Machine Learning Tools
What is Machine Learning?? Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.
Importance of Machine Learning Data is very important for all the businesses. Data-driven decisions increasingly make the difference between keeping up with competition or falling further behind. Machine learning can be the key to unlock the value of corporate and customer data and enacting decisions that keep a company ahead of the competition. The machine learning field is continuously evolving. And along with evolution comes a rise in demand and importance.
Popular Machine Learning Tools 1. Scikit-learn • Scikit-learn is for machine learning development in python. It provides a library for the Python programming language. It helps in data mining and data analysis. It provides models and algorithms for Classification, Regression, Clustering, Dimensional reduction, Model selection, and Pre-processing. • Advantages- Easily understandable documentation is provided. Parameters for any specific algorithm can be changed while calling objects.
2. PyTorch • PyTorch is a Torch based, Python machine learning library. The torch is a Lua based computing framework, scripting language, and machine learning library. It helps in building neural networks through Autograd Module. It provides a variety of optimization algorithms for building neural networks. PyTorch can be used on cloud platforms. It provides distributed training, various tools, and libraries. • Advantages- It helps in creating computational graphs. Ease of use because of the hybrid front-end.
3. TensorFlow • TensorFlow provides a JavaScript library which helps in machine learning. APIs will help you to build and train the models. It helps in training and building your models. You can run your existing models with the help of TensorFlow.js which is a model converter. It helps in the neural network. • Advantages- You can use it in two ways, i.e. by script tags or by installing through NPM. It can even help for human pose estimation.
4. Weka • These machine learning algorithms help in data mining. The features are Data preparation, Classification, Regression, Clustering, Visualization and Association rules mining. • Advantages are it provides online courses for training and it is easy to understand algorithms.
5. Knime • Knime is a tool for data analytics, reporting and integration platform. Using the data pipelining concept, it combines different components for machine learning and data mining. It can integrate the code of programming languages like C, C++, R, Python, Java, JavaScript etc. It can be used for business intelligence, financial data analysis, and CRM. • Advantages are it can work as a SAS alternative. It is easy to deploy and install and easy to learn. • Disadvantages are it is difficult to build complicated models and has limited visualization and exporting capabilities.
7. Apache Mahout • Apache Mahout helps mathematicians, statisticians, and data scientists for executing their algorithms. It provides algorithms for Pre-processors, Regression, Clustering, Recommenders, and Distributed Linear Algebra. Java libraries are included for common math operations. It follows Distributed linear algebra framework.
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