Machine Learning

Dear potential partner(s),

Thank you for your interest to work on Machine Learning with us. Machine Learning is a branch of artificial intelligence where computers use given data sets to learn. It involves the use of various statistical techniques to help the machines learn from the presented data and equip it with the ability to predict various outcomes. In other words, Machine Learning enables the machines to process information and act without explicit programming. Machine Learning depends on two major components. The components are signal and feedback, which are based on the expected outcomes in Machine Learning.

There are three main types of Machine Learning. These are supervised learning, unsupervised learning and reinforcement learning. Supervised learning involves presenting a machine with training data which are labeled as inputs so that a predictive model can be learned. From these inputs, the machine should learn and when presented with new data, it can give the desired predicted output. In other words, we know which is the correct answer beforehand when we train the model using supervised learning. The second type of Machine Learning, unsupervised learning, deals with letting the machine find out the structure of its unlabeled inputs, learn from it and produce meaningful output. In other words, we wont know which is the correct answer beforehand when we train the model using unsupervised learning – rather we are exploring the data to extract meaning. More recently, we also see a third type of Machine Learning which involve creating a self-learning system that access its environment to improves its functionality and performance. Some perfect examples of Machine Learning include effective search engines, human speech recognition, strategy games and self-driven cars among others.  

The future of Machine Learning is still vast. This is because technology keeps evolving. When Machine Learning has reached  the expected human intelligence level, it should be capable enough to take  practical undertakings rather than just theoretical functions. Some of the expected components in the future of Machine Learning may include building smart robotics with the ability to understand audio, text and video fully. Other key areas that require advancements in Machine Learning include the medical field, computer vision and software applications among others. Such areas require advancements in Machine Learning to ensure reliable and effective results that are a true representation of the repeated actions over time to give new ways of performing various tasks in world where we have to co-exist with machines.

Currently, we are open to work with all good partners involving in the Machine Learning revolution. If your are a conference / seminar organizer, you can even bring us in to talk to your clients. The only limit to a great partnership opportunity is your organizational imagination.

Contact us using this link.

Thanks and cheers!

Regards,

Alvin Lam, CEO of Massive Wisdom Group Pte Ltd, Singapore