We live in the age of data and computing. We are seeing machines and programming deliver computing solutions in all fields that better meet the needs of users.
Now, with the irruption and generalisation of the artificial intelligence (IA), it seems that a new dimension has opened up for the ultimate machine automation and work processes. One of the main contributors to this rapid development of machines is the machine learning.
Machine learning is a discipline of artificial intelligence that endows machines with the ability to learn to perform tasks from vast amounts of data by identifying patterns and making predictions.
How does machine learning work?
To put it simply, machine learning is about providing large volumes of data and using algorithms in order to provide accurate predictions. The more data samples a machine processes, the more efficient its learning process will be and the more rigorous its outputs will be.
We can schematise the functioning of machine learning as follows:
Data collection and preparation
Data relevant to the problem to be solved are collected and cleaned. This data can come from various sources, such as databases, sensors, historical records, among others.
Algorithm selection
Once the data is ready, the most appropriate machine learning algorithm for the problem at hand is chosen. There are numerous types of algorithms, each with its own strengths and weaknesses, such as linear regression, decision trees, neural networks, among others.
Model training
A part of the data (training set) is used as input to feed the algorithm and allow it to learn from the patterns and features present in the data.
Model evaluation and adjustment
The performance of the trained model is evaluated using a different dataset that has not been seen before (test set). Various metrics are analysed to measure its accuracy and effectiveness.
In case of unsatisfactory performance, the introduced algorithm is discarded and adjustments are made to the discarded algorithm to generate a new one, to the data or to its parameters to improve its performance.
Commissioning and maintenance
If the selected algorithm has passed all checks and has been proven to be infallible, it is then deployed in a production environment, where it will start making predictions or decisions based on new input data.
But the machine learning process does not stop there; its performance must continue to be monitored and updated as necessary to ensure its continued effectiveness.
What is the difference between machine learning and deep learning?
Although the two terms sound synonymous and we have heard them in very similar situations, they both refer to different AI training techniques. If you want to talk properly about artificial intelligence, you need to know the difference between the two terms.
First of all, deep learning is part of machine learning. That is to say, deep learning is a specialisation of machine learning, not a separate discipline. In fact, one could say that deep learning goes a step further than traditional machine learning, as it uses more developed neural networks.
In turn, in deep learning, the algorithm learns and evaluates itself with an abysmal amount of data., It is similar to the way a human being evaluates himself, so it does not need as much human intervention as the usual machine learning.
What is machine learning for? Machine learning applications
Medicine
This technological innovation has revolutionised medicine by enabling the analysis of large amounts of medical data, such as images from MRIs, CT scans and X-rays, to aid in the diagnosis of disease and early detection of pathologies. In addition, it is used to predict disease risk in patients, such as cardiovascular risk or the development of diabetes. It is also essential in genomics, where it helps to identify genetic mutations that lead to hereditary diseases and facilitates the development of personalised therapies based on the patient's genetic profile.
Finance
In the financial sector, machine learning is a powerful tool for analysing large volumes of financial data in real time. Machine learning algorithms can predict market movements, identify fraud patterns in financial transactions and assess the credit risk of loan applicants.
Industry and manufacturing
Machine learning is applied across industries to improve efficiency in production processes and optimise predictive maintenance of machinery. By analysing data from sensors and historical records, it is possible to anticipate equipment failures and make repairs before costly damage occurs.
E-commerce and digital marketing
In the commercial domain, machine learning is used to improve the customer experience on e-commerce platforms by customise product recommendations and suggest purchases based on browsing history and past purchases.
Agriculture
Machine learning and artificial intelligence in agriculture have further automated farming and increased yields in order to produce better quality food. By analysing meteorological, soil and crop data, it is possible to predict the optimal time for sowing and harvesting, as well as improving irrigation and fertiliser application to maximise yields and minimise environmental impact.
Energy and sustainability
Machine learning algorithms can predicting future generation and adjusting power distribution to ensure greater efficiency in consumption. Machine learning is therefore essential to optimise energy generation and distribution, especially in the case of renewable energies, such as solar and wind energy, whose production is variable and depends on climatic factors.
Who can learn machine learning?
To learn all the ins and outs of machine learning effectively, it is recommended to have a solid foundation in mathematics and programming. Although there are resources designed for beginners, machine learning is a complex discipline involving mathematical concepts such as linear algebra, calculus, statistics and probability.
While machine learning is an exciting and accessible field for many, it is important to recognise that the learning journey can take time, dedication and constant practice.
It is therefore advisable to start with the basics of mathematics and programming, and then gradually move on to the more advanced concepts of machine learning.
Do you want to learn machine learning? At Euroinnova we offer you state-of-the-art 100% online training adapted to your level. Take a look at our machine learning training catalogue and choose the one that best suits you!
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