Right now, Artificial intelligence (AI) and Machine Learning (ML) are transforming the world of business and commerce as we know it. We cannot escape this inevitable change. AI and ML are disrupting industries and organisations as you read this. All of us should understand the basics of this fast-evolving field and consider the implications for our own lives, our careers and the organisations where we work. The International Leadership Development Programme from Regenesys Business School recognises this crucial opportunity for businesses and their leaders, and an intensive insight into the worlds of AI and ML from a bases for sounds leadership training through the programme.

In this, one of a series of articles on the subject, we look at Machine Learning (ML) as an effective means that may be employed by leaders and future leaders to enhance, develop, streamline and grow their businesses, right up to an international scale.

Artificial Intelligence and Machine Learning


Machine learning is the process of teaching a computer system to make accurate predictions using the data available to it. It is different to traditional computer software in that a human developer hasn’t written code that instructs the system what to do. The machine learns by itself! ML consists designing and applying algorithms that are able to learn from previous situations. The Regenesys Business School has recognised this need, and has imported key spects of Machine Learning into the International Leadership Development Programme.

ML is used for credit card fraud detection, self-driving cars and face detection and recognition. It uses complex algorithms that constantly go back and forth over large data sets, analysing patterns in the data and then instructing other  machines to respond to situations for which they have not been explicitly programmed. The machines learn from the history to produce reliable results.

Machine learning models are able to give accurate predictions in order to create real value for an organisation. In machine learning prediction refers to the output of an algorithm after it has been trained on a historical dataset and it is applied to new data when forecasting the likelihood of a particular outcome, such as whether or not a customer will stop using a product or service 30 days. It helps to find the best model that represents our data and how well this model will work in the future.

Data scientists use machine learning to solve critical business problems. They extract valuable and actionable insights from big data. You have seen machine learning at work without being aware of it. Machine learning is used in internet search engines, email filters to sort out spam, websites to make personalised recommendations, banking software to detect unusual transactions, and lots of apps on our phones such as voice recognition.

No matter what industry you are now working in, machine learning is rapidly changing the way things get done. Now is the time to take courageous decisions to build your skills so you don’t get left behind.

Types of ML

There are two types of ML:

Supervised Learning

In supervised learning, training datasets are provided to the system. Supervised learning algorithms analyse the data and produce an inferred function. This function is then applied on new situations.

Supervised machine learning is -for example – where a computer is given a large number of photographs. Along with the photographs is information about what is on them. The computer analyses, sorts, and interprets what is on the photographs. This is called training. You then give the computer new photographs without the accompanying information, and let it analyse, sort and interpret them, using the learning from the previous set.

Supervised learning can be used in sorting oranges to identify fruit that does not conform to a particular visual standard. It is also useful for decision support: a computer can be trained with large amounts of information about actual and historic share market transactions. It can then be used to assess how effective a intended transaction will be. Credit card fraud detection is another example of supervised learning algorithms in practice.

Unsupervised Learning

Unsupervised learning algorithms are more complex because the data is unsorted. The machine learn on its own without any supervision. The solution of any problem is not provided. The algorithm has to find the patterns in the data. An example of supervised learning is recommendation engines which give you a selection of options on e-commerce sites.  Facebook’s friend suggestion mechanism is another example.

To put it simply, in unsupervised machine learning, you give a computer a number of photographs of each of 5 different people. The computer must interpret the visual imagery to separate them into 5 different piles, one pile for each person. Unsupervised learning is useful for clustering, and there are many applications for customer segmentation in marketing.

One industry commentator has estimated that there were fewer than 10,000 people around the world who have the necessary skills to create fully-functional machine learning systems. Machine learning engineers capable of developing these systems must be skilled in computer science, programming, mathematics, statistics, data science, deep learning, and problem solving.

Reinforcement learning is the training of machine learning models to make a sequence of decisions. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximise the total reward.

Machine learning is an increasingly integral component of solving business problems. One of the really useful ways it can help organisations is by telling us about what is likely to happen in the future.

Supervised Learning and Unsupervised Learning (Reference: http://dataconomy.com/whats-the-difference-between-supervised-and-unsupervised-learning/) 

AI and Ml have reached industries like Customer Service, E-commerce, Finance and where not. By 2020, 85% of the customer interactions will be managed without a human (Gartner).


The International Leadership Development Programme (ILDP) from Regenesys Business School comprises of a world-class, innovative and disruptive curriculum that develops leadership competencies for the new digital world, and included modules on Artificial Intelligence and Machine Learning.

The intense programme explores cutting-edge knowledge, global trends and best practice in digital transformation, entrepreneurship, strategy, and innovation from the fastest-growing companies in the USA.

Participants are exposed to applied learning using innovative learning techniques, master classes, exposure to giants of industry, focused networking opportunities and commercial

matching to sector-level opportunities. The programme is facilitated by global faculty comprising experts in specialised fields as well as top business leaders.

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