Top 51 Machine Learning Tools

Machine Learning is vast. There is a seemingly endless stream of information to learn. It becomes incredibly convenient if you understand how to utilize Machine Learning tools. They let you experiment with data, train your own models, develop your own algorithms, and learn new techniques. One of the most significant advantages of employing them is the time being saved. So, it is very important to ensure the tool(s) in use are well-understood and operating as expected. Let’s learn about these ML Tools:

Top 51 Machine Learning tools

1. Scikit-Learn

Scikit-Learn is a machine learning open-source tool. It also gives users a single platform. Regression, classification, and clustering are all made easier using this platform. This Machine Learning tool may also help you train and test your models.

2. Rapid Miner

Rapid Miner is a platform for data science. It’s a fantastic user interface that’s especially useful for non-programmers. This Machine Learning tool is compatible with a variety of operating systems. It is commonly used in corporations and industries for rapid data and model testing.

3. Google Cloud AutoML

The primary idea of cloud autoML is to make AI more accessible to the general public. It’s also used in enterprises. For many services, Cloud AutoML delivers pre-trained models. These services range from speech recognition to text recognition, among other things.

4. Jupyter Notebook

Jupyter notebook is an excellent tool for Python programming. It runs ML programs and forms the base in Google Colab’s environment. It allows us to use notebooks to store and exchange live code. We may also use a variety of graphical user interfaces to access it.

5. Glueviz

Glueviz is a user interface that gives users with graphical and pictorial capabilities.You may use your data to create a graphical representation of it. You may also use your data to produce 2D and 3D pictures for better analysis.

6. Weka

Weka is a graphical user interface (GUI) for an open-source machine learning technology. It is a user-friendly programme used in both education and research. It also gives you access to a number of additional tools. Scikit-learn, R, and others are among them.

7. Numpy

NumPy is a Python library that offers functionality for substantial, multi-dimensional arrays and matrices, as well as a vast number of high-level mathematical methods to work on these arrays.

8. SciPy

Scipy is a Python library that is built on Python. It also includes Numpy’s functionality. Scipy is capable of carrying out scientific and higher-order mathematical calculations. It can do things like integration and linear algebra calculations.. The speed of scipy is one of the reasons we prefer it over others. It is capable of doing these procedures at a high rate and with ease.

9. Matplotlib

Matplotlib is a 2D charting toolkit and is one of the most popular Python tools for data visualization. Developers use matplotlib to observe how data patterns are represented. The pyplot module in Python allows programmers to specify line style, font properties, and axis format, among multiple other things. It includes histograms, error charts, bar charts, and other graphs and plots for data visualization.

10. OpenNN

OpenNN (short for Open Neural Networks Package) is a neural network implementation software system. It is written in the C++ programming language and lets us download its whole library for free from GitHub or SourceForge.

11. Theano

Theano is a very popular Python package for quickly creating, evaluating, and optimizing multi-dimensional array-based mathematical calculations. It runs efficiently on CPU/GPU. It spots issues during unit testing and verification. Theano is simple enough to be beginner-friendly, but sophisticated enough to be used in scientific research.

12. Lasagne

Lasagne supports Theano. In Theano, it supports and trains neural networks. CNN, RNN, and LSTM networks are all supported by Lasagne. The design of lasagne enables for a large number of I/O. Lasagne, like Theano, works with both CPU and GPU. Lasagne is continuously being researched and is being updated on a regular basis. Its basic tenets include simplicity, lack of abstraction, and so forth.

13. CUV

CUV is a library written in C++. Elementary matrix manipulations and convolutions are included. CUV makes use of both the CPU and the GPU. As a result, it’s employed in rapid prototyping. CUV makes NVIDIA CUDA simple and straightforward to utilize.

14. Ramp

A ramp is a machine learning toolset for fast prototyping. It’s a Python module, after all. The ramp may be simply extended (i.e) it can use packages from other programmes, such as Scikit-learn. Ramp may also save and retrieve data from the hard drive.

15. Shogun

Shogun, an open-source and free-to-use machine learning software library, is easily available to enterprises of all shapes and sizes. Shogun’s solution is completely written in C++. It’s available in a variety of programming languages, including R, Python, Ruby, Scala, and others. This machine learning programme can handle everything from regression and classification to Hidden Markov models.

