Machine Learning Basics

What is Machine Learning?

Machine learning (ML) is the process of training computers, using math and statistical processes, to find and recognise patterns in data. After patterns are found, ML generates and updates training models to make increasingly accurate predictions and inferences about future outcomes based on historical and new data. For example, ML could help determine the likelihood of a customer purchasing a particular product based on previous purchases by the user or the product’s past sales history.

Building ML applications is an iterative process that involves a sequence of steps. To build an ML application, follow these general steps:

Key Terms in Machine Learning

ML Model:
The output of an ML algorithm trained on a data set used for data prediction.

The act of creating a model from historical data called Testing Dataset.

Measuring the performance of a model on Testing Dataset.

Integrating a ML model into a production pipeline or application.

Machine Learning Flywheel

ML flywheel uses data collected from parts of a business operation, uses a model to predict future outcomes, and provides ways to continuously improve efficiency and develop new operational capabilities and business practices. With ML, increasing predictions improve growth and efficiency. This leads to more usage and data, completing the feedback loop and reinforcing all parts of the flywheel.

How can machine learning help me?

Machine learning can continuously improve results, which means training models can become a part of almost any decision-making process. Machine learning can ingest limitless amounts of data, produce timely analysis and assessment, identify trends and patterns, and generate predictive forecasts.

Some examples of Machine Learning being used today

Example 1: Ride-sharing apps like Uber and Lyft use data to lower wait times, predict demand, and optimize price setting.

Example 2:Online shopping sites use ML to customize search results and improve product recommendations. Financial institutions use AI to recognize content on mobile check deposits.

Example 3: Credit- or debit-card transaction businesses use ML to scan for fraud. Research suggests that more than 8 billion digital voice assistants will be powered by AI and ML over the next few years.

Others: Industrial companies use AI and ML services for asset management. This includes using computer vision for equipment monitoring and defect detection, or analyzing operational machine behavior data to enable predictive maintenance. Customer service organizations use ML to transcribe and analyze live and archived calls for sentiment scores. ML can also help prioritize based on categorized customer feedback, and enable software to provide agents with answers to questions as they are being asked.



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