Top 11 Machine Learning Software You Must Know

While we all agree that Machine Learning is the stepping stone for future technological development, we also have to make efforts to learn and develop this system. But, for doing this, we first need to understand the basics of how this technology is developed and what tools we need to use. While there are many sources to help you learn the basics, many would agree that going through these resources and understanding them can be quite tiresome. That’s why this article from PythonGeeks would help you to find out the most efficient Software Services that you can use as the building blocks for Machine Learning algorithms.

From all your research about Machine Learning, you’d have concluded one thing till now- Machine Learning is just a set of a vast number of powerful algorithms implemented correctly. Apart from deeply studying these algorithms, one must also learn to implement them correctly to make their learning fruitful.

Though, you can always use any services of your choice, working with the most efficient services would get you to your required result easily as well as conveniently. With that being said, let’s have a look at this curated list from PythonGeeks to learn in detail about the top 11 Software Services for Machine Learning and how to use them efficiently.

Software Used for Machine Learning

1. TensorFlow

One of the most efficient and famous tools in the field of Machine Learning, TensorFlow is an open-source platform for the implementation of Machine Learning Algorithms. Written in major programming languages like C++, Python, CUDA, you can use TensorFlow on all of the major Operating Systems like Windows, MacOS, and Linux Kernel. It provides a wide range of JavaScript libraries for dataflow programming. So, now that you know basic things about TensorFlow, let’s look at some key features that it has to offer.

Features of Tensorflow:

• It helps you in building and training your own models.
• You can also run your pre-built models using TensorFlow.js which happens to be a model converter.
• Apart from being an open-source software which comes with high flexibility, it can run on GPUs, CPUs and other computing platforms.
• It can even perform numerical computations.
• You can also use it in building neural networks.
• The most significant feature of this is that it can also deploy or train the model on any cloud platform making it storage efficient.

Now that you know the features of TensorFLow, quickly take a glance at how it’d be useful to you.

• You can use this in ways, in other words, you can either install it through NPM or you can insert it in the script tags.
• The tool is free for use.
• You can also use it to predict human pose estimation.

2. PyTorch

PyTorch is another great library to implement Machine Learning algorithms. As you could have guessed it from the name itself this tool is Torch-based. Torch, in simple words, could be a Lua-based computing framework used as a scripting language for Machine Learning libraries.

Like TensorFlow, PyTorch is also built from major programming languages like Python, C++, CUDA and you can use it on all the major OS. The major use of PyTorch is to curate a path from research prototyping to production deployment. It is also used for applications like computer vision, natural language processing that’s primarily developed by Facebook’s AI Research Lab. Let us quickly go through the features of this tool to understand it a bit better.

Features of PyTorch:

• Along with the Autograd Module, you can use PyTorch in building efficient neural networks.
• You’ll have a wide range of optimization algorithms for building various networks.
• It also provides distributed training for models and comes pre-equipped with such tools.
• You can also use PyTorch to deploy models on cloud platforms.
• It is an open-source platform and is available for free use from its official website.

3. Shogun

Like previously discussed tools, Shogun is another Software that helps you in Machine Learning Algorithms. Built in C++, it facilitates you with various data structures and algorithms. Shogun provides libraries which are extensively made for research and educational purposes.

Features of Shogun:

• You are not limited to one particular language since it facilitates all the major programming languages for Data Science like Python, Octave, R, Ruby, Java, Scala, and Lua.
• It could be effectively used in the implementation of Hidden Markov models.
• One of the most used algorithms, namely the Support Vector Machine could be implemented with ease using Shogun.
• Apart from being a free open-source software, it is also easy to use, provides good customer support and is effective in handling large data-sets.

4. SciKit Learn

Written in languages like Python, C and C++, SciKit Learn provides various Python libraries which could be effectively used for Machine Learning Development. It could be operated on all the major OS and is effectively used for its Python Libraries. This tool is predominantly used by researchers for implementing algorithms like Classification, Regression, Clustering, Model Selection while the list goes on.

Features of Scikit Learn:

• It provides useful modules for Data Mining and Data Analysis.
• Its documentation is quite easy and understandable.
• You could use it for free. Also, you are allowed to change parameters for any specific algorithm while calling its object.

