Top 51 Machine Learning Tools
In order to use any system effectively, you first need to know better about its tools. The same is true for learning Machine Learning. Once you know about the tools used to deploy Machine Learning models, the built system would turn out to be powerful. As far as the scope of Machine Learning expands, the range of tools and softwares also increases. And if you know how to use these correctly, you can develop new technologies with the help of the existing models and these tools.
This PythonGeeks article will assist you in knowing about the various tools to train and deploy any Machine Learning model. Though the arena of the tools that can be used for the fabrication of the algorithms is quite extensive, this article will guide you through the top 51 tools to get your work done. These tools make it affluent for you to deploy complex models with the advancement of technology.
Top 51 Machine Learning Tools
Let us look at the most used Machine Learning tools world-wide. We will also discuss some of the lesser-known tools which could be of great use to you.
1. TensorFlow
TensorFlow is the most used name in the arena of Machine Learning. It is an open-source platform for numerical and large-scale machine learning data-sets. It is capable of deploying both machine learning and neural network models.
TensorFlow is mainly used for its advanced Python framework. You can operate it on CPU as well as GPU. It is a reliable source for deploying neural network models.
Various models such as Natural Language Processing (NLP), Image Classification and others use this framework for accuracy.
2. PyTorch
PyTorch is an extensive Deep Learning platform. It proves to be a reliable framework when operated on GPU. This makes the framework quite flexible and speedy.
Pytorch is an effective tool when it comes to deployment of tensor calculation and deep neural network models. It is a completely Python based platform which can prove to be a reliable substitute for NumPy. Since it is a less exploited platform, it has great scope of development in the future.
3. Shogun
Written in C++, this is an open-source platform which provides an interface for languages like octave, Python, R, etc. It is an excellent platform for the implementation of algorithms like Support Vector Machine (SVM), Dimensionality Reduction Algorithms like PCA, Clustering Algorithms like kNN.
4. Sci-Kit Learn
Known to be a unified platform for its users, Sci-Kit Learn is an open-source framework for Machine Learning. The platform is integrated with the help of three main libraries, namely, NumPy, SciPy and MatplotLib.
This framework is a great tool to train and test your models as well as implementation of various algorithms like regression, classification and clustering.
5. Apache Mahout
Extensively used for data mining and ML, this is a Hadoop based open-source platform. You can also use it for implementing algorithms like classification, regression, association and clustering.
The major names in the field of Machine Learning like Google, Twitter, Yahoo use this platform.
6. Accord.Net
Being a computational framework, Accord.Net is usually used for audio and image processing. Applications like computer vision and audition models could be trained and deployed using this platform.
Since it is a .Net framework, it’s base library is C#. The extensive libraries help you in the audio and video processing.
7. KNIME
Knime is a GUI-based open-source machine learning framework. The main feature of this tool is that you don’t need extensive coding skills to operate it. The workflow of the tool is quite simple.
It processes data by the means of various workflows. Its repositories consist of several nodes. These nodes can then be deployed into the Knime Portal for the execution of the workflow.
8. Weka
Used in teaching and research, Weka is a GUI-based open-source platform. The platform consists of Sci-Kit Learn, R,etc.
9. Amazon Machine Learning
Amazon Machine Learning is a powerful tool provided by Amazon for Machine Learning model training. It provides various services like sagemaker, redshift, etc. Right now, it is the most powerful tool in the field of research programs.
10. Vscode
Visual Studio Code, more commonly referred to as VScode, is a framework provided by Microsoft. It has a close integration with Azure ML framework. It is an enterprise used library.
11. Rstudio
Rstudio is an IDE used along with R. It is one of the most dominant interfaces for R programming. Since it is used in R, it is largely related to data analytics and data queries.
12. Jupyter Notebook
Jupyter is the most reliable name in the field of Machine Learning. It is trusted for being efficient and speedy. It supports three of the major programming languages, namely, Julia, Python and R.
Google Colab Environment makes use of Jupyter Notebooks. We can also access it through GUIs like anaconda navigator, winpython navigator, etc.
13. Rapid Miner
Rapid Miner is very useful for non-programmers. It is a very powerful data science platform. It is a very user-friendly framework that works cross-platform.
14. Azure Machine Learning Studio
Created by Microsoft, Azure Machine Learning Studio is an attempt to provide ML services to the users. It can work both on CPU and GPU. This tool is not as famous as Google ML, but is quite handy and reliable for the users.
15. Google Cloud AutoML
This framework from Google, provides pre-trained models for various services. It proves to be a great platform for people from non-coding as well as coding background to test and train their Machine Learning Models.
16. Google Colab
Based on Jupyter Notebooks, Google has provided this tool for Machine Learning training. You can work with various Python libraries and frameworks like TensorFLow, Keras, Pytorch. The storage queries here are handled using Google Drive. For extra processing, you can use it alongside the GPU.
