in 2005 for the first time in human history an autonomous vehicle traveled 132 miles through the Mojave Desert to win the 2 million dollar DARPA Grand Challenge the name of the car was Stanley and it used a computer vision Library called opencv hello everybody I'm Satya Malik and I'm thrilled to help you get started with opencv built over 20 years opencv is the most extensive computer vision library in the world it is downloaded between one to two million times a week and it contains over 2 500 optimized algorithms to build computer vision and AI applications opencv is the first Library you need to learn welcome to this free getting started series we designed it for absolute beginners all you need is an intermediate level of programming knowledge in Python opencv is vast it is not possible to cover all aspects of the library in the short period of time this series is your first step it will help you get started once you complete this series if it Sparks your interest in computer vision and AI you should check out our courses at opencv.org slash courses our courses in computer vision and deep learning take you from the very Basics to Mastery in computer vision these courses are designed for beginners we emphasize building real world applications and we do it without drowning you in mathematical details but today let's get started with opencv we will begin with quick installation instructions the material in the series will be covered using jupyter notebooks we will go over every notebook in a video to help you understand the code the first few notebooks are all about basics what are images and videos how do we represent them inside opencv what opencv functions do we use to read write and manipulate photos and videos next we will go over image enhancement and filtering our objective in this series is also to give you a glimpse into applications you can build using opencv functions we will go over different image Transformations and show you how to align two images the same idea can be modified slightly to create beautiful panoramas next we will dip our toes into computational photography and create high dynamic range images by combining photos taken using different exposures into one single beautifully lit photo opencv also implements many classical machine learning algorithms and has an entire module dedicated to deep learning inference we will learn how to implement face detection and object tracking finally we will wrap up the series by learning how to use deep learning module for object detection and pose estimation it's going to be very interesting that's all we will cover in this getting started series and after completing this series I encourage you to go and take a look at the free content at opencv.org and learn opencv.com and when you're ready for structured learning and you're seeking Mastery and computer vision and AI check out our courses at opencv.org courses I wish you all the best in your learning path thank you