How is Machine Learning used in industry?

  • Unsupervised Learning — Unsupervised learning occurs when an algorithm learns from plain examples without any associated response, leaving the algorithm to determine the data patterns on its own.
  • Reinforcement Learning — Using this algorithm, the machine is trained to make specific decisions. The machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.

Machine Learning is Widely Applicable

Most industries working with big data have recognized the value of Machine Learning technology. By collecting insights from this data, organizations and companies are able to work more efficiently or gain an advantage over competitors.

Which industries use machine learning?

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Skill Areas within Machine Learning

At the time of writing this article only 28% of companies have some experience with AI or Machine Learning, and more than 40% said their enterprise IT personnel don’t have the skills required to implement and support AI and/or Machine Learning.
Below are some key skill areas that are required to work in the field of Machine Learning:

  • Statistics
  • Data Modeling
  • Data Science
  • Software Engineering
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  • Data Analysts — Data Analysts monitor processes, evaluate data quality, and monitor production model performance. This allows for more senior roles to focus on innovation, not maintenance.
  • Data Scientists — Data Scientists own the modeling process. In general, they take input parameters from product or other team leads in order to understand the model’s business objective. They then work to articulate requirements to the engineers and other stakeholders. Once these criteria have been defined, the process of building tests, models, and evaluating performance begins.
  • Machine Learning Engineers — With backgrounds and skills in data science, applied research, and heavy-duty coding, these professionals run the operations of a machine learning project and are responsible for managing the infrastructure and data pipelines needed to bring code to production.

We develop a data-driven Machine Learning model that can use actual production input parameters and their output parameters to estimate the output.

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