LEARN KALMAN FILTERS: The Hidden Algorithm that silently powers the future by Jeremy Cohen

LEARN KALMAN FILTERS: The Hidden Algorithm that silently powers the future


For mid-level Engineers who want to master advanced algorithms and increase their skills.

Achieve higher results

What's included

✔️ The Hidden Algorithm that silently powers the world
✔️ The Kalman Filter Cheat Sheet - Build filters in a few minutes
✔️ Real World Portfolio Project
✔️ VIP contact and support
✔️One-on-one onboarding sessions (optional)

The little known algorithm that is changing our lives every day

At the heart of Artificial Intelligence lies a series of "No Learning" algorithms that powers our navigation systems, fusion modules, and prediction algorithms.

It's not rocket science, but the task is daunting

Probabilistic Robotics are full of maths.

Every time you try to learn about it, you face the same struggle:
The long math equations...
The complex blog papers...
And the fact that every use case is a specific use case...

It's daunting, discouraging, and you just want to give up.

Yet, it's a must have.
You can't work on Autonomous Tech, Computer Vision, or Sensor Fusion and don't know about Kalman Filters.

Introducing... LEARN KALMAN FILTERS: The Hidden Algorithms that silently powers the future!

Master the little known algorithm that changed the world and start creating tomorrow's society!

Introduction to Bayesian Probabilities

Learn the basics of Bayesian Probabilities. Learn how to correct the part, smooth the present, and predict the future.

Concepts: Sensor Fusion, Object Tracking, Motion Models, Bayesian Probabilities, Linear Kalman Filters, Motion Models, Noise, Discrete Probabilities, Gaussians, Multivariate Gaussians

Code it from scratch

Learn how to build a Kalman Filter from scratch, work with various examples and dimensions. Link your knowledge to a coding experience.

Concepts: Gaussians, Kalman Filter Libraries, 1D Kalman Filters, 2D Kalman Filters, Kalman Filter Orders, How to initialize your matrices, How to work with time prediction

Real-Life Projects

Combine your skills of Kalman Filters with Computer Vision and learn to make algorithms robust and efficients.

You will build your own bicycle tracking algorithm that will reinforce an obstacle detection algorithm.

🧐 Still need more precision? Look at the detailed summary here.

From the beginning, Kalman Filters have been at the cutting-edge of technology

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FAQs

What are the prerequisites to follow the course?

This course is an introduction for beginner and mid-level engineers. If you can code in Python, you have the coding part. The Maths part will be about linear algebra and matrix multiplications; if you can understand these, you're fine!

Will that course work for me?

If you aspire to work on Robotics, Autonomous Systems, or Computer Vision, this course is for you. Kalman Filters are a must have in this case, and you will need this knowledge.
The course has been created to be easy to follow.

What are the type of filters covered?

You will learn about the fundamentals of bayesian filters and Linear Kalman Filters in 1D and 2D.
I will also evoke Non-Linear Kalman Filters and Particle Filters but their implementation won't be covered.
See this as the Volume I of a Kalman Filters series!

Kalman Filters are a superweapon that you can use in any discipline and project. It's something that brought rockets to the moon and that will continue to change our society for decades...

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Jeremy Cohen

Artificial Intelligence & Self-Driving Car Engineer, Head Dean of France School of AI, and Machine Learning Lecturer.
I started thinkautonomous.ai to help aspiring AI & Self-Driving Car Engineers to land their dream job. Working in the industry of the future requires skills and passion. You can build your skills here, where you'll create relevant projects that are used every day in autonomous robots & AI engines.

👉 Learn more