The tradeoff between variance and bias is a fundamental concept in the field of machine learning, and it refers to the fact that there is always a balance to be struck between a model's ability to accurately capture the underlying structure of the data, and its...
During the past few years, we’ve been experiencing an upward trend in talent acquisition in the field of machine learning. Though this field has traditionally been considered as something that only institutions working with huge amount of resources could utilize, wide implementation of machine learning today has transformed the scenario completely. From e-commerce to software product to different business landscapes – machine learning is being implemented to a great extent. As a result, there’s a huge demand of machine learning professionals across industries, throughout the globe.
With exceptional emergence and implementation of big data and analytics, both AI and machine learning have become two buzzwords in the industry right now. And they often seem to be used interchangeably. However, they shouldn’t be considered as one thing since there’re some clear differences that make AI and machine learning separate. If you’re like a majority of the marketers, and are perhaps planning to any or both of these, it becomes all the more important to have a solid understanding of the differences between them.
With the heavy impact of artificial intelligence on almost every facet of society, there’s no doubt that businesses have already started harnessing the power of this technology. As a result, a huge demand of proper talents can be seen today. We all know that machine learning has the potential to change today’s business landscape but the speed of this transformation heavily depends on the availability of talents.
Data scientist has topped the list of best jobs in the U.S. for three years in a row, according to Glassdoor. Not only a huge demand exists for these professionals but there’s a significant amount of shortage too in getting qualified data scientists.
You may already know that machine learning is all about developing mathematical models in order to comprehend data. Here, a diverse range of technology and tools is used to identify patterns among large datasets to improve a knowledge base or a particular process. Though the concept of machine learning isn’t new, with the emergence of big data, the technology is gaining a huge momentum these days.
Machine learning refers to a data analytics technique, which teaches computers to perform what naturally comes to humans – learning from experience. The term was coined in 1959 by Arthur Samuel – an American pioneer in the fields of artificial intelligence and gaming. Machine learning is unquestionably the latest buzzword in the tech landscape as it’s one of the most interesting and promising subfields of computer science.
As the world started to acknowledge the true importance of artificial intelligence and machine learning, tech giants across the globe are riding this emerging tech wave. At some point of time, it was commonly believed that only smaller startups are generally more innovative and more dynamic than established and giant market leaders, but today this isn’t the case with artificial intelligence and machine learning. The main reason is that the development of innovative services and products is usually very expensive, and only companies with a great number of resources can afford to try that process out.
It is an effective machine learning modeling technique for classification and regression problems. To find solutions or possible results of a series of related choices, a decision tree makes hierarchical, sequential, decisions about the variable outcomes based on the predictor data.