17. Glueviz
If you are looking for an interface that provides graphical and pictorial facilities, Glueviz is the answer for your queries. You can use Glueviz to represent your data in a graphical form. In this framework, a Python-based library called Glue is used. This tool is available as an online web interface or could be accessed with a GUI application called Anaconda Navigator.
18. Torch 7
Based on Lua, torch is a ML library for scientific purposes. It is mainly used for Deep Learning. Torch 7 was the most used version before the use of Pytorch.
19. Open NN
Open NN is a library used extensively for neural networks. It can also be used for operating data mining functions that are used for predictive analysis. It is written in major programming languages like C++.
20. Theano
Theano is very much like TensorFlow. It is used for mathematical calculations like vector mathematics and matrix operations. The only difference between TensorFlow and Theano is that TensorFlow uses C++ as well as Python whereas Theano is completely Python-based.
21. Apache Spark MLlib
MLlib is an important software of Apache Spark. It is predominantly used for algorithms like regression, clustering, filtering, etc. The key features of this software are speed and efficiency.
22. Pylearn2
Pylearn2 is an efficient ML framework that works alongside Theano. Besides being able to run on both CPU and GPU, it is chiefly used for mathematical modeling. It is still an emerging technology in the market.
23. LIBVSM
LIBVSM is one of the lesser known softwares in the field of ML. It works for Sequential minimal optimization. It works for kernel SVM.
LIBVSM is a proficient software for problems related to regression and clustering. It works finest with C++.
24. LIBNEAR
LIBNEAR is another tool apart from LIBVSM. It differs from LIBVSM as it uses a coordinate descent algorithm. It works for algorithms such as linear SVMs and logistic regression. You can also use this for L-2 regularization.
25. IBM Watson
Watson is a question and answer, human interactive software based on Natural Language Processing (NLP). IBM provides a web-interface for operating Watson. Major industries which make use of NLP use Watson on a large scale. It aims to provide you with a human-like environment and you can use it for learning and research purposes.
26. Orange 3
Orange3 is a data mining software and it is the current version for the Orange software. You can use Orange through anaconda navigator for visualization, pre-processing and other mathematical modeling. It is a great tool if you are well-versed with Python and UI.
27. Python NLTK
Python NLTK is a Python library which you can use for Natural Language Processing (NLP). NLP is widely used to make human-interactive interfaces. NLTK provides numerous functions such as word count, character count and others which you can use to build an effective NLP model.
28. VowPal Wabbit
Wabbit is currently under research for further development. Earlier it was used for learning algorithms and developing Machine Learning Models. It was previously sponsored by Yahoo. Its current sponsor is Microsoft. Wabbit is primarily known for its speedy learning techniques and algorithms.
29. Caffe
Written in Python, Caffe primarily uses C++ as its base language. Caffe is widely used in the industrial domain for its deep learning libraries. It proves to be a great tool if you are dealing with image classification and segmentation models. It can work effortlessly on both CPU and GPU. Caffe2 is currently under development and will also include neural network models.
30. MatPlotLib
MatPlotLib is a data visualization library provided by Python. It is mainly used for plotting histograms, bar graphs and pie charts. It is similar to the glue library of Glueviz. This works predominantly with NumPy arrays.
The major difference between this and Glueviz is that Glue is used for 3-D data visualization whereas MatPlotLib works with 2-D visualization. It works effectively on larger datasets as well.
31. NumPy
NumPy is a Python library, used primarily for data science models. Much advanced tools like TensorFlow and Theano are based on NumPy. This is a quite handy tool for mathematically modeling. It is very useful for generic data as well as large databases.
32. Pandas
Like NumPy, Pandas is also a Python based library, used primarily for Machine Learning modeling. It is used for data manipulation and data science equations. It provides efficient data structures that are quite resourceful for data calculations. Till date, it is considered the most advanced and effective data manipulation tool.
33. H2O
H2O is an open-source platform used for statistical Machine Learning algorithms. It is also capable of handling AutoML models. Written in Java, it is a very interactive platform for various other programming languages.
The latest version of H2O is H2O-3 and it is closely integrated with advanced frameworks like Hadoop.
34. WinPython
Like Anaconda, WinPython is an open-source distribution of Python. Unlike Anaconda it does not have an interactive GUI for users. It also delivers Jupyter and Spyder notebooks. It is quite light-weight and is easily installed as compared to Anaconda.
35. MLPY
Built on Numpy and SciPy libraries, MLPY is a Python package used for training Machine Learning models. It is quite effective for both supervised and unsupervised algorithms. You can use it with Python3 as well as Python2. It is quite versatile in terms of algorithms. Some of the algorithms that are implemented effectively with MLPY are regression, clustering and dimensionality reduction.
36. DeepLearning4j
Written in Java, it is predominantly used for Deep Learning.
