Machine Learning – Introduction to Supervised Learning

Supervised learning – A blessing we have in this machine era. It helps to depict inputs to outputs. It uses labelled training data to deduce a function which has a set of training examples. The majority of practical machine learning uses supervised learning as of date. As AILabPage, our experience revolves around exploring and demystifying the wonders of artificial intelligence, particularly focusing on supervised learning. Supervised learning is like a guiding light in the realm of machines, helping us understand how computers learn and make decisions.

In simple terms, supervised learning is about teaching computers through examples. Imagine showing a computer lots of pictures of cats and dogs, and telling it which is which. Over time, the computer learns to recognize cats and dogs on its own, even in new pictures it hasn’t seen before. That’s the magic of supervised learning!

At AILabPage, we are passionate about sharing the excitement of supervised learning and how it’s shaping the future of technology. So, if you’re curious about how computers learn or want to discover the latest AI breakthroughs, you’re in the right place!

AILabPage defines “Machine Learning” as “A focal point where business, data, and experience meet emerging technology and decide to work together.“

What is Machine Learning

Machine learning is also a subset of Artificial Intelligence. ML borrows principles from computer science and statistics which is a graphical branch of mathematics. The real need for today’s time and business is to clarify, demonstrate and extract real values to benefit everyone from this golden keyword “Machine Learning”. Why ML is so good today; for this, there are a couple of reasons like below but not limited to though.

• The explosion of big data
• Hunger for new business and revenue streams in this business shrinking times
• Advancements in machine learning algorithms
• Development of extremely powerful machine with high capacity & faster computing ability
• Storage capacity

As on date sadly most of the machine learning methods are based on supervised learning. Which means we still have a long long way to go. In Fintech domain we say #MachineLearning is the future (actually that future is now) of #Ecommerce & #DataScientist will work as a batman for #FinTech & #InsureTech. Today’s machines are learning and performing tasks; that was only be done by humans in the past like making a better judgement, decisions, playing games etc.

It instructs an algorithm to learn for itself by analysing data. The more data it processes, the smarter the algorithm gets. Until only recently even though the foundation was laid down in 1950; ML remained largely confined to academia. Sadly it’s becoming more accessible to developers as their tool. What we need is simply an MLaaS (Machine Learning as a Service) for everyone.

Machine Learning Types

The difference between supervised, unsupervised and semi-supervised learning. Let’s see one-liners as below

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Machine Learning – Some Use Cases

ML have strengthened organizations and awarded success with each type of learning. Making the right choice of technique to be used for business problem requires a strong understanding of the field.  Evaluating which conditions are best suited for each approach is another task. Types of machine learning algorithms i.e. MLAlgos and which one to be used when is extremely important to know.

The goal of the task and all the things that are being done in the field and put you in a better place to break down a real problem and design a machine learning system.

What is SML – Supervised Machine Learning?

As per Wiki – In supervised learning, each example is a pair consisting of an input object (typically a vector) and the desired output value (also called the supervisory signal).

AILabPage defines , “Supervised Machine Learning” as an essential aspect of artificial intelligence that focuses on teaching computers to make predictions or decisions based on labelled data. In simpler terms, it’s like teaching a computer by showing it examples with clear instructions.

SML through historical data sets can hunt for correct answers, and the task of the algorithm is to find them in the new data. Supervised Machine Learning is

• Is a task of deducing function from labelled training data.
• Making predictions based on evidence in the presence of uncertainty
• Identifying patterns in given data with adaptive algorithms

In supervised machine learning, each example is sorted in a pair consisting of an input object and the desired output value. An algorithm in this domain analyses the training data and produces an inferred function, which can be used for mapping new examples. The training data consist of a set of training examples.

How Supervised Machine Learning Works

In SML, you provide the computer with a bunch of examples where you already know the correct answer. For instance, if you’re teaching it to recognize animals, you’d show it pictures of cats and dogs along with labels saying which is which.

The detailed steps for supervised learning processes are included but not limited though as directed in below graphics

The computer then learns from these examples, trying to figure out the patterns or rules that distinguish one thing from another. Once it’s trained on enough examples, you can give it new, unlabeled data, and it will make predictions based on what it’s learned.

SML is incredibly versatile and is used in various real-world applications, such as spam email detection, medical diagnosis, and even self-driving cars. It’s like having a smart assistant that learns from experience and gets better at its job over time.

While we meticulously detail the well-known supervised methods, AILabPage’s aim isn’t to assert definitive conclusions. Instead, our goal remains to furnish a nuanced comprehension of supervised learning, spotlighting fundamental concepts rather than offering a mere catalogue of algorithms within this domain.

So, in short, Supervised Machine Learning is all about teaching computers to learn from labelled data and make predictions or decisions based on that learning. The process for Supervised Machine Learning is basically a two-step process as below.

• Learning – Learn a model using the training data or train model using training data.
• Testing – Test the model using unseen test data to assess the model accuracy

Let’s take an apple as an example of this learning process. Lets assume we have our fruit basket and we call it as our fruit basket. Now to pick an apple from our basket below process at a high level would work perfectly.

• From our fruit basket, we collect data like size, colour, weight, skin type, and shape etc of all the fruits.
• After collecting the data we start classifications
  • If size is Big, colour is red, the shape is rounded shape with a depression at the top and bottom put it in set-1
  • If skin type is smooth and shiny on set-1 fruits; put it in set-2
  • Set-2 data can now be comfortably labelled as apple will be put in the apple group.

