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David Silver,
Thomas Hossler,
Antje Muntzinger,
Andreas Haja,
Aaron Brown,
Munir Jojo Verge,
and
Mathilde Badoual
In this course, you will develop critical Machine Learning skills that are commonly leveraged in autonomous vehicle engineering. You will learn about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models. This course will focus on the camera sensor and you will learn how to process raw digital images before feeding them into different algorithms, such as neural networks. You will build convolutional neural networks using TensorFlow and learn how to classify and detect objects in images. With this course, you will be exposed to the whole Machine Learning workflow...
Read more
In this course, you will develop critical Machine Learning skills that are commonly leveraged in autonomous vehicle engineering. You will learn about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models. This course will focus on the camera sensor and you will learn how to process raw digital images before feeding them into different algorithms, such as neural networks. You will build convolutional neural networks using TensorFlow and learn how to classify and detect objects in images. With this course, you will be exposed to the whole Machine Learning workflow...
Read more
In this course, you will develop critical Machine Learning skills that are commonly leveraged in autonomous vehicle engineering. You will learn about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models. This course will focus on the camera sensor and you will learn how to process raw digital images before feeding them into different algorithms, such as neural networks. You will build convolutional neural networks using TensorFlow and learn how to classify and detect objects in images. With this course, you will be exposed to the whole Machine Learning workflow and get a good understanding of the work of a Machine Learning Engineer and how it translates to the autonomous vehicle context.
Register for this course and see more details by visiting:
OpenCourser.com/course/9lpgoi/computer
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What's inside
Syllabus
Dive into Deep Learning for Computer Vision, learning about its use cases, history, and what you’ll build by the end of the course.
Machine learning is more than just building a model - getting each step of the workflow right is crucial.
Read more
Syllabus
Dive into Deep Learning for Computer Vision, learning about its use cases, history, and what you’ll build by the end of the course.
Machine learning is more than just building a model - getting each step of the workflow right is crucial.
Read more
Learn how to calibrate your camera to remove distortions for improved perception.
Build skills in linear and logistic regression before taking on feedforward neural networks, a type of deep learning.
Convolutional networks improve on feedforward networks for areas such as image classification - let’s get started building them!
Object detection builds on classification by finding multiple important objects within a single image - find out how!
Use the Waymo dataset to detect objects in an urban environment.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Core skills for autonomous vehicle engineers are developed
A focus on the critical Machine Learning essential to autonomous vehicles
Taught by experts in the autonomous vehicle industry
Develops the whole Machine Learning workflow
Leverages the popular TensorFlow framework
Covers convolutional neural networks, the state-of-the-art in image classification and object detection
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Computer Vision with these
activities:
Attend a Local Machine Learning Meetup
Show steps
Network and learn from Machine Learning professionals.
Browse courses on
Networking
Show steps
-
Find a local Machine Learning meetup group.
-
Attend a meetup and introduce yourself.
-
Participate in discussions and ask questions.
Reach Out to Machine Learning Practitioners
Show steps
Gain insights and guidance from experts in the field.
Show steps
-
Attend industry events and meetups.
-
Connect with Machine Learning professionals on LinkedIn.
-
Ask for informational interviews or guidance.
-
Follow up and maintain connections.
Form a Study Group with Classmates
Show steps
Collaborate, discuss concepts, and enhance understanding.
Show steps
-
Approach classmates and propose forming a study group.
-
Schedule regular meetings and set clear goals.
-
Discuss course material, share perspectives, and work on problems together.
-
Provide support and encouragement to other group members.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Review Course Materials Regularly
Show steps
Reinforce concepts and reduce forgetting.
Show steps
-
Set aside time each week to review lecture notes, slides, and assignments.
-
Summarize key concepts and ideas in your own words.
-
Make flashcards or use spaced repetition software to aid memorization.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Gain a more comprehensive understanding of Machine Learning concepts.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Start by reading the introduction and overview.
-
Go through the chapters on data preprocessing, model training, and evaluation.