16. Pylearn2

Pylearn2 is a machine learning library built on top of Theano. As a result, it would perform similarly to Theano.
Pylearn2 is capable of doing mathematical computations. It can run on both the CPU and the GPU. You should be familiar with Theano before working with Pylearn2. Pylearn2 is a wrapper for Scikit-learn-like packages.

17. Vowpal Wabbit

Vowpal Wabbit is a project that seeks to make learning algorithms quicker. After Yahoo, Microsoft is the current sponsor. Wabbit is a relatively rapid and efficient learning method.

18. Caffe

Caffe is a Deep Learning library. It has a lot of flexibility, speed, and adaptability. Caffe was developed at the University of California, Berkeley. Caffe is written in the C++ programming language. The interface was created using Python. It solves picture classification and segmentation tasks.

19. Dask

Dask is a Python package that allows for parallel computation. It is has two parts:

• Dynamic task scheduling that is
• Collections of big data

Dask facilitates multi-dimensional data analysis. When several processes are running at the same time, it is quick, versatile, and dependable.

20. Numba

Numba is a Python-based application. It’s also compatible with NumPy tools. Numba speeds up the execution of Python applications. As a result, Numba can help Python programmes run faster. It converts Python code to machine-level code using the LLVM compiler.

21. TensorFlow

TensorFlow is an open-source software framework for dataflow programming that Google uses for research and production. This, at its core, a machine learning framework. This machine learning technology is new to the market and is rapidly evolving. The ease with which TensorFlow allows developers to visualize neural networks is perhaps the most appealing feature.

22. Mallet

Mallet is a Java-based software that is used for NLP and other data extraction tasks. It stands for Machine Learning for Language Toolkit in its entire form. Mallet gives you a document categorization tool. Mallet also has tools for extracting entities from texts. This implies that you can also extract certain datasets.

23. Pattern

Machine Learning’s pattern is a data and web mining module. It is used in NLP, text data mining, and text analysis, among other applications.

In Python, we can use pip to install the pattern. It aids in the creation of vector space models, SVM, and other similar models. Pattern3 is the most recent version that we have. The pattern is compatible with Python versions greater than 2.5. Python3 does not, however, support it.

24. Machine Learning on Amazon

Amazon has a large number of machine learning technologies, which should come as no surprise. Amazon Machine Learning is a managed service for constructing Machine Learning models and providing predictions, according to the AWS website. It comes with an automated data transformation tool, which makes the machine learning tool even more user-friendly. Amazon also offers additional machine learning tools, such as Amazon SageMaker, a fully-managed platform that makes using machine learning models simple for developers and data scientists.

25. Keras

Keras is one of the most popular Python ML libraries. It is capable of integrating with Tensorflow, CNTK or even Theano. It performs efficiently on both CPU and GPU. Keras is very beginner-friendly and functions incredibly fast.

26. PyCaret

PyCaret is a Python-based low-code machine learning framework that allows you to go from data preparation to model deployment in minutes in your preferred notebook environment. Despite having a small staff, PyCaret allows us to integrate a wide range of machine learning approaches in analytics and operations.

27. H2O

It’s a fairly open-source machine learning platform. H2O supports statistical machine learning techniques. It also contains a few AutoML features. Although it is built in Java, it has interfaces in a variety of languages. H2O offers a variety of options, including sparkling water, h204gpu, and more. H2O-3 is the most recent version. It’s tightly linked with Hadoop and other frameworks.

28. Knime

Knime is a graphical user interface (GUI) based open-source Machine Learning application. It is most commonly used for data-related tasks. Data mining, manipulation, and so on are examples of these. Knime works with data by developing and executing multiple routines.

29. OpenCV

OpenCV is an excellent tool for image processing and computer vision tasks. It is an open-source library that provides support for tasks such as face detection, object tracking, landmark detection, and many more. It supports a variety of programming languages, including Python, Java, and C++.

30. MLlib for Spark

Spark is known for accelerating the scalability and overall performance of big data processing. Spark MLlib also has high-performance algorithms for Hadoop processes.

31. Encog Machine Learning Framework

Encog is a machine learning framework written in Java and C#. SVM, NN, Bayesian Networks, HMM, and evolutionary algorithms are all supported by Encog libraries. Encog began as a research effort and now has almost a thousand Google Scholar citations.

32. MOA

Massive Online Analysis (MOA) offers classification, regression, clustering, and recommendation methods. It also includes libraries for detecting outliers and drift. It’s made to analyze a stream of created information in real time.

