MACHINE LEARNING

9/24/2019

MACHINE LEARNING - usm systems - Medium

MACHINE LEARNING usm systems Follow Sep 24 · 3 min read

What is machine learning? https://medium.com/@usmsystems23/machine-learning-cb409e08a9b1

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MACHINE LEARNING - usm systems - Medium

Machine learning is part of artificial intelligence, which gives systems the ability to automatically learn and improve from experience. Its goal is to allow computers to make fully data-driven decisions.

The ML model differs from any conventional software, where the human developer has a written code that works for the next step. Instead, the ML model is taught how to draw inferences from given input data.

https://medium.com/@usmsystems23/machine-learning-cb409e08a9b1

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MACHINE LEARNING - usm systems - Medium

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MACHINE LEARNING - usm systems - Medium

How machine learning works The machine learning model uses two types of methods, supervised learning and unsupervised learning. In the past, the Data Scientist serves as a guide to the machine, where it is trained with known input and output data so that it can predict future outcomes. In the latter, the future output is estimated based on hidden patterns in the input data.

Here is an example that describes the performance of ML: You want to buy the best phone on the market. You search the internet to find the phone with the maximum number of good reviews and buy the one with the maximum number of reviews.

Thus, reviews from previous customers who have purchased the phone have helped prospective customers make a buying decision. This is similar to a sample of previous customers who purchased the phone and prospective customers who completed the purchase. So machine learning usually involves understanding any hidden pattern and drawing inferences from input data.

What is the importance of machine learning? https://medium.com/@usmsystems23/machine-learning-cb409e08a9b1

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ML is increasing business scalability and improving business operations for companies around the world. Literally speaking, ML pulls out meaningful insights from raw data to quickly solve data-driven business problems. It’s everywhere from self-driving cars to face recognition on Facebook.

Some statistics showing the importance of machine learning: 76% of business entrepreneurs experience sales growth after implementing machine learning. Not only that, almost 60% of businesses are interested in applying machine learning to maximize profits through marketing.

What is machine learning used for?

When it comes to machine learning moment, you need to know when and how to use machine learning. Solving a complex problem/task involving large amounts of data and many variables when you use machine learning.

Machine learning should not be used in situations where machine models cannot be identified. It should not be used even in cases where rules such as fraud detection or shopping trends are constantly changing.

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MACHINE LEARNING - usm systems - Medium

Advantages of machine learning Sales and Marketing- Machine learning helps to study customer behavior patterns from previous marketing campaigns. For example, machine learning automatically optimizes the relevant ad to suit your customer’s needs. This helps businesses make accurate sales forecasts.

Product Recommendation- Every person is different and their preference for clothes, songs, movies, etc. Companies like Amazon and Netflix use https://medium.com/@usmsystems23/machine-learning-cb409e08a9b1

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MACHINE LEARNING - usm systems - Medium

input data to draw inferences that may be of interest to the consumer.

Financial Sector — Launching the ML model in the financial sector helps prevent fraud. It continuously assesses the data and detects any anomalies and nuances.

Health care — In this field, launching the ML model helps identify highrisk patients. This helps to identify the most accurate diagnosis and recommend the best possible medication.

Disadvantages of machine learning Usage is limited — even after the ML model can solve various complex problems, we are not aware that ML algorithms work in every case.

Previous records of data are required — the machine learning system is less likely to give immediate estimates because it basically reads previous input data.

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