Halloween Special for Data Scientists!

I walked past a very impressive Halloween Castle, someone in our community put together, with whispering witches and skeletons the size of a two story house, and watching kids coming to watch, play and talk to scary and spooky characters, I couldn’t avoid a temptation to think about a Halloween Special for Data Scientists, something to impress trick-or-treaters even more. I immediately thought that quite a few examples in my book qualify for Halloween special!

Speaking of spooky bones and skeletons: in Chapter 3 of my book and video course there’s a fun example of segmentation methods for tissues, bones and MRI detection. I used an MRI scan of my own knee to demonstrate some of the tools a data scientist can use in radiology. Segmentation is important in many areas of computer vision, and the example of using Scikit-image and OpenCV to segment areas on MRI scans.

Of course there’s much more AI examples for Halloween we can apply to impress kids even more! Robotics is the key part of animating those scary characters in front of your gate greeting trick-or-treaters. Reinforcement learning can be applied to animate humanoid models. Applying AI is certainly fun, and Halloween can get even better!

Machine Learning in Sports 101

AI already helps scoring sporting events, like gymnastics in the upcoming Olympics, thanks to systems like Fujitsu’s for example. Sports science traditionally applied biomechanics for sports analysis, and it works really well! AI can help this traditional approach by adding methods from supervised, unsupervised and reinforcement learning.

Supervised learning can help with labeled sets of data in tasks like classification, for example classifying sports activities:

Unsupervised learning doesn’t assume that data is labeled, instead its goal is finding similarities in the data. It’s often used for self-organizing dimensionality reduction and clustering. For example, if you train an unsupervised model with sufficient data containing images of athletes performing actions in different sports, such a model should be able to predict what group, or sport a given image belongs to.

Reinforcement learning (RL) applies a concept of an agent trying to achieve the goal and receiving a reward for most positive actions. There’re some really cool applications of this method in sports that I covered in my book.

Want to learn more on applying ML in sports? Check out my book and the course that covers a lot of material with detailed walk throughs.

Get a signed book

This is my 24/7 virtual book signing page: you can get your signed book anytime, by clicking the button at the end of this post. If you already have my book or video course “AI in Sports with Python”, click here to Get an Electronic Signature! Or if you would like to have a physical copy of the new book with my signature on it click Get a Paperback Book with Signature ($5 flat rate shipping anywhere in the US/Canada only)!

Ever wondered how your author signed book is getting shipped? I have the process now completely streamlined, so if you want to support the book and (a vague hint) my next book, you can get a signed copy fast!

Thanks to my publishers, Apress, I have a stock of books ready and waiting for signature πŸ™‚

I ship with Stamps.com, your book will be shipped with USPS mail service in the US. The book weighs about 1 lb 6 oz and fits very nicely in a box from Amazon.

The label is printed on my trusty laser printer and your book is shipped via US Postal Service. This is my virtual signing and shipping process at the moment. Have fun and enjoy!

Vision AI in Sports

Some of the oldest datasets on human motion and sports comes from ancient images, sculptures and paintings. Scenes depicted in cave art took place 30,000 years ago, and sculptures from ancient Olympics and can still be analyzed today with deep vision methods and used to train our models. Think of the longevity of information preserved in these sculptures and images!

I allocated several chapters on deep vision methods in my book: from basics of neural nets, to 2D and 3D pose estimation, video classification and more.

#AISportBook Announcement

Video announcing my book “Applied Machine Learning for Health and Fitness”

LIVE! #AISportBook

Announcing LIVE cast on Linkedin with Kevin Ashley, hosted by Mike Downey and APRESS, discussing AI in Sports technology with examples from Kevin’s book “Applied Machine Learning for Health and Fitness” and the video course “AI in Sports with Python” based on the book.

WHEN: 9/17 THURSDAY AT 2PM Pacific Time (PST)
WHERE: LinkedIn – Mike Downey will be hosting LiveCast, click on his profile at 2PM and join the livecast!

SWEEPSTAKES:

Post #AISportBook and the name of your favorite #Sport plus the photo of the sport on Linkedin, Twitter or Instagram to win:

Resources:

“Applied Machine Learning for Health and Fitness” (APRESS)

AI in Sports with Python” (Deep dive online course)

#AISportBook

You + your sport + #AISportBook with the “Applied Machine Learning for Health and Fitness” book and a picture of your favorite sport e.g. #Surfing #Skiing #Soccer #Basketball #… to be featured in the next episodes of my “AI in Sports” video course.

The book is available!

My book “Applied Machine Learning for Health and Fitness” is now available from APRESS and Amazon!

It’s for anyone interested in sports, artificial intelligence, and new applications for sports and fitness. Learn to apply AI in sports with Python with fun applications in many sports: tennis, surfing, skiing, snowboarding, skateboarding, football, gymnastics, basketball, javelin, weightlifting, track and field and much more!Β 

Want to learn more? Check my new course, that complements the book!

