As artificial intelligence and machine learning are becoming present in almost every aspect of life, it’s essential to understand how they work and their common applications. Although machine learning has been around for a while, many still portray it as an enemy. Machine learning can be your friend, but only if you learn to “tame” it.


Regression stands out as one of the most popular machine-learning techniques. It serves as a bridge that connects the past to the present and future. It does so by picking up on different “events” from the past and breaking them apart to analyze them. Based on this analysis, regression can make conclusions about the future and help many plan the next move.


The weather forecast is a basic example. With the regression technique, it’s possible to travel back in time to view average temperatures, humidity, and other variables relevant to the results. Then, you “return” to present and tailor predictions about the weather in the future.


There are different types of regression, and each has unique applications, advantages, and drawbacks. This article will analyze these types.


Linear Regression


Linear regression in machine learning is one of the most common techniques. This simple algorithm got its name because of what it does. It digs deep into the relationship between independent and dependent variables. Based on the findings, linear regression makes predictions about the future.


There are two distinguishable types of linear regression:


  • Simple linear regression – There’s only one input variable.
  • Multiple linear regression – There are several input variables.

Linear regression has proven useful in various spheres. Its most popular applications are:


  • Predicting salaries
  • Analyzing trends
  • Forecasting traffic ETAs
  • Predicting real estate prices

Polynomial Regression


At its core, polynomial regression functions just like linear regression, with one crucial difference – the former works with non-linear datasets.


When there’s a non-linear relationship between variables, you can’t do much with linear regression. In such cases, you send polynomial regression to the rescue. You do this by adding polynomial features to linear regression. Then, you analyze these features using a linear model to get relevant results.


Here’s a real-life example in action. Polynomial regression can analyze the spread rate of infectious diseases, including COVID-19.


Ridge Regression


Ridge regression is a type of linear regression. What’s the difference between the two? You use ridge regression when there’s high colinearity between independent variables. In such cases, you have to add bias to ensure precise long-term results.


This type of regression is also called L2 regularization because it makes the model less complex. As such, ridge regression is suitable for solving problems with more parameters than samples. Due to its characteristics, this regression has an honorary spot in medicine. It’s used to analyze patients’ clinical measures and the presence of specific antigens. Based on the results, the regression establishes trends.


LASSO Regression


No, LASSO regression doesn’t have anything to do with cowboys and catching cattle (although that would be interesting). LASSO is actually an acronym for Least Absolute Shrinkage and Selection Operator.


Like ridge regression, this one also belongs to regularization techniques. What does it regulate? It reduces a model’s complexity by eliminating parameters that aren’t relevant, thus concentrating the selection and guaranteeing better results.


Many choose ridge regression when analyzing a model with numerous true coefficients. When there are only a few of them, use LASSO. Therefore, their applications are similar; the real difference lies in the number of available coefficients.



Elastic Net Regression


Ridge regression is good for analyzing problems involving more parameters than samples. However, it’s not perfect; this regression type doesn’t promise to eliminate irrelevant coefficients from the equation, thus affecting the results’ reliability.


On the other hand, LASSO regression eliminates irrelevant parameters, but it sometimes focuses on far too few samples for high-dimensional data.


As you can see, both regressions are flawed in a way. Elastic net regression is the combination of the best characteristics of these regression techniques. The first phase is finding ridge coefficients, while the second phase involves a LASSO-like shrinkage of these coefficients to get the best results.


Support Vector Regression


Support vector machine (SVM) belongs to supervised learning algorithms and has two important uses:


  • Regression
  • Classification problems

Let’s try to draw a mental picture of how SVM works. Suppose you have two classes of items (let’s call them red circles and green triangles). Red circles are on the left, while green triangles are on the right. You can separate these two classes by drawing a line between them.


Things get a bit more complicated if you have red circles in the middle and green triangles wrapped around them. In that case, you can’t draw a line to separate the classes. But you can add new dimensions to the mix and create a circle (rectangle, square, or a different shape encompassing just the red circles).


This is what SVM does. It creates a hyperplane and analyzes classes depending on where they belong.


There are a few parameters you need to understand to grasp the reach of SVM fully:


  • Kernel – When you can’t find a hyperplane in a dimension, you move to a higher dimension, which is often challenging to navigate. A kernel is like a navigator that helps you find the hyperplane without plummeting computational costs.
  • Hyperplane – This is what separates two classes in SVM.
  • Decision boundary – Think of this as a line that helps you “decide” the placement of positive and negative examples.

Support vector regression takes a similar approach. It also creates a hyperplane to analyze classes but doesn’t classify them depending on where they belong. Instead, it tries to find a hyperplane that contains a maximum number of data points. At the same time, support vector regression tries to lower the risk of prediction errors.


SVM has various applications. It can be used in finance, bioinformatics, engineering, HR, healthcare, image processing, and other branches.


