Machine Learning & Self-Driving Cars: Bootcamp with Python
What you will learn
Master Machine Learning and Python
Learn how to apply Machine Learning algorithms to develop a Self-Driving Car from scratch
Understand why Deep Learning is such a revolution and use it to make the car drive like a human (Behavioural Cloning)
Simulate a Self-Driving car in a realistic environment using multiple techniques (Computer Vision, Convolution Neural Networks, ...)
Create strong added value to your business
Gentle introduction to Machine Learning where all the key concepts are presented in an intuitive way
Code Deep Convolutional Neural Networks with Keras (the most popular library)
Learn to apply Computer Vision and Deep Learning techniques to build automotive related algorithms
Understand how Self Driving Cars work (sensors, actuators, speed control, ...)
Learn to code in Python starting from the very beginning
Python libraires: NumPy, Sklearn (Scikit-Learn), Keras, OpenCV, Matplotlib
Course Gallery
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Comidoc Review
Our Verdict
This Machine Learning & Self-Driving Cars: Bootcamp with Python offers a unique blend of machine learning, deep learning, and computer vision concepts that provide a solid foundation for learners. Hands-on projects ensure practical experience in applying these concepts using Python, which is beneficial for real-world problem solving. Although some learners may want more depth regarding specific skills or topics, the course provides a competitive edge by covering advanced autonomous vehicle concepts and offering up-to-date information through instructor dedication. Despite minor issues such as outdated materials in certain sections, this Udemy course remains an excellent choice for anyone looking to explore machine learning and self-driving car technologies.
What We Liked
- The course provides a comprehensive combination of machine learning, deep learning, and computer vision concepts, which sets a strong foundation for building a self-driving car.
- Hands-on projects throughout the course help to ensure that learners gain practical experience and confidence in applying Python and machine learning algorithms to real-world problems.
- The curriculum covers advanced topics and provides insights into how autonomous vehicles function, giving students a competitive edge in the field.
- Instructor is dedicated to maintaining the course's relevance by providing updates and quickly responding to student questions.
Potential Drawbacks
- Some learners find that the course attempts to cover too many complex topics in a short amount of time, leaving them wanting for deeper dives into specific skills.
- Coding examples might benefit from more detailed explanations about argument values and their reasoning.
- While there are hands-on projects throughout the course, certain sections may leave some learners wanting for additional real-world case studies.
- Some secondary materials (like the traffic sign classifier) have been reported as outdated or not integrated into the primary simulation project.