DeepLearning4j has a close integration with Hadoop and Apache Spark. It is helpful for training models for business analytics. It has the capability to work on both CPU and GPU.
37. SciPy
Having NumPy functionality, it is a Python-based library for Machine Learning. It is a very useful tool for mathematical calculations like calculus and algebra.
The major reason to choose SciPy over NumPy is that Scipy is quite speedy and accurate when it comes to handling higher order mathematical calculations for larger data sets.
38. Genism
The major use of Genism is Natural Language Processing (NLP). It is a Python-based library and has various other tools for Machine Learning query handling. It is quite useful for information extraction and retrieval. Therefore, it is a resourceful tool for mathematical as well as scientific modeling. The major advantage of using Genism over other tools is that you can use it to handle large datasets.
39. Pattern
Latest version of Pattern is Pattern3. It is installed through the Python package installer. It is a useful tool for NLP and data manipulation. Pattern is quite resourceful when it comes to handling queries for vector space models like SVM.
Though it is installed through the package installer in Python, it is not supported with Python3.
40. Cuda Convent
The name Cuda-Convent is derived from the fact that it is a Cuda implementation for Convolutional Neural Networking. It predominantly operates on GPU. It is a C++ based package. The latest version of this tool is Cuda-convent 2. The major feature of Cuda-Convent is that it is very speedy when used on GPU.
41. Mallet
Used majorly for NLP, Mallet is a Java- based framework. Mallet stands for Machine Learning for language Toolkit. It provides various features like information extraction and entity extraction from texts. It is very effective when you have to extract a specific dataset of the abundant dataset.
42. Numba
Like SciPy, Numba is also a Python-based library which is integrated with the NumPy module. It is used to speed up Python applications since it accelerates the Python processing. Its major advantage is that it is speedy over tools. It converts Python code into Machine Level language through LLVM compiler.
43. Dask
Dask consists of two main components namely, Dynamic task scheduling and big data collections. It is a Python based framework and it is very effective if you want to operate parallel computing. Dask is speedy, steadfast and quite adaptable. It is resourceful when it comes to handling multi-dimensional data analysis.
44. Lasagne
Supplemented alongside Theano, Lasagne is used for Neural Networking with Thano applications. It allows Input output architecture for networks like CNN, RNN and LSTM. However, it is still under development and is being constantly updated. It is quite handy and works on both CPU and GPU.
45. DLIB
Used predominantly to tackle real-world problems, DLIB is a C++ based Machine Learning tool. It is resourceful when the problems are related to embedded systems, robotics and android applications. It works effectively for image classification, networking and GUI.
46. CUV
CUV is used for mathematical modeling. It is a C++ based framework and works with convolutions and matrix manipulation. Though built in C++, it also has Python extensions. It works with CPU as well as GPU. CUV works swiftly with NVIDIA CUDA and has a really fast prototype.
47. Spyder
Spyder is an IDE used for scientific modeling. It is a Python supported tool which consists of libraries like pandas, SciPy, NumPy and Cython. It has a strong web interface. Like other Python based tools, you can use it through Anaconda Navigator. When you compare this with Jupyter, Spyder is an effective choice when the problem deals with data manipulation.
48. MLPACK
Written in C++, MLPACK is a Machine Learning tool which deals with problems, where speed and flexibility are a major factor. The three major libraries on which MLPACK is based are; Armadillo linear algebra library, Boost library and Ensmallen function optimization library. It is useful for neural networking and it majorly works with RNN.
49. Ramp
Being a Python module, Ramp is used for rapid prototyping. It is quite extensible and is similar to other tools like Sci-Kit Learn. It has the advantage that it can retrieve data from disk which makes it quite handy. The data is stored in HDF5 format on this Machine Learning Toolkit.
50. Open CV
The name of the toolkit is derived from its use i.e., computer vision. It is one of the most operated tools for image-recognition worldwide. It can work effortlessly when integrated with other modules like Numpy. OpenCV is an open-source Python Library used for image and video processing. It supports many features like face recognition, vehicle counting, medical image analysis while the list goes on.
51. Keras
If you are just starting to learn Machine Learning or perhaps are not very efficient in using vast Machine Algorithms, then Keras is the right tool for you. It provides a great API interface. It is usually used alongside Theanos, TensorFlow and CNTK. Keras is a beneficial tool to work with CNN or RNN or both. It is designed in an API format specially to assist human accessibility. It is one of the most widely used toolkits for Machine Learning.
Conclusion
Hence, you have read about the major tools used for Machine Learning algorithms. According to your needs, you can choose the tool which will suit you the best. It will help you to switch your existing tool to achieve better results. Thus, you’ve come across the leading tools which will help you to train the Machine Learning models. Now you don’t have to spin your head around to choose a toolkit from the plethora of options available. All the above-mentioned tools are very resourceful for you if you are interested in Machine Learning development.