Decision Tree Regression


This type of supervised learning algorithm can solve both regression and classification issues and work with categorical and numerical datasets.


As its name indicates, decision tree regression deconstructs problems by creating a tree-like structure. In this tree, every node is a test for an attribute, every branch is the result of a test, and every leaf is the final result (decision).


The starting point of (the root) of every tree regression is the parent node. This node splits into two child nodes (data subsets), which are then further divided, thus becoming “parents” to their “children,” and so on.


You can compare a decision tree to a regular tree. If you take care of it and prune the unnecessary branches (those with irrelevant features), you’ll grow a healthy tree (a tree with concise and relevant results).


Due to its versatility and digestibility, decision tree regression can be used in various fields, from finance and healthcare to marketing and education. It offers a unique approach to decision-making by breaking down complex datasets into easy-to-grasp categories.


Random Forest Regression


Random forest regression is essentially decision tree regression but on a much bigger scale. In this case, you have multiple decision trees, each predicting a certain output. Random forest regression analyzes the outputs of every decision tree to come up with the final result.


Keep in mind that the decision trees used in random forest regression are completely independent; there’s no interaction between them until their outputs are analyzed.


Random forest regression is an ensemble learning technique, meaning it combines the results (predictions) of several machine learning algorithms to create one final prediction.


Like decision tree regression, this one can be used in numerous industries.



The Importance of Regression in Machine Learning Is Immeasurable


Regression in machine learning is like a high-tech detective. It travels back in time, identifies valuable clues, and analyzes them thoroughly. Then, it uses the results to predict outcomes with high accuracy and precision. As such, regression found its way to all niches.


You can use it in sales to analyze the customers’ behavior and anticipate their future interests. You can also apply it in finance, whether to discover trends in prices or analyze the stock market. Regression is also used in education, the tech industry, weather forecasting, and many other spheres.


Every regression technique can be valuable, but only if you know how to use it to your advantage. Think of your scenario (variables you want to analyze) and find the best actor (regression technique) who can breathe new life into it.

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Il Sole 24 Ore: 100 thousand IT professionals missing
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
May 14, 2024 6 min read

Written on April 24th 2024

Source here: Il Sole 24 Ore (full article in Italian)


Open Institute of Technology: 100 thousand IT professionals missing

Eurostat data processed and disseminated by OPIT. Stem disciplines: the share of graduates in Italy between the ages of 20 and 29 is 18.3%, compared to the European 21.9%

Today, only 29% of young Italians between 25 and 34 have a degree. Not only that: compared to other European countries, the comparison is unequal given that the average in the Old Continent is 46%, bringing Italy to the penultimate place in this ranking, ahead only of Romania. The gap is evident even if the comparison is limited to STEM disciplines (science, technology, engineering and mathematics) where the share of graduates in Italy between the ages of 20 and 29 is 18.3%, compared to the European 21.9%, with peaks of virtuosity which in the case of France that reaches 29.2%. Added to this is the continuing problem of the mismatch between job supply and demand, so much so that 62.8% of companies struggle to find professionals in the technological and IT fields.

The data

The Eurostat data was processed and disseminated by OPIT – Open Institute of Technology. an academic institution accredited at European level, active in the university level education market with online Bachelor’s and Master’s degrees in the technological and digital fields. We are therefore witnessing a phenomenon with worrying implications on the future of the job market in Italy and on the potential loss of competitiveness of our companies at a global level, especially if inserted in a context in which the macroeconomic scenario in the coming years will undergo a profound discontinuity linked to the arrival of “exponential” technologies such as Artificial Intelligence and robotics, but also to the growing threats related to cybersecurity.

Requirements and updates

According to European House Ambrosetti, over 2,000,000 professionals will have to update their skills in the Digital and IT area by 2026, also to take advantage of the current 100,000 vacant IT positions, as estimated by Frank Recruitment Group. But not only that: the Italian context, which is unfavorable for providing the job market with graduates and skills, also has its roots in the chronic birth rate that characterizes our country: according to ISTAT data, in recent years the number of newborns has fallen by 28%, bringing Italy’s birth rate to 1.24, among the lowest in Europe, where the average is 1.46.

Profumo: “Structural deficiency”

“The chronic problem of the absence of IT professionals is structural and of a dual nature: on one hand the number of newborns – therefore, potential “professionals of the future” – is constantly decreasing; on the other hand, the percentage of young people who acquires degrees are firmly among the lowest in Europe”, declared Francesco Profumo, former Minister of Education and rector of OPIT – Open Institute of Technology. “The reasons are varied: from the cost of education (especially if undertaken off-site), to a university offering that is poorly aligned with changes in society, to a lack of awareness and orientation towards STEM subjects, which guarantee the highest employment rates. Change necessarily involves strong investments in the university system (and, in general, in the education system) at the level of the country, starting from the awareness that a functioning education system is the main driver of growth and development in the medium to long term. It is a debated and discussed topic on which, however, a clear and ambitious position is never taken.”

