8 Machine Learning Tools to Watch Out for in 2020

In this article, we will discuss the top 8 machine learning tools to watch out for in 2021.

Machine learning is an outstanding technology in today’s digital era. With the ever-growing data generation and its applications, the demand for new machine learning models is also increasing. With this, the need for AI/ML engineers is also snowballing. Suppose you are interested in data science, machine learning, and artificial intelligence and want to pursue a career in this domain; you can go with these machine learning and data science courses or AI and ML training.

Machine learning is getting more fame as ML applications are dominating almost every sector. The swift adoption of this technology in businesses is helping create plenty of job opportunities in this field. However, to make a career in this field, you must be well aware of some of the best machine learning tools. With the help of these tools, you can create excellent ML applications.

Here are the top 8 machine learning tools to watch out for in 2021-


Scikit-Learn is an open-source machine learning library for Python. It is a unified platform as it is used for multiple purposes such as regression, classification, dimensionality reduction, and data preprocessing. It is built upon three popular Python libraries, Numpy, Pandas, and Matplotlib.

Scikit-Learn was initially released in 2007, and the original author was David Cournapeau. The latest version is 0.24.2, and it was released on April 28, 2021. It is written in Python, C, and C++. Along with the functionalities mentioned above, you can test and train your model by using Scikit-Learn, said John who has founded 3 company for jobs in USA.


TensorFlow is an end-to-end open-source machine learning library. It has a comprehensive ecosystem of tools, libraries, and community resources that lets machine learning scientists quickly build and deploy ML applications.

TensorFlow is a symbolic math library based on data flow and differentiable programming. It was developed by the Google Brain team for internal Google use. It can be used for various kinds of tasks but focuses on training deep neural networks, also check this site calismileorthodontics.com.


KNIME, the Konstanz Information Miner, is a free and open-source machine learning library based on GUI. It integrates different applications for machine learning and data mining through its modular data pipeline. It is generally used for data relevant purposes such as data manipulation, data mining, etc.

The best thing about KNIME is that it doesn’t require programming knowledge. Therefore, it is a handy machine learning tool for beginners. The platform is built for powerful analytics on a GUI-based workflow. It is extensively used in pharmaceutical research, customer data analysis, business intelligence, text mining, and financial data analysis.


PyTorch is an open-source machine learning tool based on the Torch library. It is developed by Facebook’s AI Research lab (FAIR), primarily used for areas like computer vision and natural language processing. PyTorch is very fast and flexible to use as it has a good command over the GPU.

PyTorch is one of the most important machine learning tools as it is used for building neural networks and tensor calculations. Some of the most popular deep learning software built on the top of PyTorch are Tesla Autopilot, Uber’s Pyro, HuggingFace’s Transformers, PyTorch Lightning, and Catalyst.


Waikato Environment for Knowledge Analysis (WEKA) is a free machine learning tool developed by the University of Waikato, New Zealand. It contains a collection of visualization tools and algorithms for data analysis and predictive modeling together with a GUI for easy access to these functions. With the help of WEKA, one can access other machine learning tools such as Scikit-Learn, R, etc.

Google Cloud AutoML

Google Cloud AutoML allows developers with limited machine learning expertise to instruct high-quality models specific to their business needs. AutoML leverages Google’s state-of-the-art transfer learning and Neural Architecture Search (NAS) technology.

Google Cloud AutoML has become very much popular among businesses. Companies want to use AI and ML in every sector, but they have been facing difficulties due to a lack of a skilled workforce in the market. With the help of Google AutoML, they can easily apply AI and ML in their businesses.


Accord.NET is a cross-platform machine learning framework for scientific computing in DOT NET. It comes with an image and an audio package that assists in training the models and creating interactive applications such as computer vision, audition, etc.

The framework covers numerical linear algebra, numerical optimization, statistics, machine learning, artificial neural networks, signal and image processing, graph plotting, and visualization.

IBM Watson

IBM Watson is a web interface capable of building, deploying, monitoring, and updating AI models with various tools from open source code to automatic code generators. It is a questioning and answering system that is based on natural language processing. Watson is generally used in automated learning, information extraction, research, and testing purposes. The objective of IBM Watson is to offer a human-like experience to its users.


In this article, we have discussed some of the most popular machine learning tools to watch out for in 2021. Most of these tools are free and easy to use. The selection of these machine learning libraries depends on your requirement and your expertise level.

Frequently Asked Questions (FAQs)

Q. What are AI and ML?

Ans: Artificial intelligence is the ability of a machine to learn from its experience and adjust to new inputs. AI-based systems can perform tasks commonly associated with intelligent beings. Machine learning is a sub-domain of AI that allows systems to automatically learn and improve from past experience without being explicitly programmed.

Q. How ML tools help in building AI applications?

Ans: With the help of machine learning tools, we can train and improve our model without being expressly customized to do as such. This is particularly appropriate for applications that use unstructured information, for example, pictures and content.

Q. What are some of the most popular ML tools to watch out for in 2021?

Ans: Most popular ML tools to watch out for in 2021 are listed below:

  •          Scikit-Learn
  •          TensorFlow
  •          KNIME
  •          PyTorch
  •          WEKA
  •          Google Cloud AutoML
  •          Accord.NET
  •          IBM Watson

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