In short, AILabPage from its hands-on experience “Supervised learning is that learning comes from known label data to create a model rather than predicting the target class as output for the given input data”. Supervised learning is also known as a data mining task, and it’s used for inferring a function from labelled training data.

Real-Life Business Uses Cases for Supervised Learning

In real life, supervised learning is incredibly useful. For instance, it helps banks predict when customers might leave, or doctors diagnose illnesses. It’s also behind those personalized recommendations you get while shopping online. And in cybersecurity, it can help spot and stop potential threats. The supervised learning model makes predictions based on evidence in the presence of uncertainty. Some of the use cases for supervised learning are depicted in the below picture.

Overall, supervised learning is like giving machines a teacher to learn from, helping them become smarter and more helpful in many different ways.

Supervised Learning Algorithms

The main job of any supervised learning algorithm is to analyse the training data as the first step. In the second step deduce the function which can be used for depict new examples. It has “labelled” data for creating predictive models by using either type of ml algorithms as mentioned below.  It provides outputs typically in one of two forms.

• Regression outputs are real-valued numbers that exist in a continuous space.
• Classification outputs, on the other hand, fall into discrete categories.

As shown above problems under classification (binary or multi-class) and regression come under supervised learning. Some of the algorithms are mentioned below.

• Linear regression
• Logistic Regression
• Polynomial regression
• SVM for regression
• Decision trees
• Random forest
• Support vector machine (SVM)
• Naive Bayes
• k-Nearest Neighbours

Focused Use Cases Under Supervise Learning

Here will focus mainly on 4 main problems that should be considered for supervised learning. In below use cases it’s simple and easy to collect data, label data and make predictions with accuracy. AILabPage did a small survey among artificial intelligence experts to outline some facts around machine learning for personal use and blog sharing. While there were only 23 respondents out of 30 who voted to confirm what was evident already. Below use cases came out as focus areas.

People analytics

Analytical tools are being embedded into day-to-day decision-making. A new paradigm shift in HR on People Analytics has brought revolutionary transformation.

• Existing Task Force: Almost all respondents agreed this is extremely important for companies to invest in to understand their people better. Performance measurement, retention, and predicting who is on an outward path.
• New Task Force: Recruitment remains the No. 1 area as of now to understand workforce planning, compensation benchmarking, and detecting suspicious items in CV’s.

Info and Cyber Security 

Threat hunters or threat analysts roles came up recently. Skilled resources are now upgrading their skills and knowledge in areas like network administration or network engineering with an artificial intelligence and machine learning blend.

Health Care 

Supervised learning in healthcare provides practical information on how to cut health care costs, diagnose problems, and develop successful solutions. This is still struggling to gain attention for mainly two reasons, i.e., regulations and litigation.

• Decision Support: Supervised learning-based systems for medical imaging recognition greatly aid in the work of radiologists and anatomical pathologists.
• Machine learning in medicine has recently made headlines. Google has developed a machine learning algorithm to help find cancerous tumors on mammograms.

Stanford is using a deep learning algorithm to find skin cancer. Also, supervised learning methods are becoming extremely popular in the health insurance industry for predicting healthcare costs.

Financial Technology (FinTech) 

Data science in FinTech deals with both structured and unstructured data. Supervised learning provides insights in a well-organized way that combines programming, logical reasoning, mathematics, and statistics.

• Digital Age of Financial Transactions: As smartphones become a bigger part of our everyday lives, it’s only natural that we will use them more and more for shopping. Studies seem to back up this simple reflection. People spend prediction attributes like how much, when, which channel, and on what are some examples here.
• Supervised learning to demystify FinTech: SL algorithms are built through which input is received and, after statistical analysis, the output value is predicted. Because the algorithms are trained on the dataset and thus learn from it,  improved results are finally predicted. Furthermore, improved functionality of systems and markets.

Here, machines are able to learn and gain knowledge of internal and external information vulnerabilities and do  mapping against real-world cyber attacks.

Past and Future of Threats and Protection The year 2017 was dominated by news of major hacks, cybersecurity threats, and data breaches. What will 2018 have in store? Cybersecurity threats and data breaches are on the rise. What will 2019 bring?

Beating the baddies: In the info-security industry, which comes first with leadership roles and best-developed products and excellent professional services, this will be known as the winner. Yet the researchers say the technology may also be used to beat baddies at their own game.

Common Examples of Supervised Learning

Supervised Learning is as good as low-hanging fruit in data science for businesses. The key question when dealing with ML classification is not whether a learning algorithm is superior to others, but under which conditions a particular method can definitely outperform others on a given problem.

Conclusion – Supervised Learning which is one of three types of machine learning. Within our AILabPage’s lab, we’ve centered our exploration on Supervised Learning, a pivotal facet among the trio of machine learning types. This focus has allowed us to delve deeply into its intricacies and its potential to serve as a transformative force for businesses, likening its impact to that of a contemporary electricity, vitalizing enterprises. Our research endeavors have primarily revolved around a comparative analysis of various supervised machine learning techniques, particularly in their applicability to classifying FinTech data.

#MachineLearning  #SupervisedLearning

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Books + Other readings Referred

• Research through Open Internet – NewsPortals, Economic development report papers and conferences.
• Internet-based survey results from 30 AI experts
• Personal experience of  @AILabPage members.

Points to Note:

All credits if any remains on the original contributor only. We have covered supervised machine learning where we make predictions from labelled historical data. In the past post, we have walked through unsupervised machine learning.

Feedback & Further Question

Do you have any questions about Supervised Learning or Machine Learning ? Leave a comment or ask your question via email . Will try my best to answer it.

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