Introduction to Machine Learning Software
Machine Learning is a scientific application based on AI(Artificial Intelligence) that enables the computer system to learn, perform a certain task, and improve the experience gained out of it without programming it actually exclusively. Machine Learning algorithms are used in day-to-day exercises such as search engines, image & speech recognition, healthcare diagnosis, etc. These algorithms need certain software and tools to execute and perform certain actions. Machine Learning software has its own set of tools, libraries, and resources to develop applications that can replicate human behavior with smart machines and applications. The software we’ll be highlighting is reflected below.
Types of Machine Learning Software
There are tons of Machine Learning software available in the market. Some of the software has exclusive and unique features. At the same time, there are some tools that are comparatively better. Many businesses have started to adopt the technology to increase their ROI (Return on Investment). As Machine Learning is a part of AI (Artificial Intelligence), machines are trained from the result or pattern to develop new changes autonomously. Some types of Machine Learning software are- Azure Machine Learning Studio, Shogun, Apache Mahout, Apache Spark MLlib, IBM Watson Machine Learning, RapidMinor, Weka, Google Cloud ML Engine, Pytroch, Figure Eight, Crab, Microsoft Cognitive Toolkit, Torch, etc. Let’s have a closer look at some top and most used software that Machine Learning experts rely on.
Top Machine Learning software
Below are the various machine learning software:
1. Google Cloud ML Engine
Google’s Cloud Machine Learning Engine is one of the popular applications for training, analysis, and deep learning.
2. Azure ML Studio
It is a code-free, drag and drop solution for ML experts for convenient use made by Microsoft.
3. IBM Watson Machine Learning
With its open-source model operation, it helps data scientists and developers to expedite AI and Machine Learning applications.
4. TensorFlow
It’s a new open-source framework released in 2015 and not complex to operate and as well can be deployed over various platforms. Made by Google, TensorFlow is available for Python, C++, Java, Rust, etc.
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5. Microsoft Cognitive Toolkit
Microsoft’s newly launched AI solution can train the machine with its deep learning algorithms to behave like a human brain and face. It can handle data from Python, C++, etc.
6. Theano
It is a Python-based open-source library for deep learning algorithms to accelerate Machine Learning deployment. Theano is capable of taking data structures to the next level with the integration of Python libraries like NumPy, Pandas, and some native programs.
7. Torch
This typical older Machine Learning library fascinates flexibility and speediness in operations while deploying Machine Learning projects.
8. Apache Spark MLlib
Apache Spark is a trusted Machine Learning tool for advanced Machine Learning and deep learning. It is a seamless, scalable platform that can be integrated with Hadoop for better results from algorithms. Classification, Regression, Gradient boosting, Decision Trees, LDA, etc are some of the algorithms to support MLlib.
9. Pytorch
Pytorch is developed by Facebook for advanced deep learning using Neural Networks and Tensors. It is ascertained that researchers across the world use Pytorch for dynamic graphical representation and blueprint techniques.
10. Ideas2T Technologies
Ideas2T tool, unlike others, has a unique feature that can help recruiters to hire the right candidates based on their resume(s) through Machine Learning. It has been an intersection for start-ups, enterprises and business houses because of its cutting-edge and advanced algorithms.
Merits of Machine Learning Software
Machine Learning, a subset of AI that helps to examine and learn from the data and to make certain decisions out of it. It is capable of delivering decisions and recommendations at high speed with accuracy. So, ML software is very popular. Interestingly, they are inexpensive too. Both Machine Learning and AI work on large and complex data sets to visualize and make perfect decisions. A few examples are as under:
- Facebook: When we upload a photo on Facebook, it recognizes a person from the same photo and suggests mutual friendship. This is how ML works.
- Netflix: Netflix sometimes sends suggestions of web shows or movies based on what we have watched earlier. Basically, Machine Learning is used to select data based on choice.
Business entities and organizations use the right pair of the algorithm with a certain tool and make Machine Learning models based on learning from the data. It helps enterprises to work more efficiently to build high-end models at a low cost. Machine Learning software helps in the automation of data analysis which reduces manpower and makes it cost-effective. This process is quite iterative and scalable.
There are several other applications where ML is used in day-to-day life. Some of the areas where ML software is efficiently used are as follows:
- Financial Services: The financial sector use this to identify insights for investments, trading and even ascertaining financial risks.
- Marketing & Sales: Various companies and other similar establishments use Machine Learning software to analyze the history of purchases and make recommendations for customers depending on it.
- Healthcare: Electronic wearables and sensors are used widely nowadays. Sensors in those wearables provide real-time data on blood pressure, heartbeat, and other vital information related to health.
- Transportation: Machine Learning software analyses the travel history and routes to identify problems, if any, so as to help customers/passengers opt-out of risk-prone areas in the future.
- Government: Some Government agencies use ML tools to extract insights to minimize cost and increase efficiency in their operations.
- Oil & Gas: Machine Learning is mostly used in the energy sectors. It helps analyze existing and new energy sources for exploration and distribution. Many oil agencies in both Government and Private sectors have adopted these techniques.
Conclusion
Machine Learning and Artificial Intelligence have wide and broad uses among modern generations and is still expanding. Hence, it won’t be wrong to opine that Machine Learning software performs actions from the input data set and play a big role in providing insights from raw data to improve commercial and other goals.
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