Machine Learning – Do you want to get your feet wet in the wild world of machine learning? Well strap on your flippers, take a deep breath and dive in! We’ll take you through the basics of ML theory, showing you all the common concepts and techniques. So let’s get learning and have fun along the way!

Organizations should consider treating machine learning as a culture, where basic knowledge of ML and its terminology is shared among business teams, managers, and executives. Online courses abound which have been tailored for different individuals ranging from novices or students to managers and business executives alike to facilitate improved understanding This post is part 3 of Machine Learning (ML) – Basics you Need to Know.

Machine Learning – Outlook

As technology advances rapidly day by day; it has enabled machines today to learn and execute various activities like decision-making or judgment with accuracy & expertise along with excelling in gaming. By analyzing patterns and retaining learned information for future utilization, machines make this achievable. Although magnificent, implementing this bundle pack of ML for practical use in businesses is still a hurdle for many. ML’s current excellence can be explained by several reasons, such as those provided below although not exclusively..

  • 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

The main purpose of ML (machine learning) is to create an automatic data model for the purpose of analysis. Thus ML is to create a system that can learn from the data according to the algorithm used. The result can be found by mapping the output to the input or finding patterns/structures or learning by rewarding/punishing.

It is important to note that AI is not the same as Machine learning, although It is one of the tools included in the AI bundle for achieving artificial intelligence. For example, let’s say that AI typically defines and displays creativity in terms of the following:

  • Planning & Predicting
  • Learning & Adopting
  • Reasoning & Logic Building
  • Problem Solving & Avoiding
  • Knowledge Representation
  • Perception & Reasoning
  • Motion Detection and Manipulation

By utilizing the appropriate techniques, algorithms, and data sets, machine learning aids in the implementation of all the aforementioned. In the past, it was thought that human intellect could be properly characterized and that artificial intelligence (AI) could imitate it in computers. The machine has to learn from a lot of data before trying simulation.

Machine Learning – Integral Issues

Machine learning techniques  – These tools are growing almost daily with the aim of bringing good values to today’s businesses (ChatGPT is a wonderful example). It’s a unique game. It spans a vast territory and has affected almost every business domain/vertical.

ML of today can tackle tasks in language, video, image processing, anomaly detection, pattern recognitions, credit scoring, sentiment analysis, etc. This whole game and environment are entirely centered around artificial neural networks working like the human brain.

The ML hype, the abundance of information on the internet, and the use of ML terms by nearly every tech show have led to a ‘misinformation epidemic’ of ML. ML is changing the way we do business and what needs to be improved upon. ML creates its own comprehensive strategy based on the experience it has gained over time. Mathematics, Statistics, Programming and the common sense of human beings are now integral parts of machine learning.

Machine learning techniques come with benefits and drawbacks equally, but the disadvantage of supervised machines is their dependency on substantial amounts of labelled data. Labelling an abundance of samples requires considerable costs and consumes much time, although unsupervised learners tend to be less precise.  The main goal is to improve unsupervised machines and decrease the need for supervised ones.

You can view GANs and RLs means of improving unsupervised machines (neural networks). The strategy is learning from the experience it comes across over the period of time.

The correct information and little basic information about machine learning for non-tech and tech business professionals is the need of today. For business people, knowledge in the area of machine learning is less important than knowledge of their own business to create required and correct data models.

ML gives exposure to businesses to make data-driven, more informed and intelligent decisions after inputting of correct data from the business and not by creating complex and unnecessary use of relevant algorithms. Such decisions help to make faster and better results compared to traditional approaches. There are a lot of common mistakes that are made and should be avoided in order to successfully submerge machine learning in an analytics strategy for better business transformation. Some classic issues are 

Inadequate Infrastructure – As mentioned above the requirement of a powerful machine and accelerated hardware with high storage capacity and faster computing ability is a basic need in machine learning which is normally ignored. This a fatal problem and issue.

Data Quality Problems – The explosion of big data has created great hunger for new business and revenue streams in these shrinking times. What data is correct and what is not is the primary issue to resolve with a business goal. For example, an insurance company selling tooth insurance needs data on how teeth hygiene are maintained by customers along with brushing habit and brand of toothpaste used, time spent on brushing teeth and frequency rather than data on blood report and blood pressure.

Correct skilled resources – Implementing machine learning without qualified data scientists is the biggest issue of any business today. The cost of the skilled resource is pretty high, supply is very low and demand is big.

Implementation without Strategy – Advancements in ML algorithms have created a tsunami-like environment where businesses are running behind this buzzword.  Unfortunately, the challenge is big as not all corporates are able to make a decision about which strategy to be picked up at what time at this time of digital transformation. Not all business models need complex machine learning without proper analysis. Should machine learning be brought in the form of advancement to analytics or simple regression models are enough.

Points to Note:

All credits if any remains on the original contributor only. We have covered all basics around Machine Learning. It is all about data, computing power and algorithms to look for information. In the previous post, we covered Generative Adversarial Networks. A family of artificial neural networks.

Books + Other readings Referred

  • Research through open internet, news portals, white papers and imparted knowledge via live conferences & lectures.
  • Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.

Feedback & Further Question

Do you have any questions about AI and its bundle? Leave a comment or ask your question via email. Will try my best to answer it.

Machine Learning (ML) - Everything You Need To Know

Conclusion –  I particularly think that getting to know the types of machine learning algorithms actually helps to see a somewhat clear picture. The answer to the question “What machine learning algorithm should I use?” is always “It depends”. It depends on the size, quality, and nature of the data. Also, what is the objective/motive data torturing? As more we torture data more useful information comes out. It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have. To us, at AILabPage we say ML is crystal clear and ice cream eating task. It is not only for PhDs aspirants but it’s for you, us and everyone.

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Posted by V Sharma

A Technology Specialist boasting 22+ years of exposure to Fintech, Insuretech, and Investtech with proficiency in Data Science, Advanced Analytics, AI (Machine Learning, Neural Networks, Deep Learning), and Blockchain (Trust Assessment, Tokenization, Digital Assets). Demonstrated effectiveness in Mobile Financial Services (Cross Border Remittances, Mobile Money, Mobile Banking, Payments), IT Service Management, Software Engineering, and Mobile Telecom (Mobile Data, Billing, Prepaid Charging Services). Proven success in launching start-ups and new business units - domestically and internationally - with hands-on exposure to engineering and business strategy. "A fervent Physics enthusiast with a self-proclaimed avocation for photography" in my spare time.

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