Machine Learning with C++ هذه الدورة تدرس باللغة الإنجليزية

تفاصيل الدورة

ML has become a fundamental part of the 21st century; from Netflix recommendations to fraud detection, ML is ever- present in our daily lives. At its roots, ML effectively applies statistics and pattern recognition, we will use these ideas to help solve a range of modern-day problems. C++ is a very fast language to execute your code and is extensively used when your final models are being deployed. If you want to run a program, with a lot of array calculation then C++ should be your weapon of choice.

This course will start off with a broad overview of ML and the varying methods associated with it. You will understand data types, Machine Learning algorithms, and a simple classification task. We then study two simple but effective algorithms to deepen your understanding and provide some practical experience. Specifically, the two algorithms that we will be investigating are linear regression and K-means clustering.

By taking this course, you will be able to get your machine Learning basics right and be able to build efficient algorithms which will help you to predict and cluster data.

About the Author :

Colibri 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 better make sense of its data, and process it in more intelligent ways.

The company lives by its motto: Data -> Intelligence -> Action.

Tom 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) startup 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 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.