Machine Learning & Self-Driving Cars: Bootcamp with Python

Machine Learning & Self-Driving Cars: Bootcamp with Python, available at $64.99, has an average rating of 4.65, with 78 lectures, 12 quizzes, based on 305 reviews, and has 39941 subscribers.

You will learn about 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 This course is ideal for individuals who are All-levels, every section is separated with three levels: Introduction, Hands-On, Deep Dive or Any student who wants to transition into the field of artificial intelligence or Entrepreneurs with an interest in working on some of the most cutting edge technologies or To upgrade or get a job in the Automotive / Data Science domain or Any people who want to create added value to their business by using powerful Machine Learning tools It is particularly useful for All-levels, every section is separated with three levels: Introduction, Hands-On, Deep Dive or Any student who wants to transition into the field of artificial intelligence or Entrepreneurs with an interest in working on some of the most cutting edge technologies or To upgrade or get a job in the Automotive / Data Science domain or Any people who want to create added value to their business by using powerful Machine Learning tools.

Enroll now: Machine Learning & Self-Driving Cars: Bootcamp with Python

Summary

Title: Machine Learning & Self-Driving Cars: Bootcamp with Python

Price: $64.99

Average Rating: 4.65

Number of Lectures: 78

Number of Quizzes: 12

Number of Published Lectures: 78

Number of Published Quizzes: 12

Number of Curriculum Items: 90

Number of Published Curriculum Objects: 90

Number of Practice Tests: 2

Number of Published Practice Tests: 2

Original Price: $29.99

Quality Status: approved

Status: Live

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

Who Should Attend

All-levels, every section is separated with three levels: Introduction, Hands-On, Deep Dive
Any student who wants to transition into the field of artificial intelligence
Entrepreneurs with an interest in working on some of the most cutting edge technologies
To upgrade or get a job in the Automotive / Data Science domain
Any people who want to create added value to their business by using powerful Machine Learning tools

Target Audiences

All-levels, every section is separated with three levels: Introduction, Hands-On, Deep Dive
Any student who wants to transition into the field of artificial intelligence
Entrepreneurs with an interest in working on some of the most cutting edge technologies
To upgrade or get a job in the Automotive / Data Science domain
Any people who want to create added value to their business by using powerful Machine Learning tools

Interested in Machine Learning or Self-Driving Cars (i.e. Tesla)? Then this course is for you!

This course has been designed by a professional Data Scientist, expert in Autonomous Vehicles, with the goal of sharing my knowledge and help you understand how Self-Driving Cars work in a simple way.

Each topic is presented at three levels:

Introduction [Beginner]: the topic will be presented, initial intuition about it
Hands-On [Intermediate]: practical lectures where we will learn by doing
Deep dive [Expert/Optional]: going deep into the maths to fully understand the topic

What tools will we use in the course?

Python: probably the most versatile programming language in the world, from websites to Deep Neural Networks, all can be done in Python
Python libraries: matplotlib, OpenCV, numpy, scikit-learn, keras, … (those libraries make the possibilities of Python limitless)
Webots: a very powerful simulator, which free and open source but can provide a wide range of simulation scenarios (Self-Driving Cars, drones, quadrupeds, robotic arms, production lines, …)

Who this course is for?

All-levels: there is no previous knowledge required, there is a section that will teach you how to program in Python
Maths/logic: High-school level is enough to understand everything!

Sections:

[Optional] Python sections: How to program in python, and how to use essential libraries
Computer Vision: teaches a computer how to see, and introduces key concepts for Neural Networks
Machine Learning: introduction, key concepts, and road sign classification
Collision Avoidance: so far we have used cameras, in this section we understand how radar and lidar sensors are used for self-driving cars, use them for collision avoidance, path planning
- Help us understand the difference between Tesla and other car manufacturers, because Tesla doesn’t use radar sensors
Deep learning: we will use all the concepts that we have seen before in CV, in ML and CA, neural networks introduction, Behavioural Cloning
Control Theory: control systems is the glue that stitches all engineering fields together
- If you are mainly interested in ML, you can only listen to the introduction for this section, but you should know that the initial Neural Networks were heavily influenced by CT

Who am I, and why am I qualified to talk about Self-driving cars?

