“Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years.” - Mark Cuban
Machine learning is changing the world and it’s not going to stop. Leading companies like Google, Amazon, and Facebook are betting their futures on AI. Organizations of all kinds can’t hire machine learning engineers fast enough. The world needs more people that understand machine learning and our goal is to get you started on that path as efficiently as possible. While there are plenty of online resources, we know it's tough to learn a technical topic without a teacher. This workshop will arm you with the tools to get started using machine learning in your day job and the resources to find additional help if you want to go deeper.
The course is expertly designed to leave you with the ability to take training data, do feature selection and actually build models for applications like content categorization, sentiment analysis, and image recognition. By the end of the day, students will be able to use models in their day-to-day work. You will also walk away with a high-level understanding of how common models such as Deep Neural Networks, SVMs, Logistic Regression and Naive Bayes work and when to use them.
Intro to Machine Learning
Intro to Deep Learning
Intro to Machine Learning Platforms
Google Cloud ML
We try to make this class as accessible as possible. Some proficiency with Python is necessary. If you can open up a Jupyter notebook and install requisite software that’s helpful but we’ll also cover how to do that quickly in the beginning.
What will be provided
We will provide all the food, coffee, wifi and power.
What you Need to bring
You must also bring your own laptop (don’t forget your charger).
It saves a lot of time if you can get your laptop setup in advance. If you can't get everything setup, try to come early and we'll help you with the installation.
Download code for the class from https://github.com/lukas/ml-class
There are instructions on this website for how to install all the necessary programs at https://github.com/lukas/ml-class/blob/master/README.md
- if you have questions, you can email us or put them in the github issues tracker where they might help another student.
Lukas Biewald: Lukas Biewald is the founder of CrowdFlower, an Artificial Intelligence company that works with data science teams at Google, Bloomberg, Facebook and hundreds of other organizations to make machine learning work in the real world. Prior to that, Lukas was the first data scientist at Powerset (Acquired by Microsoft and rebranded as Bing) and a scientist at Yahoo!, Lukas was shipping machine learning algorithms to hundreds of millions of users.
Lukas frequently teaches invited Machine Learning workshops with Galvanize, O’Reilly and ODSC. He is a frequent contributor to Computerworld, Forbes and O’Reilly and has presented at the machine learning academic conferences such as AAAI, SIGIR, ACL and EMNLP. He was in Inc’s annual 30 under 30 and was also a finalist at TechCrunch Disrupt.
9:00 – 10:00 Breakfast and Intro to Machine Learning
We will assume no knowledge of Machine Learning, so we'll go over terminology and the history of Machine Learning and Artificial Intelligence. We'll talk about the common use cases and how they fit in with the different Machine Learning algorithms.
10:00 – 12:00 Build a Sentiment Classifier From Scratch
Everyone builds a Twitter sentiment classifier using scikit-learn. We try multiple feature selection approaches and multiple model types. We learn some common tricks for actually making machine learning effective in the real world.
12:00-1:00 Lunch and Overview of State Machine Learning
Eat lunch and for your eating entertainment, Lukas will introduce a little math, stats and history of how machine learning got to where it is today. We will go over the state of machine learning platforms today and how to get an entry-level job in machine learning for those that are interested.
1:00-2:30 Try the Common Machine Learning Platforms
These days, there are many excellent, scalable, low cost machine learning platforms. We will try rebuilding our sentiment classifier on two of the most common: Microsoft Azure ML and Amazon ML.
2:30-3:00 Break and Q&A
We can discuss other applications of this technology and look at how it might apply to real-world tasks that students may be working on.
3:00-5:00 Introduction to TensorFlow and Deep Neural Networks
We will learn how deep neural networks work and actually build one! If you bring a laptop with a GPU that supports CUDA (for example a MacBook with Mac OS X 10.11 or later), we’ll see if we can make it GPU accelerated.
We’ll all build a network to do handwritten digit recognition.
5:00-5:30 Wrap-up and Q&A
We will finish up and discuss how to apply this knowledge directly to problems that we actually face in our jobs.
5:30-7:00 Drinks & Networking
We’ll bring together top entrepreneurs, tech executives & engineers to connect with and learn from. Plus, this is a chance to meet your classmates and teachers in an informal and fun setting.
Testimonials and Feedback
"I found it to be really engaging and interesting. I was already familiar with some ML concepts, so it helped me understand them better and think about how to apply them. The code samples are really great and will definitely reference them in the future. I thought the class went at a generally good pace."
"The class offers a really great overview of ML"
"Good experience - full of great resources and discussion. Good, practical intro for new folks, and also valuable for those familiar with the basics. I walked away excited to experiment!"
“What an outstanding workshop yesterday. Thank you for your time and enthusiasm. As a newcomer to the field, was fascinated by the way you seamlessly presented such a challenging topic to a widespread audience. Even more, you quickly built a great sense of community and positive energy in the room, which made all the difference. Your advice on the job market and nuances about the field were much appreciated.”
“Class was great, you ticked off my curiosity. I am excited to review the content and retry it by myself. Thank you for encouraging peer to peer collaboration and making the effort to build the slack channel. I think it was nice to see you debug live.”
We also do corporate trainings - send us an email for details.