-
Follow along with the code examples and try them out on your own.
Build a Convolutional Neural Network using Keras
Show steps
Develop hands-on skills in building and training CNNs.
Browse courses on
Convolutional Neural Networks
Show steps
-
Find a tutorial on building a CNN for image classification.
-
Follow the tutorial step-by-step, implementing the code yourself.
-
Experiment with different CNN architectures and hyperparameters.
Solve Machine Learning Problems on LeetCode
Show steps
Enhance problem-solving abilities and apply ML concepts.
Browse courses on
Problem Solving
Show steps
-
Sign up for a LeetCode account.
-
Select problems related to Machine Learning.
-
Attempt to solve the problems yourself.
-
Review solutions and learn from alternative approaches.
Create Visualizations for Object Detection Data
Show steps
Reinforce your understanding by creating visual representations.
Browse courses on
Object Detection
Show steps
-
Choose a dataset with object detection annotations.
-
Load the dataset and explore the data.
-
Create visualizations of the data, such as bounding boxes and heatmaps.
-
Use the visualizations to analyze the performance of object detection models.
Build an Object Detection System for a Specific Domain
Show steps
Apply ML skills to solve real-world problems.
Browse courses on
Project Building
Show steps
-
Identify a specific domain for object detection (e.g., medical imaging, manufacturing).
-
Collect and prepare a dataset for the domain.
-
Choose and train an object detection model.
-
Deploy the object detection system and evaluate its performance.
Attend a Local Machine Learning Meetup
Show steps
Network and learn from Machine Learning professionals.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Find a local Machine Learning meetup group.
- Attend a meetup and introduce yourself.
- Participate in discussions and ask questions.
Reach Out to Machine Learning Practitioners
Show steps
Gain insights and guidance from experts in the field.
Show steps
Show steps
Show steps
- Attend industry events and meetups.
- Connect with Machine Learning professionals on LinkedIn.
- Ask for informational interviews or guidance.
- Follow up and maintain connections.
Form a Study Group with Classmates
Show steps
Collaborate, discuss concepts, and enhance understanding.
Show steps
Show steps
Show steps
- Approach classmates and propose forming a study group.
- Schedule regular meetings and set clear goals.
- Discuss course material, share perspectives, and work on problems together.
- Provide support and encouragement to other group members.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Review Course Materials Regularly
Show steps
Reinforce concepts and reduce forgetting.
Show steps
Show steps
Show steps
- Set aside time each week to review lecture notes, slides, and assignments.
- Summarize key concepts and ideas in your own words.
- Make flashcards or use spaced repetition software to aid memorization.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Gain a more comprehensive understanding of Machine Learning concepts.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Start by reading the introduction and overview.
- Go through the chapters on data preprocessing, model training, and evaluation.
- Follow along with the code examples and try them out on your own.
Build a Convolutional Neural Network using Keras
Show steps
Develop hands-on skills in building and training CNNs.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Find a tutorial on building a CNN for image classification.
- Follow the tutorial step-by-step, implementing the code yourself.
- Experiment with different CNN architectures and hyperparameters.
Solve Machine Learning Problems on LeetCode
Show steps
Enhance problem-solving abilities and apply ML concepts.
Browse courses on
Problem Solving
Show steps
Show steps
Show steps
- Sign up for a LeetCode account.
- Select problems related to Machine Learning.
- Attempt to solve the problems yourself.
- Review solutions and learn from alternative approaches.
Create Visualizations for Object Detection Data
Show steps
Reinforce your understanding by creating visual representations.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Choose a dataset with object detection annotations.
- Load the dataset and explore the data.
- Create visualizations of the data, such as bounding boxes and heatmaps.
- Use the visualizations to analyze the performance of object detection models.
Build an Object Detection System for a Specific Domain
Show steps
Apply ML skills to solve real-world problems.
Browse courses on
Project Building
Show steps
Show steps
Show steps
- Identify a specific domain for object detection (e.g., medical imaging, manufacturing).
