What is Machine Learning?
- Machine Learning is the subset of the AI (Artificial Intelligence).
- Machine Learning (ML) is an important area of computational science.
- That focuses on analyzing and interpreting patterns and data structures that enable learning, reasoning, and decision-making.
- Decisions without human interaction.
- In other words, Machine Learning allows the user to feed a computer algorithm with a vast amount of data.
- The computer analyzes all the information and can make decisions
- Make recommendations based solely on the data entered.
- In the case of identifying corrections, the algorithm can incorporate that information to improve future decision-making.
How does machine learning work?
Machine learning consists of three parts:
- The computational algorithm, located at the core of making determinations.
- The variables and functions that make up the decision.
- The base knowledge according to which the answer allows the system to learn (trains it) is known.
- Initially, the model with parameter data for which the answer is known.
- The algorithm is then run and adjustments until the algorithm’s result (the learning) matches the known solution.
- At this time, the amount of data entered increases to help the system learn
- The Process a more significant number of computational decisions.
Why is machine learning meaningful?
- Data is an essential part of all businesses.
- Based on data analysis, decisions increasingly make the difference between keeping up with the competition or falling.
- It can also be the key to unlocking the value of a customer
- Corporate data and enacting decisions that keep the business ahead of the competition.
Machine Learning Case Studies:
- Machine learning applies to all industries.
- These include manufacturing, retail, health services, life sciences, travel, hospitality, financial services, energy, raw materials, and utilities.
The practical cases are:
- Predictive maintenance and conditional supervision
- Upselling and multichannel marketing
Health services and life sciences:
- Disease identification and risk satisfaction
Travel and hospitality:
- It is best for Dynamic pricing
- Financial services Risk analysis and regulation
- Energy demand and supply optimization
But why is there so much talk today about machine learning?
- Many of the methods used in machine learning and statistical modeling have been with us for several decades.
Essential reasons for these techniques for the current boom are:
- The computers of computational capacity have been increasing, and it is currently possible to treat problems.
- Firstly, it increase has been vertical
- Secondly, it improves the individual computing capacity, CUDAs)
- Horizontal (increase in computing capacity using Big Data when working with several computers.
- In ML the data revolution, motivated by digitization.
- And also, ML has led to a considerable increase in data
- It can be processed and modeled to gain knowledge.
- Years ago, there was much less data, seeing statistical models of a few hundred records.
- Also in other words, algorithms that learn and improve “on their own” thanks to experience.
- They do it alone in quotes because they do it using data, past experiences.
- Unlike models in which a business expert assigns rules and models
- And also, something based on their knowledge (their expertise).
- However, sometimes it is based on statistical models and ML models.
- Lastly, let the data do the talking and get the relationships automatically.
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