33. ADAMS

ADAMS stands for Advanced Data Mining And Machine Learning System, and it is a new and adaptable workflow engine that aims to easily develop and manage real-world processes that are often complicated. It was distributed under the GPLv3 license. Instead of allowing the user to drag and drop operators or “actors” onto a canvas and then manually connect input and output, ADAMS controls data flow in the workflow using a tree-like structure.

34. Deeplearning4j

DeepLearning4j is an open-source Java Programming toolkit. It provides an environment with support for complex deep learning models. It recognizes patterns or emotions in voice, text or music. This can also detect abnormalities in financial transactions and other similar data.

35. ELKI

ELKI (Environment for Developing KDD-Applications) is an acronym for Environment for Developing KDD-Applications. Index-structure is also an open source data mining program built in Java that is supported by Index-structure. It has a huge number of highly customizable algorithm settings and is designed for academics and students. It is a knowledge discovery in databases (KDD) software framework that was created for use in research and education.

36. JavaML

It’s a Java API that contains a set of Java-based machine learning and data mining techniques. It is intended for both software engineers and scientific researchers. There is no graphical user interface, but there are clear interfaces for each sort of algorithm. It is simple compared to other clustering techniques and enables for easy development of new techniques.

37. MLPack

MLPack is developed in C++ and uses the Armadillo linear algebra library, the ensmallen numerical optimization library, and certain components of Boost. It intends to offer quick and adaptable implementations of cutting-edge machine learning methods.

38. Catboost

Yandex created CatBoost, an open-source software package. It provides a framework for Gradient Boosting on Decision Trees library that can solve problems on ranking, classification, and regression. It’s simple to include into a deep learning system.

39. Dlib

Dlib is a contemporary C++ library that includes algorithms and tools for machine learning. It writes complicated C++ software to tackle real-world challenges.

40. DyNet

DyNet is a neural network library written in C++. It was developed by Carnegie Mellon University and runs efficiently on both GPU and CPU. It integrates seamlessly, neural networks that utilize different or changing structures for every instance.

41. ML.NET

For the C# and F# programming languages, ML.NET is a free machine learning library. When combined with NimbusML, it supports Python models as well.

42. Flux

Flux is a Julia-based open-source machine-learning software library and environment. For simple models, it offers a layer-stacking-based interface, and it has a heavy focus on compatibility with other Julia packages.

43. Infer.net

Infer.NET is a machine learning software package for.NET that is free and open source. It does probabilistic scripting and executes Naïve bayes algorithm in graphical models.

44. Boost

Boost is a collection of libraries for the C++ programming language that handles linear algebra, pseudo – random number generation, multithreading, image processing, regular expressions, and unit testing, among other activities and structures. There are 164 different libraries in all.

45. NimbusML

NimbusML seeks to make ML.NET’s features and performance accessible to data science teams who are used to Python. It offers battle-tested, cutting-edge machine learning algorithms, transformations, and components.

46. Orange3

Orange makes tasks like data visualization, preparation, and other data-related tasks easier. It is useful for Python programming and has a visually appealing UI. The most recent version of Orange software, a data-mining tool, is Orange3.

47. Rstudio

DataExplorer is a prominent R package for machine learning that focuses on three key goals:

• exploratory data analysis (EDA)
• feature engineering
• data reporting.

This program automates data exploration for analytic jobs and predictive modeling, allowing users to concentrate on analyzing data and deriving insights. Each variable is scanned and analyzed, and shows results using standard graphical representations.

48. VScode

Vscode, or Visual Studio Code, is a Microsoft framework. It’s tightly linked with the Azure Machine Learning framework. Vscode is a library widely used in the business world. It is generally a source code editor and is built on Node.js.

49. Gensim

Gensim is a Python-based library that is useful in natural language processing. It can also retrieve or extract information. It is based on the numpy and scipy Python libraries. As a result, it is capable of doing both mathematical and scientific tasks.

50. Pytorch

PyTorch is an open source machine learning library based on the Torch library, largely created by Facebook’s AI Research division for applications such as computer vision and natural language processing.

51. Pandas

Pandas is a data manipulation and analysis software package created for the Python computer language. It provides data structures and functions for manipulating numerical tables and time series in particular

Summary

We’ve discussed 51 Machine Learning tools and libraries spread among multiple languages. Although exhaustive, this article cannot even begin to cover the amount of tools available to machine learning practitioners out there, due to how vast ML has become.