Machine Learning & Self-Driving Cars: Bootcamp with Python from Udemy

What's inside

Learning objectives

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
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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
Show more
Show less

Syllabus

Understand why this is the right course for YOU!

Welcome aboard, future self-driving pioneers! ? Are you ready to embark on a journey that will transform the way you perceive technology and innovation? In this captivating first lecture, we're pulling back the curtain to reveal why this course is the ultimate key to unlocking the world of self-driving cars.

? Why You Should Be Excited:

? Revolutionize Your Knowledge: The self-driving car industry is changing the game. Discover how this course will equip you with the tools to stay ahead in the rapidly evolving landscape of autonomous vehicles.

? Demystify Complex Concepts: Have you ever wondered how a machine can drive itself? We break down intricate ideas into digestible chunks, ensuring that you'll not only understand but also master the art of self-driving technology.

? Tap into the Future: Self-driving cars are not just a trend; they're shaping our future. By delving into this course, you're tapping into a realm of knowledge that could lead to groundbreaking career opportunities and insights.

?️ What Awaits You:

✅ Dive into the core concepts of machine learning and artificial intelligence.

✅ Understand the nuts and bolts of computer vision and its pivotal role in self-driving technology.

✅ Demystify the magic of neural networks and deep learning, enabling you to train your own models.

✅ Grasp the power of control theory and its impact on the self-driving experience.

✅ Elevate your proficiency in Python programming and essential libraries.

✅ Get hands-on with exciting real-world projects and simulations.

? Ready to accelerate your learning journey and become part of the self-driving revolution? Buckle up and get ready to take the driver's seat in the world of innovation, transformation, and limitless possibilities.

How to Approach This Course?

Many people find that faster playback speeds allow them to better retain information from the videos they’re watching. When watching at a normal speed, some viewers may struggle to stay focused or pay attention for the entire duration of the video. By speeding up the video, they’re able to engage more fully and retain more information.

Source: https://www.thetechedvocate.org/why-people-watch-youtube-videos-at-faster-playback-speeds/#:~:text=For%20example%2C%20instructional%20videos%20or,YouTube%20videos%20at%20faster%20speeds.

Basic understanding about Python

Installation

--- Basic Understanding ---

Python types categorise values in the language, simplifying code debugging and maintenance. The built-in types include:

Integers (e.g., 1, 56, -23): Whole numbers without a decimal point.
Floats (e.g., 2.0, -8.5, 3.14): Numbers that require a decimal point.
Strings (e.g., "hello", 'Python'): Sequences of characters enclosed in quotes.
Lists (e.g., [1, 2, 3], ["a", "b", "c"]): Ordered collections of values, which can be of mixed types.

--- Advanced Insight ---

Custom Classes: Create your own types with specific attributes and methods, enhancing code organisation.
Type Hinting (e.g., def function(arg1: int, arg2: str) -> bool:): Specify expected data types, improving code readability and allowing better tooling support.

This video elucidates the significance of understanding types in Python, paving the way from fundamental concepts to advanced applications for more proficient coding.

Types in Python

List & Map

Operations

Statements

Functions

Classes

Classes Basic

Classes Inheritance

Object Oriented Programming

Libraries / Modules

Modules

Learn how to use some essential libraries with Python

Introduction to Python Libraries

Numpy

Matplotlib

Just a brief text

OpenCV

Just a quick test to ensure that you understood some basic OpenCV concepts, since we will use this library quite a lot

Other Libraries

This quick quiz is designed to ensure that you understood the power of python libraries :)

Learn the Basics of Computer Vision and create an algorithm to drive an autonomous car

Introduction to Computer Vision

How Computers "See" Images?

Kernel & Convolution

Image Processing with Kernels

Thresholding

Road Segmentation

Why Webots?

Windows installation steps:

Download the "webots-R2022a_setup.exe" installation file from our website.
Double click on this file.
Follow the installation instructions.

Use in Visual Studio Code:

Prerequisites: we need to have installed Python and Visual Studio Code, you can find how to install those in the first video of the Python section
Install the following Python libraries: numpy, opencv-python
- You can find out how to install python libraries in the Python Libraries section (through pip install)
Click the "Windows Key" and write "Edit the system environment variables"
- Create variable “WEBOTS_HOME” with content “C:\Program Files\Webots”
- Create variable “PYTHONPATH” with content “%WEBOTS_HOME%/lib/controller/python3X”
  - WARNING: in the documentation says to use ${WEBOTS_HOME}, but doesn’t work
  - Change the last “X” for the minor revision number of your Python version
- Add to “PATH”
  “%WEBOTS_HOME%\lib\controller”
  “%WEBOTS_HOME%\msys64\mingw64\bin”
  “%WEBOTS_HOME%\msys64\mingw64\bin\cpp”
- Create variable “PYTHONIOENCODING” with content “UTF-8”

How to Install Webots in Linux?

