Top 11 Machine Learning Software – Learn before you regret!
Machine Learning Software – An important and latest blog in DataFlair’s Machine Learning Tutorial Series. The objective of this blog is to provide you with several softwares that will allow you to implement machine learning algorithms with ease. We will discuss software and tools that facilitate both rapid prototyping as well as an added functionality to other languages in the form of tools. While there are umpteen software services available for developing machine learning solutions, we selected the ones that have been making their name in this industry.
Don't become Obsolete & get a Pink Slip
Follow DataFlair on Google News & Stay ahead of the game
11 Machine Learning Softwares
Machine Learning has emerged as the most important technology of the 21st century. With so many prolific algorithms that can be used for designing machine learning solutions, we will take a look at some of the highly popular software solutions that you can use for building your very own machine learning model.
The standard name for Machine Learning in the Data Science industry is TensorFlow. It facilitates building of both statistical Machine Learning solutions as well as deep learning through its extensive interface of CUDA GPUs. The most basic data type of TensorFlow is a tensor which is a multi-dimensional array.
It is an open-source toolkit that can be used for build machine learning pipelines so that you can build scalable systems to process data. It provides support and functions for various applications of ML such as Computer Vision, NLP and Reinforcement Learning. TensorFlow is one of the must-know tools of Machine Learning for beginners.
Explore the top Machine Learning Tools to upskill yourself
Shogun is a popular, open-source machine learning software. It is also written in C++. It supports various languages like Python, R, Scala, C#, Ruby etc. Some of the algorithms supported by Shogun are –
- Support Vector Machines
- Dimensionality Reduction
- Clustering Algorithms
- Hidden Markov Models
- Linear Discriminant Analysis
3. Apache Mahout
Apache Mahout is an open-source Machine Learning focused on collaborative filtering as well as classification. These implementations are an extension of the Apache Hadoop Platform. While it is still in progress, the number of algorithms that are supported by it have been growing significantly. Since it is implemented on top of Hadoop, it makes use of the Map/Reduce paradigms. Some of the unique features of Apache Mahout are –
- It provides expressive Scala DSL and a distributed linear algebra framework for deep learning computations
- It provides native solvers for CPUs, GPUs as well as CUDA accelerators.
4. Apache Spark MLlib
Spark is a powerful data streaming platform and on top of that, it provides several advanced machine learning features through its MLlib. It provides a scalable machine learning platform with its several APIs that allow users to implement machine learning on real-time data.
With MLlib, you can easily integrate any Hadoop source to work seamlessly by applying machine learning algorithms with ease. With Spark, you can perform iterative computation through which you can achieve better results for your algorithms. Some of the algorithms supported by MLlib are as follows –
- Classification, Naive Bayes, Logistic Regression
- Regression – Linear, Survival Analysis
- Gradient Boosting, LDA, Topic Modeling
- Decision Trees, Random Forests, etc.
It is the right time to learn everything about Apache Spark for FREE!
5. Oryx 2
Oryx 2 makes use of Lambda Architecture for real-time and large scale machine learning processing. This model was built on top of the Apache Spark architecture that involves packaged functions for building rapid-prototyping and applications. It facilitates end to end model development for collaborative filtering, classification, regression as well as clustering operations.
Oryx 2 comprises the following three tiers.
- The first tier is of a generic lambda tier that provides speed and serving layers that are not specific to Machine Learning procedures.
- The second specialization provides ML abstractions for selecting the hyperparameters.
- It provides an end-to-end implementation of the ML applications in its third tier.
H20’s deep learning platform provides a scalable multi-layer artificial neural network. This ANN comprises of several components and parameters which can be altered accordingly based on the data provided. It also comprises of rate annealing and adaptive learning rate to yield highly predictive output. The native H20-3 only supports the standard feedforward neural network. Other versions of H20 also support Convolutional Neural Networks and Recurrent Neural Networks.
Don’t forget to check DataFlair’s latest tutorial on Artificial Neural Networks
Developed by Facebook, Pytorch provides an advanced deep learning framework. The important features of Pytorch are Deep Neural Networks and Tensors.
With Pytorch, you can develop rapid prototyping for research. Furthermore, you can build software pipelines using Pytorch. Uber’s very own probabilistic programming language is built with Pytorch. Using it, you can develop dynamic graphs to accelerate your machine learning processes. PyTorch also gives your code the ability of data parallelism
RapidMiner provides an integrated and comprehensive environment for carrying out several tasks like data preparation, machine learning, deep learning, text mining as well as predictive analytics. It is popular for its lightning-fast speed to drive revenue, reduce costs and avoid risks.
One of its most essential features is its GUI based drag and drop feature that allows the users to intuitively build data processing workflows that can be selected from over 2000 available nodes. Apart from building machine learning models, one can also optimize the model performance through bagging, boosting and building the model ensembles.
Weka stands for Waikato Environment for Knowledge Analysis. It is a machine learning software that is written in Java. It comprises of several machine learning algorithms can be deployed and are ready for use. These algorithms are mostly used for data mining. Some of these tools are classification, clustering, regression, visualization as well as data preparation.
Weka is an open-source GUI interface that allows easy implementation of machine learning algorithms with minimal programming lines. We can perform the functioning of machine learning on the data without writing any line of code. Therefore, this software is ideal for freshers in machine learning.
KNIME or Konstanz Information Miner is an open-source data analytics, reporting as well as an integration platform. With the help of KNIME, one can carry out the various components of machine learning and data mining. It is intuitive and is constantly integrating new development features to it. It assists the users in understanding the data and designing the data science workflows using reusable components that are accessible to all.
Knime makes use of a modular data pipelining concept. With the help of GUI and JDBC, it can blend several data sources to carry out data modeling, analysis, and visualization without the need for extensive programming.
Keras is an open-source neural network library that provides support for Python. It is popular for its modularity, speed, and ease of use. Therefore, it can be used for fast experimentation as well as rapid prototyping. It provides support for the implementation of Convolutional Neural Networks, Recurrent Neural Networks as well as both. It is capable of running seamlessly on the CPU and GPU. Compared to more widely popular libraries like TensorFlow and Pytorch, Keras provides user-friendliness that allows the users to readily implement neural networks without dwelling over the technical jargon.
In this article, we went through some of the popular machine learning software that can be used for developing models from scratch as well as for rapid prototyping. We went through some of the popular softwares through which you can build vast arrays of software applications.
Enjoyed the article? Share feedback with us through comments.