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Self-Driving Car Engineer

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Course Report - Self-Driving Car Engineer

Course Report

Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.

Course Features

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Duration

5 months

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Advanced

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Effort

10 hours per week

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Teaching Type

Self Paced

Course Description

This course will teach you how to power self-driving cars using all the capabilities of autonomous vehicles. You will learn computer vision and deep-learning to solve perception problems such as lane finding and classification of traffic signs. Additionally, you will be able to create a complete end-to-end algorithm that allows for behavioral cloning. Additionally, you will learn how to track objects using radar and lidar data. You will then learn how to implement the concepts of localization, path planning, and control. This will ensure that your vehicle is aware of its surroundings and can navigate through them.

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Highlights

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Hands on training

Top 30 Percentile

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Pedagogy

Top 1 Percentile

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Career Impact

Top 1 Percentile

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Course Credibility

Top 1 Percentile

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Parameters

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Course Credibility

Delivered through Udacity a renowned institution in the field, this course offers a comprehensive learning experience.

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Pedagogy

This comprehensive course equips you with all major Robotics Process Automation skills applicable to your daily life. Personalized teaching ensures one-on-one doubt resolution with faculty, maximizing skill acquisition. These practical skills empower you to confidently apply your knowledge and thrive in various real-life situations. An exceptional course in Robotics Process Automation, this stands out for its Self Paced learning approach. Learners have the flexibility to progress at their own speed, tailoring the experience to their individual needs.

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Hands on training

Inclusion of the capstone project and hands-on training offers a dual focus. This enriches its value, making it an ideal choice for those seeking comprehensive learning and real-world application in Robotics Process Automation.

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Career Impact

This course is exceptional, ranking among the top 1 percentile in Robotics Process Automation for its significant career impact and excellent job assistance. Learners benefit from valuable career opportunities and support, enabling them to secure relevant positions and excel in the industry. The course's dual focus on career impact and job assistance enhances its value, making it an ideal choice for individuals seeking to advance their careers and succeed in the Robotics Process Automation field.

Course Overview

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Job Assistance

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Personlized Teaching

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

Prerequisites/Requirements

Build object-oriented programs in any language (ideally Python or C++)

Compute integrals and derivatives of polynomial functions

Multiply matrices and understand related aspects of linear algebra

Calculate mean, median, and standard deviation of a dataset

Model the effects of forces on point masses

What You Will Learn

Develop critical Machine Learning skills

Learn about the Lidar sensor and its role in the Autonomous Vehicle Sensor suite

Learn all about Robotic Localization

Apply Model-Driven and Data-Driven approaches to predict how other vehicles on the road will behave

Target Students

Anyone who meets the eligibility criteria can join this course

Course Instructors

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Thomas Hossler

Sr. Deep Learning Engineer

Thomas is originally a geophysicist but his passion for Computer Vision led him to become a Deep Learning engineer at various startups. By creating online courses, he is hoping to make education more...
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Antje Muntzinger

Self-Driving Car Engineer

Antje Muntzinger is a technical lead for sensor fusion at Mercedes-Benz. She wrote her PhD about sensor fusion for advanced driver assistance systems and holds a diploma in mathematics. By educating ...
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Andreas Haja

Professor

Andreas Haja is an engineer, educator and autonomous vehicle enthusiast with a PhD in computer science. Andreas now works as a professor, where he focuses on project-based learning in engineering. Du...
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Aaron Brown

Senior AV Software Engineer

Aaron has a background in electrical engineering, robotics and deep learning. Currently working with Mercedes-Benz Research & Development as a Senior AV Software Engineer, he has worked as a Content ...

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