Astonishing Hierarchy of Machine Learning Needs

Machine Learning Needs – Artificial intelligence and machine learning are often used interchangeably, but they are not the same. Machine learning is one of the most active areas and a way to achieve AI. Why ML is so good today? There are a couple of reasons. Machine learning entirely depends on algorithms of two kinds.

• Learning style
• Symmetry & similarity

This post only depicts the high-level summary to gain a simple, easy, and layman-friendly idea. This blog post is not for PhD students or experienced professionals in the industry, but for sure, it might just give them some smiling effects.

Machine Learning Outlook

Machine learning is the hottest subject of today’s time, and being a data scientist is the sexiest job of today, but implementing these buzzwords in real-life business is the most important need. 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 those listed below but not limited to them.

• 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 of today, sadly, most of the machine learning methods are based on supervised learning. Which means we still have a long way to go. In the Fintech domain, we say “machine learning” is the future (actually that future is now) of “e-commerce” & “data scientists” will work as batman for “fintech” & “and PureTech.” Today’s machines are learning and performing tasks that were only done by humans in the past, like making better judgments and decisions, playing games, etc.

Lets Define Machine Learning

Arthur Samuel coined it in 1959. He called it a “field of study that gives computers the ability to learn without being explicitly programmed.”

In our words, machine learning is a subject for real-life work outside of PhD or scholarly books. At AILabPage, we define ML as follows:

Machine learning is the hottest subject of today’s time, and being a data scientist is the sexiest job of today, but implementing these buzzwords in real-life business is the most important need.

Machine learning is a focal point where business needs and experience (mathematics, statistics, and algorithmic logic/thinking) meet emerging technology and decide to work together to put useful results on the table for real business.

Some History of Machine Learning

ML Arthur Samuel coined it in 1959. Some of the timelines of machine learning have evolved from an artificial intelligence subset to its own domain. It has reached an inflexion point, at least in terms of messaging. I remember in my school days, as part of statistics class, we were told something about AI and ML, and we laughed then, in the 1990s.

Although machine learning has now gained prominence owing to the exponential rate (1990 was the flying gear year) of data generation and technological advancements to support it, it has roots in the old days. Major discoveries, achievements, milestones, and other major events are included in the picture here.

Types of Machine Learning

The approach to developing ML includes learning from data inputs based on “what has happened”. Evaluating and optimizing different model results remains the focus here. As of today, machine learning is widely used in data analytics as a method to develop algorithms for making predictions on data. It is related to probability, statistics, and linear algebra.

Machine learning is classified into three categories at a high level, depending on the nature of the learning and the learning system.

1. Supervised learning: the machine gets labelled inputs and their desired outputs. The goal is to learn a general rule to map inputs to outputs.
2. Unsupervised learning: the machine gets inputs without desired outputs; the goal is to find structure in the inputs.
3. Reinforcement learning: This algorithm interacts with a dynamic environment and must perform a certain task without a guide or teacher.

In a hypothetical situation, or most of the time (at least from our personal experience), the amount of data anyone will find may look like the picture above. We are talking about the volume of the data. The volume of data for supervised learning is the highest, and for reinforcement learning, it’s the lowest almost all the time.

Data Science & Machine Learning

Thanks to statistics, machine learning became very famous in the 1990s. The intersection of computer science and statistics gave birth to probabilistic approaches in AI. This shifted the field further toward data-driven approaches. Machine learning is about the use and development of fancy learning algorithms. Data science is more about the extraction of knowledge (KDD) from data through algorithms to answer a particular question or solve particular problems.

In large-scale data-available systems, intelligent systems with the best-suited algorithms analyze, detect patterns, and learn to inform decisions and information. Machine learning helps data science by making provisions for data analysis, data preparation, and even decision-making. The word learning in machine learning means that the algorithms depend on some data, used as a training set, to fine-tune some model or algorithm parameters.

Machine learning is contained inside data science; every time an ML algorithm is used, the process is as good as doing data science. As explained above, ML algorithms learn from data, which is the essential fuel of data science. Not 100% accurate or correct, though, but comfortably, we can say machine learning and statistics are part of data science.

Hierarchy of Machine Learning Needs

It is important to note that excessively engaging in any activity can prove to be deleterious and result in adverse consequences. There is no guarantee that expediting tasks or achieving their entirety will lead to the improvement of machine learning and artificial intelligence.

The installation of a Ferrari engine into a non-specialized 2-wheel-drive automobile with the expectation of it producing a performance commensurate with that of a genuine Ferrari would be deemed impractical and unwise. Moreover, the employment of advanced data analytics software can hasten the accomplishment of intended outcomes. Nevertheless, there is no guarantee of precision.

There is a need to collect correct, accurate, quality, and relevant real-time data that is organized, clean, tested, and optimized. We may have to repeat this cyclic process many times, though. The time to test machine learning and artificial intelligence solutions arrives after much-required efforts.

Machine Learning – An Independent Domain

Artificial intelligence and machine learning are two very hot buzzwords right now and often seem to be used interchangeably. In my best knowledge and experience, machine learning is growing at top speed and going to make this statement true: “ML is not AI and has its own independent identity”. AI is all about smart devices and applications, but machine learning is all about learning and gaining accuracy. Maybe ML’s finished product can be called “AI.

