Top 16 Machine Learning Tools to Make Your Business Smarter

Top 16 Machine Learning Tools to Make Your Business Smarter

Machine learning (ML) is reshaping the way companies make decisions and deal with ever-growing data.

Chatbots are taking over customer service queries. Banks are using intelligent software to detect fraud. SaaS companies are predicting when customers are likely to churn. And, yes, all these tasks (and many others) are powered by machine learning tools.

In the last few years, machine learning has become more accessible than ever, making it easier for businesses to adopt this technology. You can either build or buy a SaaS solution, depending on your budget, the amount of data you handle from day to day, and the technical skills available to you.

Open-source libraries allow you to build your own machine learning software. But you’ll need a team of developers and data scientists to navigate the complexities of open-source code. You’ll also need to spend months fine-tuning machine learning algorithms and integrating custom-built ML tools to your stack.

SaaS (software as a service) products for machine learning, on the other hand, are out-of-the-box solutions that let you get started with machine learning in next to no time. They often provide pre-trained machine learning models, as well as powerful APIs, and integrations with tools you use every day.

Read on to find the best machine learning tools and solutions for your business.

16 Best Machine Learning Tools

  1. MonkeyLearn | No-code ML tools for text analysis & visualization
  2. Knime | Data science workflows that you can visualize
  3. Accord.Net | ML libraries for audio and image processing
  4. Scikit-Learn | User-friendly Python library for machine learning
  5. TensorFlow | Large-scale deep learning projects
  6. Weka | Open-source library with an easy learning curve
  7. PyTorch | A fast deep learning framework
  8. Keras | Beginner-friendly library for deep learning
  9. RapidMiner | Intuitive interface for data science
  10. Google Cloud ML Engine | Run ML models on the cloud
  11. Azure Machine Learning Studio | Drag-and-drop machine learning platform
  12. IBM Watson | Powerful APIs for AI applications
  13. Apache Mahout | Scalable machine learning models
  14. Apache Spark | Super fast library for big data processing
  15. Amazon Machine Learning (AML) | Rich set of tools & pre-trained models
  16. Colab | Free GPU for machine learning projects

1. MonkeyLearn

MonkeyLearn is a SaaS machine learning tool that makes text analysis accessible to everyone. You can build custom models for topic classification, keyword extraction, and more, and tailor them to your needs to gain rich insights from your data.

For a quick start, you can use pre-trained models like this sentiment analyzer.

Key features:

  • Intuitive no-code interface that makes machine learning accessible to non-technical users.
  • Easily connect machine learning tools to your data, through point-and-click integrations or via the API.
  • Get the Google Sheets add-on to analyze data directly in your spreadsheets.
  • Create visualizations from your data and get greater granularity with MonkeyLearn Studio.

2. Knime

Knime is an open-source machine learning platform for data mining. The visual workflow editor has ML capabilities and allows you to create and execute data science workflows. You can use it to build services and applications, for predictive analytics, or to analyze customer or financial data.

Key features:

  • Scalable, modular components.
  • Easy-to-use drag and drop interface, no coding needed.
  • Constantly updated with new integrations. You can seamlessly integrate with open-source projects such as Keras, R, Apache, and Python.
  • Hundreds of ready-to-run workflows and components available at the Knime Hub.

3. Accord.Net

Accord.Net is a .NET machine learning framework written in #C language. It contains multiple libraries for machine learning, statistics, pattern recognition, and even audio and image processing. It’s popular for building applications related to computer vision and image categorization.

Key features:

  • Free, even for commercial use.
  • Ready-to-use algorithms, from kernel functions to hypothesis tests.
  • Extensive documentation and Wiki page to help you get started.
  • A variety of sample applications that you can use to start quickly.

4. Scikit-Learn

Scikit-Learn is a free and versatile library for machine learning in Python. You can find supervised and unsupervised algorithms for classification, regression, and clustering, among others. It’s suitable for academic or commercial use.

Key features:

  • Ease-of-use, highly recommended for programming beginners.
  • An exhaustive collection of robust algorithms for supervised learning: support vector machines, naive bayes, decision trees, and more.
  • Many code examples that you can download.
  • Built on top of other Python packages, like SciPy and NumPy, which you have to install first.

5. TensorFlow

TensorFlow is a powerful open-source machine learning & deep learning library, created by the team at Google Brain. It hosts a wide array of tools and resources to help you build and train advanced ML models. Currently, it’s the most trending library for machine learning, with many big companies using it in their operations and for research.

Key features:

  • A large ecosystem, with a range of modules, services, and components for building end-to-end applications.
  • Ideal for working with large datasets and complex deep neural networks projects.
  • A Python API that’s easy to read and use. Also available in other languages.
  • An excellent visualization tool (TensorBoard), to help you track your process while building a model.

6. Weka

Weka is an open-source machine learning tool written in Java. It supports a variety of text and data analysis tasks, such as classification, regression, and data preprocessing. It also includes visualization tools, and is often used for teaching or research.

Key features:

  • No-code tools to build machine learning models without programming.
  • A very easy learning curve and simple GUI (graphical user interface).
  • A wide collection of algorithms for machine learning and pre-built models.
  • It can be easily integrated with other data science tools, such as R or Scikit-Learn.

7. PyTorch

PyTorch is an open-source framework for deep learning, developed by Facebook AI. It’s written in Python (but also available in C++) and provides a tool package for building deep learning projects. It’s the leading framework in research, especially for Natural Language Processing (NLP) and computer vision applications.