Get The Code!

Webots too slow?

Webots Code: Explained

[Exercise]: Your Line Following Algorithm!

[Advanced] How to Read a Paper?

[Advanced] Paper: SIFT

This video is an practical example of how to read a paper from beginning to end.

Understand all the key concepts about Machine Learning

What's Machine Learning?

Train, Predict & Evaluate

Types of Machine Learning

ML for Self-Driving Cars

Implement, train and evaluate an image classifier (example: road sign classification)

Machine Learning Hands-On: Introduction

Feature Engineering

HOG

SVM

Performance Metrics

Download the Dataset

Code Explanation

[Exercise]: Modify the code

Useful ML Models

Bias Vs Variance

[Advanced] Paper: SVM

Understand the difference between using cameras and radars

Collision Avoidance: Introduction

Ranging Sensors

Cameras

Simulation

My Solution

[Exercise]: Your Solution

Path Planning

[Advanced] RRT Code

Understand the logic behind Deep Learning

Deep Learning: Introduction

How do Neural Networks Work?

How does a Neural Network Learn?

Convolutional Neural Networks

Code Example

Implement a Convolution Neural Network to make a car drive like a human

Deep Learning Hands-On: Introduction

Creating a Dataset

Training

See it drive!

[Exercise]: Train it yourself!

[Advanced] AlexNet

Learn about Control Theory, why is it still used and why it will never be replaced by Machine Learning / Deep Learning?

Why Learn Control Theory

Control Systems Map

Note: there is a mistake says "Machine Learning is the missing part of Machine Learning" should say "Machine Learning can't guarantee that it will produce a stable output"

Stability - Missing in Machine Learning

Open and Closed Loop Control

Video Note: Error of last point (orange) says 100, should say 0

PID - Introduction

PID Controller - Deep Dive

PID Controller - How to Tune it?

PID Controller - Why is it use SO much?

[Advanced] Paper: PID Controller Design

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers real-world applications and industry best practices

Prerequisite knowledge required, may not suit absolute beginners

Multi-modal learning, with videos, readings, and hands-on labs

Builds a solid foundation for beginners in machine learning and self-driving cars

Taught by a professional data scientist with experience in autonomous vehicles

Covers advanced topics like deep learning and control theory, suitable for experienced learners

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 Machine Learning & Self-Driving Cars: Bootcamp with Python with these activities:

Understand the Architecture of a Self-Driving Car

Show steps

Build a conceptual foundation for the course by reviewing the overall architecture of a self-driving car, its different components, and how they work together.

View Taming Silicon Valley on Amazon

Show steps

Read Chapter 1 of the book
Research different types of sensors used in self-driving cars
Create a diagram of the architecture of a self-driving car

Practice Using Python and Libraries for Computer Vision

Show steps

Enhance your computer vision skills by following guided tutorials on using Python, NumPy, and OpenCV, which are essential tools used in the course.

Browse courses on Python

Show steps

Follow the 'Python for Computer Vision' tutorial series
Complete the 'OpenCV for Beginners' course
Practice working with images using NumPy

Test Your Understanding of Convolutional Neural Networks

Show steps

Solidify your understanding of convolutional neural networks (CNNs) by completing practice drills that test different aspects of CNNs, such as their architecture and how they are used for image classification.

Browse courses on Deep Learning

Show steps

Solve the 'MNIST Image Classification with CNNs' challenge
Answer questions on the 'Convolutional Neural Networks' quiz
Predict the classes of images using a pre-trained CNN model

Three other activities

Expand to see all activities and additional details

Show all six activities

Learn the Theoretical Foundations of Control Theory

Show steps

Gain a deeper understanding of the theoretical concepts of control theory by reviewing a foundational textbook, which will provide insights into how these concepts apply to self-driving cars.

View Modern Control Engineering on Amazon

Show steps

Read Chapter 1-3 of the book
Solve the end-of-chapter exercises
Complete the online simulations

Attend a Workshop on Autonomous Vehicle Development

Show steps

Get hands-on experience and learn from industry experts by attending a workshop focused on autonomous vehicle development.

Browse courses on Autonomous Vehicles

Show steps

Research and find a suitable workshop
Register for the workshop
Attend the workshop and actively participate

Contribute to OpenCV Projects

Show steps

Enhance your understanding of OpenCV and computer vision by collaborating on open-source projects, allowing you to learn from others and contribute to the community.