Anyway, our intention here is not to justify or show the difference between these two buzzwords but to prove machine learning is and is going to be independent of AI. Machine learning would mature into a mature enough technique (or set of techniques) independently that it would not need AI as a superset of it.

Machine learning pertains predominantly to generalized artificial intelligence (AI), as opposed to applied AI. The discipline of machine learning cannot be classified as “the emulation of human intelligence through algorithms,” although it undeniably falls under the broader domain of artificial intelligence. The field of machine learning diverges considerably from the cognitive capabilities of natural intelligence, instead bearing a greater resemblance to a development procedure for synthetic models.

Machine Learning is not Magic

The complex amalgamation of various constituent elements, comprising data, mathematical computations, statistical analyses, algorithmic procedures, and computational proficiency, is conventionally referred to as “machine learning” in academic discourse.

The previously mentioned notion may be likened to the domain of wizardry, although it may also display similarities to the field of futurology, which involves the examination of feasible, credible, and attractive future possibilities. The examination of the fundamental perspectives and beliefs linked to machine learning is a subject of critical inquiry.

It is imperative to acknowledge that the progression of technology is not attributed to any mystical powers; instead, conventional tools such as MS Excel, Python, R, and cloud-based machine learning services from Azure and Amazon Web Services are utilized to eradicate such misconceptions. This topic exhibits an extraordinary level of clarity and simplicity, unencumbered by any necessity for memorization through repetition.

According to AILabPage, the aforementioned issue is unambiguous and as simple as consuming a serving of ice cream. This assertion is relevant not solely to individuals undertaking a doctorate program but also to all members of the community.

During a prior period, Microsoft embarked upon the integration of machine learning components onto the Azure platform. During that epoch, individuals experienced the sensation of being inundated and disorientated by the immense multitude of technological innovations and specialized jargon that permeated their environment.

The advent of Google’s TensorFlow and Cloud ML, coupled with Amazon’s foray into the realm of autonomous machine learning services, suggests a discernible trend towards the adoption of machine learning capabilities in the domain of cloud computing. The present state of affairs underscores the growing realization that machine learning is on the cusp of emerging as a substantial cloud computing phenomenon.

Real Life Use Cases

Machine learning is the process of a machine attempting to accomplish a task, independent of human intervention, more efficiently and more effectively with every passing attempt i.e learning phase.

At this point, AI- a machine which mimics the human mind, is still a pipe dream. In the middle, we have the meat of the pipeline, the model, which is the machine learning algorithm that learns to predict given input data.

ML is a sub-set of artificial intelligence (shouting for independence and getting there slowly) where computer algorithms are used to autonomously learn from data and information. Machine learning, however, has been a reality in our lives for quite some time.

Deep learning is a subcategory of machine learning algorithms that use multi-layered neural networks to learn complex relationships between inputs and outputs.

Machine Learning to bring – Data Intelligence as a Service

In the coming years, data intelligence services will be the most prominent application that will be provisioning prototypes for security measures to truly fortify DIaaS.

There are certain market dynamics that determine the growth of data and its related analytics. That’s where data intelligence’s adaptive dynamics come into play to assess the factors driving the organization to adapt their existing, profitable lines of business. This helps them stay relevant in the rapidly evolving future of the world and is an enormous help for the Blue Ocean Shift strategy.

In the context of machine learning, the conventional methodology involves generating distinct train-and-test partitions of the dataset, which may be accomplished through the use of cross-validation techniques. The process of loading all training data into the main memory, followed by the computation of a model from said data, may necessitate multiple passes. The present depiction is delineated in this communication.

Machine Learning use cases for Info-Security (#GDPR)

Machine learning can help automate more menial tasks previously carried out by IT or network security teams that are neither qualified nor skilled for this type of information security, especially in financial system data security.

In principle, machine learning can help businesses better analyze threats and respond to attacks and security incidents. The results of ML protecting data are astonishing, and ML as an info-security guard is much better than any human. Thusly, machine learning in information security involves quickly developing and detecting anomalies in patterns.

I guess we have now done enough drumming for machine learning and all those learnings around it; it’s time to take some action and get this done on the ground for our real and daily use.

The realm of machine learning is expanding at a notable pace and has emerged as a crucial tool in various domains, including web search, advertisement placement, credit assessment, and stock trading, among others.

Points to Note:

All credits, if any, remain with the original contributor only. We have now summarized our last four posts here to give a quick glimpse. You can find previous posts on machine learning (the helicopter view), supervised machine learning, unsupervised machine learning, and reinforcement learning links.

Books + Other readings Referred

• Research through the open internet, news portals, white papers, and imparted knowledge via live conferences and lectures.
• Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.
• Machine Learning – An Introduction 
• Machine Learning -A Probabilistic Perspective

Feedback & Further Question

Do you have any questions about supervised learning or machine learning? Leave a comment or ask your question via email. I will try my best to answer it.

Conclusion – This article explores the implementation of machine learning techniques utilizing computer algorithms to identify patterns within datasets. Such patterns are then used to make predictions and informed decisions in real-world scenarios. As a specific application, the utilization of machine learning within healthcare settings, specifically in genomics and preterm birth, is examined. The objective is to uncover latent topics within voluminous document repositories via topic modeling methodologies. The succeeding blog entries shall explicate the answers to the aforementioned inquiries.

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