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.
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.
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.