Key features:

  • Super fast and flexible. It integrates smoothly with other libraries within the Python ecosystem.
  • A simple and well-designed API.
  • It’s easy to scale and supported by all major cloud platforms, like AWS or Google Cloud Platform.
  • It has a short learning curve compared to other frameworks. Plus, there’s plenty of documentation & in-depth tutorials, and an active community of developers.

8. Keras

Keras is an open-source deep learning library built on top of TensorFlow. It provides an intuitive interface for artificial neural networks, and takes care of the mathematical and computational aspects to facilitate the process of building a model. It’s considered state-of-the-art in research and it’s used by many scientific organizations.

Key features:

  • A high-level library, which is intuitive and user-friendly. Great for beginners.
  • Modular structure that resembles Lego blocks. This makes it easy to understand and allows for fast prototyping.
  • Ideal for innovative research, because it reduces the effort of building deep learning models.

9. RapidMiner

RapidMiner is a data science platform to help analytics teams create predictive machine learning models. It provides a collection of tools and products that companies can use for machine learning and data analytics. There’s a free version available.

Key features:

  • A simple and intuitive drag-and-drop graphical interface.
  • Accessible to a wide range of users, from non-experts to data scientists.
  • More than 1,500 algorithms for creating machine learning models.
  • Pre-built templates for specific use cases, like fraud detection or customer churn.

10. Google Cloud ML Engine

The Google Cloud ML Engine is a cloud-based AI platform, used to create and run machine learning projects at scale. It’s aimed at developers and data scientists. You can easily build and train custom models using its tool AutoML, or deploy machine learning models which have been created externally.

Key features:

  • State-of-the-art Google’s Search technology.
  • It supports Python frameworks such as Tensorflow, Scikit-Learn, and Keras.
  • Fully integrated with all Google Cloud’s services.

11. Azure Machine Learning Studio

Azure Machine Learning Studio is a cloud-based environment that provides low-code and no-code tools for building machine learning tools. This platform allows data scientists to experiment with different algorithms, create workflows, and visualize the results along the way.

Key features:

  • Drag-and-drop interface that makes it simple to create machine learning projects with no code.
  • Pre-built models that you can upload into your workspace and adapt to your needs.
  • You can publish machine learning models as a web service and use them from any device.

12. IBM Watson

IBM Watson is a suite of AI tools and solutions to help businesses extract value from their data. Watson Studio allows you to prepare data and build AI models. Then, you can deploy and run your ML models with Watson Machine Learning.

Key features:

  • A variety of powerful APIs for text analysis, speech recognition, and visual recognition.
  • Integrations with open-source libraries and frameworks.
  • Automate the process of creating models with AutoAI (no coding needed).
  • Large portfolio of use cases and tailored solutions for specific industries.

13. Apache Mahout

Apache Mahout is an open-source machine learning software created by Apache. It runs on top of Hadoop Map Reduce, works well with huge data sets, and combines three machine learning techniques: classification, recommendation, and clustering. Many big companies, from Yahoo to LinkedIn, use Apache Mahout.

Key features:

  • Creates highly scalable machine learning applications.
  • A large and very active community.
  • A wide variety of pre-built algorithms for Apache Flink, Apache Spark, and H2O.

14. Apache Spark

Apache Spark is an open-source distributed framework for big data processing and machine learning. One of the things that makes it stand out from its competitors is that it processes data in-memory, which makes it perfect for working with huge datasets and delivering real-time analytics.

Key features:

  • Its own machine learning library: MLib.
  • Lightning fast performance (it runs 10 to 100 faster than other alternatives)
  • Easy-to-use APIs supporting common programming languages.
  • It’s the largest open-source project in data processing. It has a huge community of developers.

15. Amazon Machine Learning (AML)

Amazon Machine Learning (AML) is a cloud-based set of machine learning services within the AWS environment. This includes Amazon SageMaker ‒ which allows data scientists to build, train, and deploy machine learning models ‒ as well as pre-trained models to use in your applications.

Key features:

  • A broad number of pre-trained models (no machine learning expertise needed) for text analysis, real-time translation, document analysis, personalized recommendation, and more.
  • It supports all major machine learning frameworks (like Keras, Tensorflow, or PyTorch).
  • A collection of pre-built algorithms you can choose for different use cases.

16. Google Colab

Google Colab allows you to write and run Python code in an interactive environment called a Colab notebook. You can easily execute and write code, share your Colab notebooks with co-workers or friends, and allow them to comment or even edit them. Colab notebooks are Jupyter notebooks that are hosted by Colab. It’s used extensively by the machine learning community, to import datasets, train classifiers, and evaluate models.

Key features:

  • No setup required: Colab runs on Google’s servers.
  • Free GPUs (Graphic Processing Units) and TPUs (Tensor Processing Units) for deep learning.
  • Includes all the main libraries for machine learning, data science, and deep learning.
  • Store your notebook in Google Drive, save a copy in GitHub, and easily share your work with others.

Final Note on ML Tools

Machine learning tools will make your business smarter by quickly sorting data and delivering actionable insights. There are many options available, but the quickest way to get started is with SaaS machine learning tools.

Ready-to-use machine learning software, like MonkeyLearn, is perfect for businesses looking for a quick setup, customized solutions, and a powerful suite of machine learning models. Want to know more about how machine learning tools can solve your problems?

Request a free demo, personalized for your use case, or check out MonkeyLearn’s plans and pricing.

Inés Roldós

November 2nd, 2020

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