-level programming interface. It runs on Linux, macOS, and Windows, and is best for projects written in languages such as Python, C++, and CUDA. 

Price: Free

Used for: Fast and flexible, PyTorch is popular because it is used in some of the most important aspects of machine learning, like tensor calculations and building deep neural networks. Some of its features and algorithms include the autograd module, optim module, and nn module for building neural networks.

Pros: 

  • Can be used in the cloud
  • Provides distributed training
  • Offers users an extensive machine learning library
  • The hybrid front end is easy to use
  • Helps create computational graphs

Cons:

  • Small developer community
  • Limited use in production
  • Limited monitoring

2. TensorFlow

Type of tool: TensorFlow is an open-source framework for large-scale numerical machine learning applications. It utilizes machine learning algorithms to train neural network models that run on CPU and GPU. It operates well with Linux, macOS, and Windows for projects written in Python, C++, and CUDA.

Price: Free

Used for: TensorFlow provides a JavaScript library for dataflow programming that is crucial for building training neural networks. It is often used for projects that involve image classification and natural language processing. TensorFlow and PyTorch both offer frameworks to help developers build neural networks, but TensorFlow is a favorite for production due to its scalability.

Pros: 

  • Can be used by script tags
  • Can also be used by installing through NPM
  • Helps with human pose estimation

Cons:

  • Not as easy to use as PyTorch
  • Difficult to learn

3. KNIME

Type of tool: This open-source GUI-based tool doesn’t require users to have any coding knowledge, due to its guided automation machine learning. KNIME breaks down complicated machine learning processes so teams can easily run data analytics instances with their own algorithms. 

Price: Free

Used for: KNIME is mainly used for data analytics operations, including data mining and manipulation. You can use KNIME to create and execute workflows to process data, generate reports, and integrate with other processes. Business intelligence, financial data analysis, and CRM are all excellent use cases for KNIME. 

Pros:

  • SaaS alternative
  • Easy installation
  • Simple to deploy
  • Easy to learn

Cons:

  • Limited visualization tools
  • Lack of exporting capabilities
  • Hard to build complex models

4. Apache Mahout

Type of tool: Apache Mahout is a Hadoop-based, open-source platform. It uses machine learning techniques like classification, regression, and clustering. This tool is widely used to create scalable machine learning algorithms. 

Price: Free

Used for: Statisticians, mathematicians, and data scientists use Apache Mahout to execute algorithms. It follows a distributed linear algebra framework and includes Java libraries for common math operations. Mahout can also be used to find commonalities in large data groups and tag large volumes of content online. 

Pros:

  • Works well for large datasets
  • Enables users to define new language features
  • Simple to use

Cons:

  • Lacks documentation
  • Some commonly used algorithms are missing

5. Rapid Miner

Type of tool: Rapid Miner is a data science platform that uses machine learning to test data and other models quickly. It works on all cross-platform operating systems and has a helpful interface for individuals with limited programming knowledge. 

Price: Rapid Miner is available at four different price points (be sure to contact Rapid Miner to determine which package/pricing plan is appropriate for your enterprise):

  • Free plan
  • Small package: $2,500/year
  • Medium package: $5,000/year
  • Large package: $10,000/year

Used for: Rapid Miner provides a platform for machine learning, data preparation, text mining, deep learning, and predictive analytics. It is often used for research, educational purposes, and application development. It has a simple drag-and-drop interface perfect for non-programming team members to design and implement analytical workflows. The tool includes result visualization, model validation, optimization, and data preparation capabilities. 

Pros:

  • Extensibility through plug-ins
  • No programming skills are necessary
  • Simple to use interface

Cons:

  • High financial investment

3. Final thoughts

Machine learning has become widely available in recent years, creating an even more competitive environment driven by agility, speed, and data analytics. These powerful tools enable data analysts to automate tasks, learn about user behaviors, and gain deeper analytic insights. 

There are many factors that go into choosing the best machine learning tool for your next project. The right tools for you will be easy to use, meet your financial requirements, and allow you to create automation and algorithms that solve recurring issues. 

Interested in machine learning, or data analytics in general? Why not try out our free, self-paced data analytics short course?

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