Welcome to ML BYTE!

Humans are on the verge of traditional computing where machine learning is the next big change. Every chore human is unique for is now imitated by machines in less or a greater degree. Remember the famous Industrial revolution in the 19th century? In this century, machine learning is the next big player of the game.

Machine learning is not among rocket sciences anymore. It has already fascinated us with face recognition technologies, busting out the spam messages, blocking tracked adverts, predict various health hazards and as a smarter voice assistant. Machine learning in its fully evolved state is much more exciting and awe wondering. So, what makes a machine learn something? How on Earth it is recognizing faces and doing all magic wizardry.And, the most import and the real question is…

DO MACHINES HAVE ‘REAL’ INTELLIGENCE?

No! Machine Learning is bit exaggeration in the literal sense. For the love of God, no machine has made a poetry or planned an invasion. So, What is the hype with ML? The Machine learning is the ability of a computer to learn (through statistics and algorithms) without being explicitly programmed.

Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.

Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to “produce reliable, repeatable decisions and results” and uncover “hidden insights” through learning from historical relationships and trends in the data.

As of 2016, machine learning is a buzzword, and according to the Gartner hype cycle of 2016, at its peak of inflated expectations. Effective machine learning is difficult because finding patterns is hard and often not enough training data is available; as a result, machine-learning programs often fail to deliver.

Source — Wikipedia

ABOUT ML BYTE

ML BYTE is a data science venture where Machine Learning algorithms are explored, explained and ensembled in layman’s fashion! We will explore datasets and post insights about hidden stories in complex data using python, scikit-learn, pandas, tensorflow and keras. Our datasets will be open sourced on GitHub and links to EDA (Exploratory Data Analysis) will be published in the EDA category.

We shall predict — the rise of temperature, results of cricket/sports matches, probabilities of diabetes, values of the stock market, recognize faces, process emotions based on text messages and much more…

So, drop your cocks and grab your socks cause as you are going to get ML’ed with bytes. Make sure you follow our facebook handle for upcoming updates and content. Stay curious!