Browse courses on OpenCV

Show steps

Identify an OpenCV project to contribute to
Read the documentation and understand the project's goals
Fork the repository and make your changes
Submit a pull request

Career center

Learners who complete Machine Learning & Self-Driving Cars: Bootcamp with Python will develop knowledge and skills that may be useful to these careers:

Robotics Engineer

Robotics Engineers design, build, and maintain robots. This course provides a strong foundation in computer vision, machine learning, and control theory, which are essential for success in this role. It also covers topics such as robot kinematics, dynamics, and control, which are important for developing robust and efficient robots.

See salaries and explore the career path for Robotics Engineer

Machine Learning Engineer

Machine Learning Engineers design and implement machine learning models to solve real-world problems. This course provides a comprehensive introduction to machine learning, including supervised and unsupervised learning, and deep learning. It also covers topics such as feature engineering, model evaluation, and deployment, which are essential for success in this role.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their expertise in statistics, machine learning, and data analysis to solve complex business problems. This course provides a strong foundation in these areas, and it also covers topics such as data visualization and communication, which are essential for success in this role.

See salaries and explore the career path for Data Scientist

Computer Vision Engineer

Computer Vision Engineers develop algorithms and software to enable computers to see and interpret images and videos. This course provides a comprehensive introduction to computer vision, including topics such as image processing, feature extraction, and object detection. It also covers topics such as deep learning and convolutional neural networks, which are essential for developing state-of-the-art computer vision systems.

See salaries and explore the career path for Computer Vision Engineer

Automotive Engineer

Automotive Engineers design, develop, and maintain vehicles. This course provides a strong foundation in computer vision, machine learning, and control theory, which are essential for success in this role. It also covers topics such as vehicle dynamics, powertrain systems, and safety systems, which are important for developing safe and efficient vehicles.

See salaries and explore the career path for Automotive Engineer

Control Systems Engineer

Control Systems Engineers design, implement, and maintain control systems to ensure that systems operate safely and efficiently. This course provides a strong foundation in control theory and control systems design, which are essential for success in this role. It also covers topics such as stability analysis, feedback control, and state-space methods, which are important for developing robust and reliable control systems.

See salaries and explore the career path for Control Systems Engineer

Aerospace Engineer

Aerospace Engineers design, develop, and maintain aircraft, spacecraft, and other aerospace systems. This course provides a strong foundation in computer vision, machine learning, and control theory, which are essential for success in this role. It also covers topics such as aerodynamics, propulsion, and structural analysis, which are important for developing safe and efficient aerospace systems.

See salaries and explore the career path for Aerospace Engineer

Systems Engineer

Systems Engineers design, develop, and maintain complex systems. This course provides a strong foundation in computer vision, machine learning, and control theory, which are essential for success in this role. It also covers topics such as system analysis, design, and integration, which are important for developing safe and efficient systems.

See salaries and explore the career path for Systems Engineer

Chemical Engineer

Chemical Engineers design, develop, and maintain chemical processes. This course provides a strong foundation in computer vision, machine learning, and control theory, which are essential for success in this role. It also covers topics such as chemical reactions, thermodynamics, and fluid mechanics, which are important for developing safe and efficient chemical processes.

See salaries and explore the career path for Chemical Engineer

Biomedical Engineer

Biomedical Engineers design, develop, and maintain biomedical systems. This course provides a strong foundation in computer vision, machine learning, and control theory, which are essential for success in this role. It also covers topics such as biomaterials, biomechanics, and medical imaging, which are important for developing safe and efficient biomedical systems.

See salaries and explore the career path for Biomedical Engineer

Electrical Engineer

Electrical Engineers design, develop, and maintain electrical systems. This course provides a strong foundation in computer vision, machine learning, and control theory, which are essential for success in this role. It also covers topics such as power systems, electronics, and control systems, which are important for developing safe and efficient electrical systems.

See salaries and explore the career path for Electrical Engineer

Materials Engineer

Materials Engineers design, develop, and maintain materials. This course provides a strong foundation in computer vision, machine learning, and control theory, which are essential for success in this role. It also covers topics such as materials science, mechanics, and thermodynamics, which are important for developing safe and efficient materials.

See salaries and explore the career path for Materials Engineer

Mechanical Engineer

Mechanical Engineers design, develop, and maintain mechanical systems. This course provides a strong foundation in computer vision, machine learning, and control theory, which are essential for success in this role. It also covers topics such as mechanics, materials science, and thermodynamics, which are important for developing safe and efficient mechanical systems.

See salaries and explore the career path for Mechanical Engineer

Software Engineer

Software Engineers design, develop, and maintain software systems. This course can help build a foundation in Python and object-oriented programming, which are essential for success in this role. It also covers topics such as software design, testing, and debugging, which are important for developing high-quality software.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course can help build a foundation in Python and data analysis techniques, which are essential for success in this role.

See salaries and explore the career path for Data Analyst