- Collect and prepare a dataset for the domain.
- Choose and train an object detection model.
- Deploy the object detection system and evaluate its performance.
Career center
Learners who complete Computer Vision will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers build and maintain software systems that allow computers to "see" and interpret visual information. Some Computer Vision Engineers work on image and video analysis software for use in security systems or self-driving cars, while others develop facial recognition systems or medical imaging systems. This course "Computer Vision" may be useful for someone who wants to be a Computer Vision Engineer as it will teach you how to process raw digital images and build convolutional neural networks using TensorFlow.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models that can be used to predict outcomes, make decisions, or identify patterns. They work on a variety of projects, from developing fraud detection systems to building self-driving cars. This course "Computer Vision" may be useful for someone who wants to be a Machine Learning Engineer as it will teach you about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make decisions. They work on a variety of projects, from developing new products to improving customer service. This course "Computer Vision" may be useful for someone who wants to be a Data Scientist as it will teach you about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work on a variety of projects, from developing mobile apps to building enterprise software. This course "Computer Vision" may be useful for someone who wants to be a Software Engineer as it will teach you about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and maintain robots. They work on a variety of projects, from developing robots for use in manufacturing to building robots for use in space exploration. This course "Computer Vision" may be useful for someone who wants to be a Robotics Engineer as it will teach you how to process raw digital images and build convolutional neural networks using TensorFlow.
Computer Graphics Artist
Computer Graphics Artist
Computer Graphics Artists use computer software to create digital images and animations. They work on a variety of projects, from developing video games to creating special effects for movies. This course "Computer Vision" may be useful for someone who wants to be a Computer Graphics Artist as it will teach you how to process raw digital images and build convolutional neural networks using TensorFlow.
Medical Imaging Analyst
Medical Imaging Analyst
Medical Imaging Analysts use computer software to analyze medical images, such as X-rays, MRI scans, and CT scans. They work on a variety of projects, from diagnosing diseases to planning treatment. This course "Computer Vision" may be useful for someone who wants to be a Medical Imaging Analyst as it will teach you how to process raw digital images and build convolutional neural networks using TensorFlow.
Geospatial Analyst
Geospatial Analyst
Geospatial Analysts use computer software to analyze geographic data, such as satellite images and maps. They work on a variety of projects, from planning land use to managing natural resources. This course "Computer Vision" may be useful for someone who wants to be a Geospatial Analyst as it will teach you how to process raw digital images and build convolutional neural networks using TensorFlow.
Data Analyst
Data Analyst
Data Analysts use computer software to analyze data, such as financial data, sales data, and customer data. They work on a variety of projects, from identifying trends to predicting outcomes. This course "Computer Vision" may be useful for someone who wants to be a Data Analyst as it will teach you about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models.
Business Analyst
Business Analyst
Business Analysts use computer software to analyze business data, such as financial data, sales data, and customer data. They work on a variety of projects, from identifying trends to recommending improvements. This course "Computer Vision" may be useful for someone who wants to be a Business Analyst as it will teach you about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models.
Product Manager
Product Manager
Product Managers use computer software to manage the development and launch of new products. They work on a variety of projects, from defining product requirements to tracking customer feedback. This course "Computer Vision" may be useful for someone who wants to be a Product Manager as it will teach you about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models.
Project Manager
Project Manager
Project Managers use computer software to manage projects, such as software development projects and construction projects. They work on a variety of projects, from planning and budgeting to tracking progress and resolving issues. This course "Computer Vision" may be useful for someone who wants to be a Project Manager as it will teach you about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models.
Systems Analyst
Systems Analyst
Systems Analysts use computer software to analyze computer systems, such as software systems and hardware systems. They work on a variety of projects, from identifying problems to recommending improvements. This course "Computer Vision" may be useful for someone who wants to be a Systems Analyst as it will teach you about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models.