The course is accompanied by practical step-by-step Python code samples and Jupyter notebooks. These models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in sports, health and fitness, motion capture, gaming, cinema production and more.

Enjoy!

Kevin

Advance your career with this course in sports technology and AI

AI in Sports with Python course has recently been approved by ISTA Professional Sports Technology Institute for sports and technology professionals. This approval shows how important AI becomes for sports technology helping data scientists and sport professionals and changing the world of sports and athletic performance. Taking the course will give you access to many examples that you can use in your careers as data scientist or a sport practitioner working with health, fitness, sports or human movement analysis. If you are in the sport technology space, ISTA offers discounted access to my course for its members.

ISTA’s Professional Sports Technology Instituteβ„’ advances careers, improves athletic success and further matures the field of sports technology through recognized standards, certifications, accreditation, partner resources, tools, research, publications, courses and career development guides.

AI in Sports with Python is a comprehensive course and guide to using machine learning methods in sports. It’s for anyone interested in sports, artificial intelligence, and new applications for sports and fitness. Created by Kevin Ashley, Microsoft developer Hall of Fame engineer, a professional instructor and author of popular sports apps, this course complements his new book β€œApplied Machine Learning for Health and Fitness”, and has been reviewed by Olympic coaches and athletes, data scientists and engineers. Learn AI with Python with fun applications in many sports: tennis, surfing, skiing, snowboarding, skateboarding, football, gymnastics, basketball, javelin, weightlifting, track and field and much more! 

The course is accompanied by practical step-by-step Python code samples and Jupyter notebooks. These models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in sports, health and fitness, motion capture, gaming, cinema production and more.

Check http://activefitness.ai and http://ai-learning.vhx.tv/

AI in Sports with Python – Kickstarter funded!

It’s been only 60 days after I launched a Kickstarter campaign on my video course AI in Sports with Python supplementing my book ‘Applied Machine Learning in Health and Fitness’ and it’s been an amazing success! The campaign has now closed with more than one hundred supporters backing my project, raising more than 450% of what was initially requested to fund it, providing valuable feedback and reviewing videos. I’ve been working with startups and ISVs in California and worldwide for almost 10 years, and this is a great proof that the entrepreneurial spirit drives this economy. No matter how big or small the project is, if it’s creative and provides value, it’ll fly! THANK YOU!

During these days of your support, I recorded 20 episodes out of about 25 episodes planned (about 80%), improving quality, light, cinematography, editing, learning along the way! I also listened to your feedback and comments, sending screeners to those of you who volunteered to review. The course is scheduled to launch together with the book by the end of August, but early episodes will be made available for those who supported the project during the campaign soon.

Now… that the Kickstarter caompaign is complete, here’s the link to the course. Ta-Da!

Release DatesΒ and Availability:

Book Availability: The book availability is listed on the publisher’s web site, which says August 17 (https://www.apress.com/us/book/9781484257715). The ebook will be available a bit sooner (a few weeks) than the printed book. If you ordered the printed book, it’ll come separately once available. Those who requested signed rewards will receive once I get printed copies for you to sign and ship πŸ™‚

I’m on track to record the last episodes (in italics):

Video Episodes Recording Schedule

  • Module 1 – Getting Started
    Episode 1.1 – Getting Started
    Episode 1.2 – Data Scientist Toolbox
  • Module 2 – Physics of Sports
    Episode 2.1 – Sensors
    Episode 2.2 – Kinematics
    Episode 2.3 – Figure Skating
  • Module 3 – Neural Networks
    Episode 3.1 – Neural Networks
    Episode 3.2 – Simple Neural Network
    Episode 3.3 – Multi-Layer Neural Network
  • Module 4 – Deep Vision
    Episode 4.1 – Computer Vision
    Episode 4.2 – Classification
    Episode 4.3 – Detection
    Episode 4.4 – Segmentation
    Episode 4.5 – Human Body Joints
  • Module 5 – Human Body Pose Estimation
    Episode 5.1 Pose Estimation Methods: 2D and 3D
    Episode 5.2 Surfing
    Episode 5.3 Detecting Skill Level
    Episode 5.4 Multi-person pose estimation
  • Module 6 – Video Action Recognition
    Episode 6.1 Action Recognition Methods, Datasets, Models
    Episode 6.2 Video Classification
  • Module 7 – Reinforcement Learning
    Episode 7.1 – Reinforcement Learning
    Episode 7.2 – Skateboarding
    Episode 7.3 – Gymnastics
    Episode 7.4 – Human Models
  • Module 8 – Machine Learning in the Cloud
    Episode 8.1 – Working with AI in the cloud
    Episode 8.2 – Training models in the cloud
    Episode 8.3 – Managing machine learning in the cloud
    Episode 8.4 – Taking your AI to production

Estimated length: 3 hours