Worked in self-driving motorbikes, boats and cars
Some of the biggest companies in the world
Over 8 years experience in the industry and a master in Robotic & CV
Always been interested in efficient learning, and used all the techniques that I’ve learned in this course

Course Curriculum

Chapter 1: Introduction

Lecture 1: Why This Course?

Lecture 2: How to Approach This Course?

Lecture 3: Make it Engaging

Chapter 2: Python [Optional]

Lecture 1: Installation

Lecture 2: Types in Python

Lecture 3: List & Map

Lecture 4: Operations

Lecture 5: Statements

Lecture 6: Functions

Lecture 7: Classes

Lecture 8: Object Oriented Programming

Lecture 9: Libraries / Modules

Chapter 3: Python's Essential Libraries [Optional]

Lecture 1: Introduction to Python Libraries

Lecture 2: Numpy

Lecture 3: Matplotlib

Lecture 4: OpenCV

Lecture 5: Other Libraries

Chapter 4: Computer Vision

Lecture 1: Introduction to Computer Vision

Lecture 2: How Computers "See" Images?

Lecture 3: Kernel & Convolution

Lecture 4: Image Processing with Kernels

Lecture 5: Thresholding

Lecture 6: Road Segmentation

Lecture 7: Why Webots?

Lecture 8: How to Install Webots in Windows? [Complete Video]

Lecture 9: How to Install Webots in Linux?

Lecture 10: Get The Code!

Lecture 11: Webots too slow?

Lecture 12: Webots Code: Explained

Lecture 13: [Exercise]: Your Line Following Algorithm!

Lecture 14: [Advanced] How to Read a Paper?

Lecture 15: [Advanced] Paper: SIFT

Lecture 16: [Advanced] Read Paper Example

Chapter 5: Machine Learning

Lecture 1: What's Machine Learning?

Lecture 2: Train, Predict & Evaluate

Lecture 3: Types of Machine Learning

Lecture 4: ML for Self-Driving Cars

Chapter 6: Machine Learning Hands-On

Lecture 1: Machine Learning Hands-On: Introduction

Lecture 2: Feature Engineering

Lecture 3: HOG

Lecture 4: SVM

Lecture 5: Performance Metrics

Lecture 6: Download the Dataset

Lecture 7: Code Explanation

Lecture 8: [Exercise]: Modify the code

Lecture 9: Useful ML Models

Lecture 10: Bias Vs Variance

Lecture 11: [Advanced] Paper: SVM

Chapter 7: Collision Avoidance

Lecture 1: Collision Avoidance: Introduction

Lecture 2: Ranging Sensors

Lecture 3: Cameras

Lecture 4: Simulation

Lecture 5: My Solution

Lecture 6: [Exercise]: Your Solution

Lecture 7: Path Planning

Lecture 8: [Advanced] RRT Code

Chapter 8: Deep Learning

Lecture 1: Deep Learning: Introduction

Lecture 2: How do Neural Networks Work?

Lecture 3: How does a Neural Network Learn?

Lecture 4: Convolutional Neural Networks

Lecture 5: Code Example

Chapter 9: Deep Learning: Hands-On

Lecture 1: Deep Learning Hands-On: Introduction

Lecture 2: Creating a Dataset

Lecture 3: Training

Lecture 4: See it drive!

Lecture 5: [Exercise]: Train it yourself!

Lecture 6: [Advanced] AlexNet

Chapter 10: Control Theory

Lecture 1: Why Learn Control Theory

Lecture 2: Control Systems Map

Lecture 3: Stability – Introduction

Lecture 4: Stability – Missing in Machine Learning

Lecture 5: Open and Closed Loop Control

Lecture 6: Closed Loop Control – Cruise Control

Lecture 7: PID – Introduction

Lecture 8: PID Controller – Deep Dive

Lecture 9: PID Controller – How to Tune it?

Lecture 10: PID Controller – Why is it use SO much?

Lecture 11: [Advanced] Paper: PID Controller Design

Instructors

Iu Ayala
Data Scientist & Robotics Engineer, CEO of Gradient Insight

Rating Distribution

1 stars: 0 votes
2 stars: 4 votes
3 stars: 31 votes
4 stars: 68 votes
5 stars: 202 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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