Mastering the Information Age to Solving Problems with Visual Analytics

The convergence of computing and communication has produced a society that feeds on information. Yet most of the information is in its raw form: data.

The amount of information hidden in databases—information is potentially important but has not yet been discovered or captured well.

The amount of data passing through Steemit alone keeps increasing everyday, with more than 170,000 users (as of May, 2017) and nothing less than 2bytes of data are being posted everyday, yet we don’t make good use of it.

Machine Learning is the process of making computer to act without following traditional programming, i.e

Machine learning enables computers to process, learn from, and draw an actionable insights out of the otherwise impenetrable walls of big data.
From the massive supercomputers that support Google's search engines to the smartphones that we carry in our pockets, we rely on Machine Learning to power most of the world around us—often, without even knowing it.

As modern pioneers in the brave new world of big data, it then behooves us to learn more about Machine Learning. What is Machine Learning and how does it work? How can I use Machine Learning to know when to exchange my SBD rate to USD , How can I use Machine Learning to take a glimpse into the unknown, power my business, or just find out what the Internet at large thinks about my favorite post? All of this and more will be covered in the subsequent posts.

I probably don't need to tell you that machine learning: a subfield in Artificial Intelligence has become one of the most exciting technologies of our time and age. Big companies, such as Google, Facebook, Apple, Amazon, IBM, and many more, heavily invest in machine learning research and applications for good reasons. Although it may seem that machine learning has become the buzzword of our time and age, it is certainly not hype. This exciting field opens the way to new possibilities and has become indispensable to our daily lives. Talking to the voice assistant on our smart phones, recommending the right product for our customers, stopping credit card fraud, filtering out spam from our e-mail inboxes, detecting and diagnosing medical diseases, the list goes on and on.

To become a machine learning practitioner, a data scientist, a better problem solver, or may be even consider a career in machine learning / Artificial Intelligence research, then following this trend will be good for you.

To enjoy the practical concepts behind the machine learning, one needs to have basic theoretical understanding behind machine learning especially for the novice, many practical books have been published in recent years and will help you to start a career in machine learning by implementing powerful learning algorithms. In my opinion, basic introduction to the theoretical concept and the use of practical code examples serve an important purpose. I will illustrate the concepts by putting the examples in the post directly into action. However, remember that with great power comes great responsibility!

The concepts behind machine learning are too beautiful and important to be hidden in a black box.