The human brain is among the most complicated organs and one of nature’s most amazing creations. The brain’s capacity is considered limitless; there isn’t a thing it can’t remember. Although many often don’t think about it, the processes that happen in the mind are fascinating.


As technology evolved over the years, scientists figured out a way to make machines think like humans, and this process is called machine learning. Like cars need fuel to operate, machines need data and algorithms. With the application of adequate techniques, machines can learn from this data and even improve their accuracy as time passes.


Two basic machine learning approaches are supervised and unsupervised learning. You can already assume the biggest difference between them based on their names. With supervised learning, you have a “teacher” who shows the machine how to analyze specific data. Unsupervised learning is completely independent, meaning there are no teachers or guides.


This article will talk more about supervised and unsupervised learning, outline their differences, and introduce examples.


Supervised Learning


Imagine a teacher trying to teach their young students to write the letter “A.” The teacher will first set an example by writing the letter on the board, and the students will follow. After some time, the students will be able to write the letter without assistance.


Supervised machine learning is very similar to this situation. In this case, you (the teacher) train the machine using labeled data. Such data already contains the right answer to a particular situation. The machine then uses this training data to learn a pattern and applies it to all new datasets.


Note that the role of a teacher is essential. The provided labeled datasets are the foundation of the machine’s learning process. If you withhold these datasets or don’t label them correctly, you won’t get any (relevant) results.


Supervised learning is complex, but we can understand it through a simple real-life example.


Suppose you have a basket filled with red apples, strawberries, and pears and want to train a machine to identify these fruits. You’ll teach the machine the basic characteristics of each fruit found in the basket, focusing on the color, size, shape, and other relevant features. If you introduce a “new” strawberry to the basket, the machine will analyze its appearance and label it as “strawberry” based on the knowledge it acquired during training.


Types of Supervised Learning


You can divide supervised learning into two types:


  • Classification – You can train machines to classify data into categories based on different characteristics. The fruit basket example is the perfect representation of this scenario.
  • Regression – You can train machines to use specific data to make future predictions and identify trends.

Supervised Learning Algorithms


Supervised learning uses different algorithms to function:


  • Linear regression – It identifies a linear relationship between an independent and a dependent variable.
  • Logistic regression – It typically predicts binary outcomes (yes/no, true/false) and is important for classification purposes.
  • Support vector machines – They use high-dimensional features to map data that can’t be separated by a linear line.
  • Decision trees – They predict outcomes and classify data using tree-like structures.
  • Random forests – They analyze several decision trees to come up with a unique prediction/result.
  • Neural networks – They process data in a unique way, very similar to the human brain.

Supervised Learning: Examples and Applications


There’s no better way to understand supervised learning than through examples. Let’s dive into the real estate world.


Suppose you’re a real estate agent and need to predict the prices of different properties in your city. The first thing you’ll need to do is feed your machine existing data about available houses in the area. Factors like square footage, amenities, a backyard/garden, the number of rooms, and available furniture, are all relevant factors. Then, you need to “teach” the machine the prices of different properties. The more, the better.


A large dataset will help your machine pick up on seemingly minor but significant trends affecting the price. Once your machine processes this data and you introduce a new property to it, it will be able to cross-reference its features with the existing database and come up with an accurate price prediction.


The applications of supervised learning are vast. Here are the most popular ones:


  • Sales – Predicting customers’ purchasing behavior and trends
  • Finance – Predicting stock market fluctuations, price changes, expenses, etc.
  • Healthcare – Predicting risk of diseases and infections, surgery outcomes, necessary medications, etc.
  • Weather forecasts – Predicting temperature, humidity, atmospheric pressure, wind speed, etc.
  • Face recognition – Identifying people in photos

Unsupervised Learning


Imagine a family with a baby and a dog. The dog lives inside the house, so the baby is used to it and expresses positive emotions toward it. A month later, a friend comes to visit, and they bring their dog. The baby hasn’t seen the dog before, but she starts smiling as soon as she sees it.


Why?


Because the baby was able to draw her own conclusions based on the new dog’s appearance: two ears, tail, nose, tongue sticking out, and maybe even a specific noise (barking). Since the baby has positive emotions toward the house dog, she also reacts positively to a new, unknown dog.


This is a real-life example of unsupervised learning. Nobody taught the baby about dogs, but she still managed to make accurate conclusions.


With supervised machine learning, you have a teacher who trains the machine. This isn’t the case with unsupervised learning. Here, it’s necessary to give the machine freedom to explore and discover information. Therefore, this machine learning approach deals with unlabeled data.


Types of Unsupervised Learning


There are two types of unsupervised learning:


  • Clustering – Grouping uncategorized data based on their common features.
  • Dimensionality reduction – Reducing the number of variables, features, or columns to capture the essence of the available information.

Unsupervised Learning Algorithms


Unsupervised learning relies on these algorithms:


  • K-means clustering – It identifies similar features and groups them into clusters.
  • Hierarchical clustering – It identifies similarities and differences between data and groups them hierarchically.
  • Principal component analysis (PCA) – It reduces data dimensionality while boosting interpretability.
  • Independent component analysis (ICA) – It separates independent sources from mixed signals.
  • T-distributed stochastic neighbor embedding (t-SNE) – It explores and visualizes high-dimensional data.

Unsupervised Learning: Examples and Applications


Let’s see how unsupervised learning is used in customer segmentation.


Suppose you work for a company that wants to learn more about its customers to build more effective marketing campaigns and sell more products. You can use unsupervised machine learning to analyze characteristics like gender, age, education, location, and income. This approach is able to discover who purchases your products more often. After getting the results, you can come up with strategies to push the product more.


Unsupervised learning is often used in the same industries as supervised learning but with different purposes. For example, both approaches are used in sales. Supervised learning can accurately predict prices relying on past data. On the other hand, unsupervised learning analyzes the customers’ behaviors. The combination of the two approaches results in a quality marketing strategy that can attract more buyers and boost sales.


Another example is traffic. Supervised learning can provide an ETA to a destination, while unsupervised learning digs a bit deeper and often looks at the bigger picture. It can analyze a specific area to pinpoint accident-prone locations.



Differences Between Supervised and Unsupervised Learning


These are the crucial differences between the two machine learning approaches:


  • Data labeling – Supervised learning uses labeled datasets, while unsupervised learning uses unlabeled, “raw” data. In other words, the former requires training, while the latter works independently to discover information.
  • Algorithm complexity – Unsupervised learning requires more complex algorithms and powerful tools that can handle vast amounts of data. This is both a drawback and an advantage. Since it operates on complex algorithms, it’s capable of handling larger, more complicated datasets, which isn’t a characteristic of supervised learning.
  • Use cases and applications – The two approaches can be used in the same industries but with different purposes. For example, supervised learning is used in predicting prices, while unsupervised learning is used in detecting customers’ behavior or anomalies.
  • Evaluation metrics – Supervised learning tends to be more accurate (at least for now). Machines still require a bit of our input to display accurate results.

Choose Wisely


Do you need to teach your machine different data, or can you trust it to handle the analysis on its own? Think about what you want to analyze. Unsupervised and supervised learning may sound similar, but they have different uses. Choosing an inadequate approach leads to unreliable, irrelevant results.


Supervised learning is still more popular than unsupervised learning because it offers more accurate results. However, this approach can’t handle larger, complex datasets and requires human intervention, which isn’t the case with unsupervised learning. Therefore, we may see a rise in the popularity of the unsupervised approach, especially as the technology evolves and enables more accuracy.

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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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