Web Developer
Web Developer
Web Developers use computer software to develop and maintain websites. They work on a variety of projects, from creating simple websites to building complex web applications. This course "Computer Vision" may be useful for someone who wants to be a Web Developer as it will teach you how to process raw digital images and build convolutional neural networks using TensorFlow.
Database Administrator
Database Administrator
Database Administrators use computer software to manage databases, such as SQL databases and NoSQL databases. They work on a variety of projects, from creating and maintaining databases to backing up and recovering data. This course "Computer Vision" may be useful for someone who wants to be a Database Administrator as it will teach you about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models.
For more career information including salaries, visit:
OpenCourser.com/course/9lpgoi/computer
Reading list
We've selected 15 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Computer Vision.
Save
Provides a comprehensive overview of computer vision algorithms and their applications. It covers a wide range of topics, from image formation and processing to object detection and recognition, and valuable resource for anyone interested in learning more about computer vision.
Provides a practical introduction to deep learning for computer vision. It covers a wide range of topics, from convolutional neural networks to object detection and segmentation, and valuable resource for anyone interested in learning more about deep learning for computer vision.
Provides a comprehensive overview of computer vision models, learning, and inference. It covers a wide range of topics, from image formation and processing to object detection and recognition, and valuable resource for anyone interested in learning more about computer vision.
Provides a practical introduction to machine learning using Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, from data preprocessing to model evaluation, and valuable resource for anyone interested in learning more about machine learning.
Provides a practical introduction to deep learning using Python. It covers a wide range of topics, from convolutional neural networks to recurrent neural networks, and valuable resource for anyone interested in learning more about deep learning.
Save
Provides a practical introduction to computer vision using OpenCV. It covers a wide range of topics, from image processing to object detection, and valuable resource for anyone interested in learning more about computer vision.
Provides a practical introduction to Python for data analysis. It covers a wide range of topics, from data munging to data visualization, and valuable resource for anyone interested in learning more about data analysis using Python.
Provides a comprehensive overview of the mathematics behind machine learning. It covers a wide range of topics, from linear algebra to calculus, and valuable resource for anyone interested in learning more about the mathematics behind machine learning.
Provides a comprehensive overview of probability and statistics for computer science. It covers a wide range of topics, from probability theory to statistical inference, and valuable resource for anyone interested in learning more about probability and statistics for computer science.
Provides a comprehensive overview of convex optimization. It covers a wide range of topics, from convex sets to duality theory, and valuable resource for anyone interested in learning more about convex optimization.
Provides a comprehensive overview of linear algebra and its applications. It covers a wide range of topics, from vectors and matrices to eigenvalues and eigenvectors, and valuable resource for anyone interested in learning more about linear algebra.
Provides a comprehensive overview of the calculus of variations and optimal control theory. It covers a wide range of topics, from the calculus of variations to optimal control, and valuable resource for anyone interested in learning more about the calculus of variations and optimal control theory.
Provides a comprehensive overview of partial differential equations. It covers a wide range of topics, from the heat equation to the wave equation, and valuable resource for anyone interested in learning more about partial differential equations.
Save
Provides a comprehensive overview of numerical analysis. It covers a wide range of topics, from interpolation to numerical integration, and valuable resource for anyone interested in learning more about numerical analysis.
Provides a comprehensive overview of optimization. It covers a wide range of topics, from unconstrained optimization to constrained optimization, and valuable resource for anyone interested in learning more about optimization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9lpgoi/computer
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Time
40320 hours
Level
Advanced
Via
Udacity
Instructors
David Silver
Thomas Hossler
Antje Muntzinger
Andreas Haja
Aaron Brown
Munir Jojo Verge
Mathilde Badoual
Language
English
Good to know
Core skills for autonomous vehicle engineers are developed
A focus on the critical Machine Learning essential to autonomous vehicles
Taught by experts in the autonomous vehicle industry
Develops the whole Machine Learning workflow
Leverages the popular TensorFlow framework
Covers convolutional neural networks, the state-of-the-art in image classification and object detection
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