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Machine learning is increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.
With this course, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the course, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.
You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modelling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
About The Author
Prateek Joshi is an Artificial Intelligence researcher and a published author. He has over 8 years of experience in this field with a primary focus on content-based analysis and deep learning. He has written two books on Computer Vision and Machine Learning. His work in this field has resulted in multiple patents, tech demos, and research papers at major IEEE conferences.
His blog has been visited in more than 200 countries and has received more than a million page views. He has been featured as a guest author in prominent tech magazines. He enjoys blogging about topics such as artificial intelligence, Python programming, abstract mathematics, and cryptography.
He has won many hackathons utilizing a wide variety of technologies. He is an avid coder who is passionate about building game-changing products. He graduated from the University of Southern California and he has worked at companies such as Nvidia, Microsoft Research, Qualcomm, and a couple of early stage start-ups in Silicon Valley.
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