Get yourself trained on Machine Learning with with this Online Training Machine Learning with C++ : Choosing the Right Algorithm.
Online Training Machine Learning with C++ : Choosing the Right Algorithm
Machine Learning is a field of Computer Science that involves the use of various methods for pattern recognition and computational learning from data sources. This tutorial will introduce you to the fundamentals of Machine Learning and show you how you can utilize your C++ skills to build efficient data models.We start by discussing the suitability of different algorithms for different situations; this often involves inspecting data and then making an informed decision based on prior experience and knowledge. We will then implement popular supervised and unsupervised machine learning algorithms in C++ with the help of practical examples. The coursealso includes videos on tuning and optimizing the model for different use cases, and a quick introduction to neural networks and deep learning.During the course of this tutorial, you will work with different types of C++ library used for Machine Learningsuch as mlpack, Shark, and so onto solve different kinds of problem. By the end, you will be able to efficiently extend your knowledge of C++ to build a solid foundation in the field of Machine Learning.About The AuthorsColibri is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help their clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, Machine Learning, and cloud computing.Over the past few years, they have worked with some of the world’s largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the world’s most popular soft drinks companies, helping each of them to make better sense of its data, and process it in more intelligent ways.The company lives by its motto: Data -> Intelligence -> Action.Tom Joy has just graduated from the University of Oxford with a degree in Engineering Science. He is currently working for a SLAM (Simultaneous Localization and Mapping) start-up as a research and development engineer. He is about to start a PhD at the University of Oxford in Semantic SLAM, which is the process of simultaneously localizing a robot in space, producing a map/understanding of the surrounding area, and also detecting and delineating objects in 3D space. Achieving this requires a high level of competency in computer vision, Machine Learning, and optimization.Tom Joy has extensive experience in computer vision and Machine Learning, having taken several internships and placements over the course of his degree. He is a big advocate of explaining concepts simply and in a clear and concise manner, and strives to obtain and provide a comprehensive understanding of all relevant methods to the task at hand.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.