Explain learning about the machine to my mom [ who does not have a background in STEM ].
[Machine learning is present in our daily life.]
The birth of machine learning
Machine learning, known in Spanish as aprendizaje automático or aprendizaje de máquina, was born as an ambitious AI idea in the 1960s. What this branch intended to study where pattern recognition and computer learning. Throughout the year machine learning began to focus on different topics such as probabilistic reasoning, statistically-based research, information retrieval and continued to delve into pattern recognition. Now, the main objective of machine learning is to address and solve practical problems in which any of the above-mentioned numerical disciplines are applied.
It is a field of computer science that, according to Arthur Samuel in 1959, gives computers the ability to learn without being explicitly programmed.
For it is nothing more than a sequence or series of instructions, which represent the solution to a given problem.
The purpose of machine learning is that people and machines work hand in hand, as they can learn as a human would.
Once you see how easy and practical it is to apply machine learning techniques to problems you thought would be impossible, you begin to believe that it could solve virtually any problem as long as enough data exists. For the modern consumer, machine learning is a key facilitator of many of their daily tasks. Since machine learning is a system based on processing and analyzing data that is translated into findings, it can be applied to any field that has large enough databases.
An example of the last point is Adext. Adext is the first and only AMaaS to apply Artificial Intelligence and Machine Learning to digital advertising to find the best audience or demographic for any ad. Besides, agencies that are Adext Partners are guaranteed under contract to exceed the current conversion cost of all accounts or campaigns they run as an agency. And if you’d like to know in more detail how Adext uses Machine Learning to find the best audience for each ad, ensuring the best conversion costs, this step-by-step guide is perfect for you.
Trending AI Articles:
2. Are you using the term ‘AI’ incorrectly?
According to the Global Digital IQ survey, 54% of the organizations they interviewed are making significant investments in AI and that percentage is expected to increase to 63% in less than three years. Meanwhile, HubSpot reported that according to an AI survey it conducted, 63% of people use AI daily, without even knowing it.
Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead.
This is the study of the algorithms that computers use to perform a specific task without using explicit instructions.
In simple words: Machine Learning is training a computer with many examples so that it can predict new samples. Like when the Instagram algorithm tries to predict things you like according to your likes.
Learning or training
It is the process in which patterns in a data set are detected, that is, it is the heart of machine learning. Once the patterns are identified, predictions can be made with new data that are incorporated into the system.
For example, historical data from book purchases on an online website can be used to analyze customer behavior in their purchasing process (titles visited, categories, purchase history…), group them into behavior patterns and make purchase recommendations to new customers who follow the patterns already known or learned.
Training Algorithms: the essential piece for making predictions
As a result, it is possible to obtain better results while investing less time in training and obtaining the model. However, it is convenient to remember that not only the choice of the algorithm we are going to use in the training is important to guarantee the adequate resolution of a problem. At the same time, it is very important to understand the data we have, prepare them and process them properly to perform the training of what will be our final model.
Machine Learning is divided into two main areas: supervised learning and unsupervised learning. Although it may seem that the first refers to prediction with human intervention and the second does not, these two concepts have more to do with what we want to do with the data.
Supervised learning
The machine is provided with detailed and labeled data and information. This is the knowledge base for your analyses. The different examples serve you to make further generalizations.
Unsupervised learning
It’s more like the way our brain works. The computer receives no prior information about the data. It has to track its database and establish patterns through understanding and abstraction.
Learning by reinforcement
The computer learns from experience. It is based on the trial-and-error system, by which the observation of the world around it is the basis of its learning. That feedback makes it better.
Linear Regression and Logistic Regression (Supervised Learning)
Linear Regression is a Machine Learning algorithm used to obtain a numerical result. This algorithm tries to establish a linear relationship between independent variables and output or dependent variable. An example of the application of Linear Regression, shown in Figure, would be the prediction of the demand for a product at a given time from a set of previously recorded demand data. Logistic Regression, on the other hand, is not used for numerical variables but is used to predict the outcome of a categorical variable as a function of independent variables.
An example of the application of logistic regression is to predict whether a customer will give up a certain service from a telephone company.
Decision Tree (Supervised Learning)
Decision tree models where the target variable can take a finite set of values are called classification trees. A Decision Tree can be used, for example, to produce a model for medical diagnostics.
Boosted Decision Tree and Decision Forest (Supervised Learning)
The Boosted Decision Tree algorithm, whose operation is schematically illustrated in Figure, is based on a set of Decision Trees in which the second tree corrects the errors of the first, the third corrects the errors of the first and second and so on. On the other hand, a Decision Forest works by building multiple Decision Trees and “voting” for the most popular type of output. Therefore, unlike the Boosted Decision Tree, where the results are additive, in a Decision Forest, the results are averaged. The Boosted Decision Tree and Decision Forest algorithms are used to study the same type of problems that are analyzed with Decision Trees.
Neural Network and Averaged Perceptron (Supervised Learning)
A Neural Network is composed of a set of interconnected layers in which the input values or inputs give rise to the output values or outputs through a series of nodes with their corresponding weights. Between the input and output layers, there may be one or more hidden layers, as shown in Figure 5. The Averaged Perceptron is a simplified version of the Neural Network that classifies the inputs into the different possible outputs based on a linear function.
Clustering Algorithms (Unsupervised learning)
They classify the data in groups or clusters according to the similarities of their attributes. At the same time, they look for data grouped in different clusters to be different. To assess the differences between the data, the clustering algorithms calculate the Euclidean distance between numerical attributes, so that the lower this value is, the more similar the instances are and the more likely they are to be grouped in the same cluster.
Anomaly Detection Algorithms (Unsupervised learning)
One of the most used algorithms in Anomaly Detection is the Isolation Forest algorithm, in which very anomalous instances will present very different attributes from the usual ones, which will allow us to differentiate and separate them from the rest that compose the data set. By establishing successive conditions on the attributes, the instances are separated in nodes. Anomaly Detection algorithms are often applied to detect fraud, for example in cases of bank loans.
Classifier comparison
A comparison of various classifiers in scikit-learn into synthetic data sets. The purpose of this example is to illustrate the nature of the decision boundaries of the different classifiers. Particularly in high dimensional spaces, data can be more easily separated linearly and the simplicity of classifiers such as naive Bayes and linear SVM could lead to better generalization than other classifiers.
Let’s play Machine Learning
The price of a house:
The price could be:
- COP 80.000
- COP 120.000
- COP 190.000
The price is 💁 $120,000
Is machine learning magic?
Once you realize how easy it is to apply machine learning techniques to seemingly difficult problems (such as handwriting recognition), you begin to feel that you can use machine learning to solve any problem and get a satisfactory answer as long as you have enough data. Just feed the data and watch the computer magically find the answer!. But it is important to remember that machine learning only works if the problem can be solved first with the data you have.
How to learn more about machine learning?
If you want to learn about this wonderful world, I leave you this repository where you will find a list of courses and materials about ML and others issues.
References.
http://slides.com/marisbotero/deck#
https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico
https://www.bbva.com/es/machine-learning-que-es-y-como-funciona/
https://cleverdata.io/que-es-machine-learning-big-data/
https://blog.gfi.es/algoritmos-entrenamiento-machine-learning/
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