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 is being used in a wide range of applications today and it is set to grow at a high speed.
Machine Learing process involves Identificaton of relevant data, Choose the algorithm, Build and Train the model and then run the model to generate report.
Machine learning is also entering an array of enterprise applications. From Customer relationship management (CRM) systems, Application tracking systems (ATS), Ecommerce applications, Business intelligence (BI) and analytics systems, it is set to be involved everywhere.
There are various types of machine learning algorithms like Decision trees, K-means clustering, Neural networks, Supervised learning, Unsupervised learning, Reinforcement learning etc.
While machine learning algorithms have been around for decades, they've attained new popularity as artificial intelligence (AI) has grown in prominence. Deep learning models in particular power today's most advanced AI applications.
Some of the top Machine Learning use cases are: Process Automation, Sales Optimization, Customer Service, Security, Collaboration.
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Python is the most widely used language for Machine Learing. Scikit-learn is a Python Machine Learning Library. It has features to classify data using Support Vector Machines(SVMs).
If you want your program to predict, for example, traffic patterns at a busy intersection, you can run it through a machine learning algorithm with data about past traffic patterns and, if it has successfully “learned”, it will then do better at predicting future traffic patterns.
Within the field of data analytics, machine learning is used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to “produce reliable, repeatable decisions and results” and uncover “hidden insights” through learning from historical relationships and trends in the input data set.