8 Popular Machine Learning Frameworks and Tools

8 Popular Machine Learning Frameworks and Tools

8 Popular Machine Learning Frameworks and Tools

Before going through the list of popular Machine Learning frameworks, let’s understand why it is important to embrace ML skills.

We all know that Machine Learning and Artificial Intelligence are the hot trends in Technology.

According to Gardner, these Technologies will create 2.3 million jobs in the future.

It has been seen that there are lots of tools and Frameworks that help us to easily implement Machine learning in our application.

So let’s have a quick introduction to Machine Learning.

machine learning

What is Machine Learning aka ML?

It is a type of Artificial Intelligence that makes Software applications learn from data and predict outcomes without human intervention.

This helps Machines to learn from Examples and Experiences without being explicitly programmed.

This is a quick explanation of Machine Learning. I would recommend going through a detailed ML introduction if you are new to ML.

Majorly, Python and R programming languages are used for ML. Read the best programming languages for ML for more detail.

Let’s check popular Machine Learning frameworks one by one.

1. Scikit Learn

This is one of the Python libraries for Machine learning as per the list curated by Aniruddha Chaudhari.

Scikit Learn is a free software Python library and one of the most popular ones used by beginners.

It is built using Numpy (Numpy tutorial), Scipy, and Matplotlib. It is the Simplest tool used for data analysis, data mining, and data cleaning.

Usage: This provides a range of Supervised and Unsupervised learning algorithms like clustering, linear regression, classification and much more.

This is one of the most important Python libraries that everyone should know as it forms a building block of any basic ML project.

2. KNIME(Konstanz Information Miner)

It’s a free open source data analytics, reporting, and integration tool with features like powerful analytics.

Usage: It’s used to gather insights about the data. You can perform several operations like Data Gathering, Data wrangling, Creating a Model, Visualizing it, and Optimizing the model.

Advantage: The most important advantage is that it has a GUI-based workflow so no need to have prior coding experience.

It helps us to manage or work with all the processes in a single process. It is basically known as an all-in-one package.

3. Tensorflow

It is one of the most popular libraries that is extensively used in ML, AI, and DL.

This library is excellent as it is created by the Google brain team and is open source.

Usage: It is mostly used for numerical computing and large-scale machine learning.

Advantages: The code is easy to understand and highly flexible as it deals with Keras. It is very easy to process unstructured data like images, video, and audio as it is very easy to create neural networks in TensorFlow.

This library works well with both CPUs and GPUs.

Tensorflow is available in different forms for different platforms.

  • Tensorflow JS is used to implement Machine Learning and Deep Learning in Web Applications.
  • Tensorflow Lite is used to implementing Computer vision in Mobile applications.

4. WEKA (The University of Waikato)

This is an open-source Java framework that consists of a collection of Machine Learning Algorithms for data mining and data exploration tasks.

It is one of the most powerful Machines Learning tools for understanding and visualizing Machine learning algorithms on your local machine with Graphical as well as a command-line interface.

Disadvantage: The disadvantage is the lack of documentation and online support. It’s basically an environment for comparing learning algorithms.

5. Pytorch

Pytorch is one of the biggest rivals of Tensorflow. It is built using Python and extensively used to build deep learning applications and deploy them.

This library is now owned and maintained by Facebook.

Usage: It is extensively used in Facial recognition and image classification applications on Facebook.

This framework is embedded with ports to Android and iOS backends.

Advantages: The dynamic computational graph and faster performance are the most important advantage of this library.

6. RapidMiner

This is one of the main tools used in Data science projects. This tool has a robust graphical user interface and is used by teams for data preparation, machine learning, and predictive model deployment.

This platform also enables uses to create, maintain, and deliver productive analytics.  It also helps us to structure data and organize it and help teams.

Disadvantage: One of the disadvantages is that it is a costly tool.

7. Google AutoML

This is available on the Google cloud platform.

Google cloud auto ML makes the power of Machine learning available to those who have limited knowledge of machine learning.

It also ensures whether our Model is properly built. They have various products for different purposes.

Advantage: They have the Auto ML vision for images, Video intelligence, Auto ML NLP and translator and much more. This set of high-quality tools makes machine learning easier to implement.

8. Azure Machine Learning Studio

Azure Machine Learning Studio is from Microsoft. And it is a collaborative drag and drops ML tool.

It is used to build, test, and deploy predictive analytics solutions.

We can easily drag and drop our datasets and analysis modules on the interactive canvas and run the model.

It’s very easy to build and visualize models using this platform.

What next?

These are the top popular machine learning frameworks and tools you should learn. As these tools are endorsed by giant companies like Google, Facebook; learning these skills will be very useful looking at future job opportunities.

Machine learning is one of the types of Artificial intelligence. There are a lot of pros and cons of AI. These tools should be controlled and used by the right people.

If you look into the above ML tools, Python is the most suitable programming language for ML. Improve your Python coding skills. This is the first step you should start with if you want to excel in your ML skills.

What are other ML tools you would like to recommend? Let me know in the comment.


  1. I appreciate your work. Thanks, Author! I started with Machine Learning. I’m trying to learn it by myself. Which programming language I should focus on, first?

  2. I learned Python in my school. I will start learning Machine Learning. I was searching for good resources and thanks for writing this.

  3. I’m a college student and I want to learn Machine Learning. I only know C/C++. Do you know any good books I can refer to?

    1. Cool Apeksha.
      Don’t know about books but I can tell you a complete path to become an ML professional with online resources.
      1) Khan Academy: Learn some maths(integration and differentiation)
      2) CSEstack.org or online courses: Learn Python
      3) Follow Andrew NG course on ML
      4) Do projects

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