Stagnant context and educational offer

In this stagnant context, the educational offer that comes from online universities increasingly meets the needs of flexibility, quality and cost of recently graduated students, university students looking for specialization and workers interested in updating themselves with innovative skills. According to data from the Ministry of University and Research, enrollments in accredited online universities in Italy have grown by over 141 thousand units in ten years (since 2011), equal to 293.9%. Added to these are the academic institutions accredited at European level, such as OPIT, whose educational offering is overall capable of opening the doors to hundreds of thousands of students, with affordable costs and extremely innovative and updated degree paths.

Analyzing the figures

An analysis of Eurostat statistics relating to the year 2021 highlights that 27% of Europeans aged between 16 and 74 have attended an entirely digital course. The highest share is recorded in Ireland (46%), Finland and Sweden (45%) and the Netherlands (44%). The lowest in Romania (10%), Bulgaria (12%) and Croatia (18%). Italy is at 20%. “With OPIT” – adds Riccardo Ocleppo, founder and director – “we have created a new model of online academic institution, oriented towards new technologies, with innovative programs, a strong practical focus, and an international approach, with professors and students from 38 countries around the world, and teaching in English. We intend to train Italian students not only on current and updated skills, but to prepare them for an increasingly dynamic and global job market. Our young people must be able to face the challenges of the future like those who study at Stanford or Oxford: with solid skills, but also with relational and attitudinal skills that lead them to create global companies and startups or work in multinationals like their international colleagues. The increasing online teaching offer, if well structured and with quality, represents an incredible form of democratization of education, making it accessible at low costs and with methods that adapt to the flexibility needs of many working students.”

Point of reference

With two degrees already starting in September 2023 – a three-year degree (BSc) in Modern Computer Science and a specialization (MSc) in Applied Data Science & AI – and 4 starting in September 2024: a three-year degree (BSc) in Digital Business, and the specializations (MSc) in Enterprise Cybersecurity, Applied Digital Business and Responsible Artificial Intelligence (AI), OPIT is an academic institution of reference for those who intend to respond to the demands of a job market increasingly oriented towards the field of artificial intelligence. Added to this are a high-profile international teaching staff and an exclusively online educational offer focused on the technological and digital fields.

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Times of India: The 600,000 IT job shortage in India and how to solve it
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
May 2, 2024 3 min read

Written on April 25th 2024

Source here: Times of India 


The job market has never been a straightforward path. Ask anyone who has ever looked for a job, certainly within the last decade, and they can tell you as much. But with the rapid development of AI and machine learning, concerns are growing for people about their career options, with a report from Randstad finding that 7 in 10 people in India are concerned about their job being eliminated by AI.

 Employers have their own share of concerns. According to The World Economic Forum, 97 million new AI-related jobs will be created by 2025 and the share of jobs requiring AI skills will increase by 58%. The IT industry in India is experiencing a tremendous surge in demand for skilled professionals on disruptive technologies like artificial intelligence, machine learning, blockchain, cybersecurity and, according to Nasscom, this is leading to a shortage of 600,000 profiles.

 So how do we fill those gaps? Can we democratize access to top-tier higher education in technology?

These are the questions that Riccardo Ocleppo, the engineer who founded a hugely successful ed-tech platform connecting international students with global Universities, Docsity, asked himself for years. Until he took action and launched the Open Institute of Technology (OPIT), together with the Former Minister of Education of Italy, Prof. Francesco Profumo, to help people take control of their future careers.

OPIT offers BSc and MSc degrees in Computer Science, AI, Data Science, Cybersecurity, and Digital Business, attracting students from over 38 countries worldwide. Through innovative learning experiences and affordable tuition fees starting at €4,050 per year, OPIT empowers students to pursue their educational goals without the financial and personal burden of relocating.

The curriculum, delivered through a mix of live and pre-recorded lectures, equips students with the latest technology skills, as well as business and strategic acumen necessary for careers in their chosen fields. Moreover, OPIT’s EU-accredited degrees enable graduates to pursue employment opportunities in Europe, with recognition by WES facilitating transferability to the US and Canada.

OPIT’s commitment to student success extends beyond academics, with a full-fledged career services department led by Mike McCulloch. Remote students benefit from OPIT’s “digital campus,” fostering connections through vibrant discussion forums, online events, and networking opportunities with leading experts and professors.

Faculty at OPIT, hailing from prestigious institutions and industry giants like Amazon and Microsoft, bring a wealth of academic and practical experience to the table. With a hands-on, practical teaching approach, OPIT prepares students for the dynamic challenges of the modern job market.

In conclusion, OPIT stands as a beacon of hope for individuals seeking to future-proof their careers in technology. By democratizing access to high-quality education and fostering a global learning community, OPIT empowers students to seize control of their futures and thrive in the ever-evolving tech landscape.

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