Machine Learning: What is it and how is it applied?

One of the trendy concepts nowadays is Artificial Intelligence in its Data Science and Machine Learning aspects. However, there are few non-specialists who know how to clearly define what we are talking about when we talk about a course to learn Machine Learning.

In Ubiqum Code Academy, we have a specialized course in Data Analytics and Machine Learning and in this short article we will tell you what it is and how you can learn Machine Learning and how it is used in data analysis.

If we go to the “official” definition we find the following: Machine learning, or automatic learning, is the branch of artificial intelligence that endows machines with the ability to “learn” from data analysis in order to identify patterns and support decision making with minimal human intervention; people and machines work hand in hand.

In our view, this definition is rather abstract and inaccurate. First of all because “machines” do not “learn”. Computers are still “very fast dummies” that execute very complex calculations very fast, but everything they execute has been programmed by humans. It has not yet been invented, and it will take a long time to see it, a computer that “self-programs” itself, that is, that dynamically and at will alters the code of its programs according to its own experience, something that the human mind and memory do quite easily.

In any case, what is certain is that software engineering together with the capacity and power of today’s computers and the enormous amount of digital data stored and available have made possible the emergence of very complex and ingenious programs that allow us to process and analyze a huge amount of data efficiently. This capability is new, it did not exist just a couple of decades ago and it is changing the way we do business.

To begin to clarify this topic for the reader who is looking for a course that will allow access to a new and promising profession, we will begin by describing the three profiles that make up the modern and very recent Data Science profession, which includes concepts such as Big Data, Machine Learning and Data Analytics.

We can say that in this new profession there are three well-differentiated profiles:

Machine Learning Engineer.

First of all we need people to develop the algorithms. These algorithms are very complex software programs and require a great deal of expertise. A Machine Learning Engineer, as described here, is a software engineer on steroids. To develop this specialization requires between 10 and 15 years of work and possibly a PhD whose thesis is the development of a new Machine Learning algorithm.

Data Engineer

Secondly, we need the people who deal with the data. For as long as there have been computers there have been people who have dealt with data. In systems administration departments there has always been a database administration team. But this job has become much more sophisticated in the last 20 years. This profile is still very technical and specialized in managing new products around database administration.

Business Data Analyst

Finally we have the people who analyze and leverage data to improve the business. This is the profile we develop at Ubiqum, with the students who participate in our Data Analytics & Machine Learning course. We like to assert that, within 5 to 10 years, any senior executive in any company should have developed these skills.

Machine Learning algorithms are thus one piece of the overall Data Analytics process in which they are used to process large amounts of data and obtain results that help improve business.

What machine learning algorithms will you learn at Ubiqum?

In view of the above definition, Machine Learning algorithms are an integral part of a more complex process, the business data analytics process, or Cross Industry Standard Process for Data Mining. Data Mining is another name for data analysis.

This process consists of the following steps:

  1. Formulation of a hypothesis or business problem to be solved through data analysis.
  2. Creation, cleaning, preparation and pre-processing of a dataset. It includes EDA (Exploratory Data Analysis) and FE (Feature Engineering), both activities require the use of Python and/or R libraries.
  3. Modeling the problem by using a Machine Learning algorithm. This step requires knowledge of ML algorithms and how they work.
  4. Analysis of results and iteration between steps 2, 3 and 4 until satisfactory results are achieved (model training).
  5. Convert the results into valid and executable conclusions for the business.

 

In this broader context it can be seen that knowing and knowing how to operate Machine Learning algorithms is a necessary but not sufficient condition to become a professional data analyst.

Here are some of the main algorithms you will learn in Ubiqum and examples of their application:

  1. Linear Regression: This algorithm is used to predict a continuous value based on one or more independent variables. An example would be the prediction of housing prices, based on factors such as size in m2, location and number of rooms to see how they influence the price.

  2. Logistic Regression: Used primarily for binary classification, this algorithm predicts the probability that an instance belongs to a particular class. An example is medical diagnosis, where the probability that a patient has a disease is estimated based on symptoms and test results.

  3. Decision Trees and Random Forest: Decision trees divide the data set into subsets based on the feature that provides the maximum information gain. Random Forest, which combine multiple decision trees, are used in image classification and predictive analytics in the financial industry.

  4. K-Nearest Neighbors (K-NN): This algorithm ranks an instance based on the majority of its K nearest neighbors. It is common in recommender systems, where products are suggested to a user based on the preferences of similar users.

  5. Clustering (K-means): This algorithm groups data into K clusters based on similar characteristics. It is useful in customer segmentation in marketing, where customers with similar buying behavior are grouped together to customize sales strategies.
  6. Neural Networks (advanced): Inspired by the human brain, these networks are effective for complex tasks such as speech recognition, machine translation and advanced medical diagnostics. Deep learning neural networks are used in artificial intelligence applications such as autonomous driving.
  7. Time Series Analysis (Advanced) . Widely used for demand or inventory forecasting.

 

Each of these algorithms has its specific applications and are chosen according to the nature of the problem and the available data. In Ubiqum you can learn how they all work.

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