5. Apache Mahout

Being a cross-platform software, Apache Mahout is a really effective library if your area of concern is Mathematics, Statistics, and other Data Science queries. Built in Java and Scala, it provides an extensive range of libraries for execution of data science algorithms like Preprocessing, Regression, Clustering and others. Its implementations are an extension of Apache Hadoop Platform, which makes use of Map/Reduce paradigms.

Features of Mahout:

• Apart from the various enlisted libraries, it also has Java Libraries for mathematical libraries.
• It comes pre-equipped with the necessary algorithms for the implementation of Mathematical Modeling.
• It is efficient in handling large data-sets, is quite simple to use and extensible.
• This mainly follows the Distributed Linear Algebra framework which makes it effective for mathematical modeling.

6. Accord.Net

Built in C# this Machine Learning tool is quite handy when it comes to handling problems related to Kernel methods, Hypothesis tests, Distribution and Clustering. It is extensively used for processing image and audio. It proves to be an effective framework for pattern recognition, statistical data processing and linear algebra. Also it has a set of extensive libraries like Accord.Math, Accord.MachineLearning and Accord.Statistics.

Features of Accord.Net:

• It comprises more than 40 non-parametric as well as parametric estimation for statistical distribution.
• It includes 35+ hypothesis tests that includes two-way ANOVA tests, Kolmogorov-Smirnov test and much more.
• This is effective for producing computer audition, computer vision, signal processing and many more.
• It consists of 38+ kernel functions.

7. KNIME

Widely used in the field of data analysis, the Konstanz Information Miner, more commonly known as KNIME, is a reporting and integration platform. It follows a unique Modular pipelining, called “Building Blocks of Analysis” concept for machine learning and data mining. It supports all the common database management systems like JDBC, SQL Server, MySQL, PostgreSQL and SQLite. In recent times, KNIME is being used as RPA (robotic process automation).

Features of Knime:

• It effectively integrates the codes of programming languages like C, C++, R, Python, Java, JavaScript.
• It can also be used in the business arena for financial data analysis, CRM with its set of libraries.

8. Apache Spark MLlib

Spark is a much heard tool now-a-days. It is a powerful data streaming service. Apart from that, it also facilitates you with various machine learning algorithms with its MLlib. You can perform iterative computations and achieve better results with the help of Spark. With its libraries, you can easily integrate any of the Hadoop source along with machine learning algorithms for efficient work.

Features of Spark MLlib:

• It consists of various utilities like Basic Statistics, Recommendation System, Dimension Reduction and Optimization for better statistical results.
• You can easily fabricate a plugin to Hadoop workflows.
• It can be easily integrated with Spark’s APIs and Python’s NumPy.
• You can use data sources like HDFS, HBase with this tool.

9. Amazon Machine Learning

Being a cloud-based platform, AML is a robust machine learning software, which you can use as a web or mobile app developer. It is mainly used for prediction algorithms in addition to handling database queries. It can also integrate data from multiple sources like Amazon S3, RDS and many more.

Features of Amazon ML:

• It allows you to build a data source object from the data that is being stored on Redshift.
• It supports three types of models of machine learning, namely, binary classification, regression and multi-class classification.
• Along with real-time predictions, it also facilitates Batch predictions.

10. Weka

Written in Java, you can use Weka when you’ve to work with data mining algorithms. Apart from the fact that you can operate it from all the leading operating systems like Windows, macOS, and Linux Kernel, Weka is a really powerful tool for mathematical modeling.

Features of Weka:

• You can use it for Clustering Algorithms
• The major feature of this tool is it provides online training to beginners and is great for learning algorithms.
• It is beneficial and easy to use for students.

11. Rapid Miner

If you are looking for a platform for using Machine Learning algorithms, deep learning, predictive analytics and more, then Rapid Miner is a great tool for you. You can also use it for research, educational as well as application development.

Features of Rapid Miner:

• It helps you in designing and implementing predictive analysis through GUI.
• It is a great tool for model validation and optimization.
• You do not require expertise in any programming languages to use this tool.
• It is available for free for some period and then the applicable prices are low as well.

Conclusion

Now that you are familiar with all the leading machine learning softwares, you’ll now be able to get your hands on learning these. According to your requirements, you’ll now be able to choose your favorable framework. With this curated list of tools, you can now provide your customers with an effective software development solution as per their choice.