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Technical Introduction to AI, Machine Learning & Deep Learning Part 1

Engineered Education

Tuesday, November 7, 2017 from 9:00 AM to 7:00 PM (PST)

Technical Introduction to AI, Machine & Deep Learning...

Ticket Information

Ticket Type Remaining Sales End Price Fee Quantity
Early Registration   more info 15 Tickets Ended $395.00 $22.72
Registration 29 Tickets 2h 42m $495.00 $28.22
Late Registration 40 Tickets Not Started $595.00 $33.72
Team Discount (4 or more registrations)   more info 25 Tickets Sep 29, 2017 $299.00 $17.44
Techincal Intro Part 1+2: Two Day Pass
Want to take your skills to the next level? By signing up for our 2-day pass, you will learn the skills and processes to start applying AI, Machine Learning & Deep Learning right out of the gate. Interested in just deep learning, register now for our upcoming workshop: Technical Introduction to AI, Machine Learning & Deep Learning Part 2: Vision Algorithms by clicking here:
25 Tickets Sep 28, 2017 $695.00 $39.22
Techincal Intro Part 1+2+3: Three Day Pass   more info 20 Tickets Sep 28, 2017 $995.00 $49.80

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Event Details

This a course to take engineers from zero to one in machine learning in a day.  


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 designed to leave you with the ability to take training data, do feature selection and actually build models for applications.  We do two standard use cases: sentiment analysis, and image recognition but the concepts are applicable to all use cases.


By the end of the day, you will be able to use models in their day-to-day work. You will also walk away with a high-level understanding of the most useful machine learning models such SVMs, Logistic Regression and Naive Bayes work and when to use them.  Special attention is given to Deep Learning and in particular Convolutional Neural Networks for vision.


We try to keep the class collaborative and fun.


Intro to Machine Learning/Data Science Python Libraries

  1. Scikit-learn

  2. Numpy

  3. Pandas

  4. TensorFlow

  5. Keras


Intro to Cloud Machine Learning Platforms

  1. Google Cloud ML

  2. Azure ML

  3. Amazon ML


This class is designed for working engineers with no experience in machine learning.  Some students have taken this class after taking an online machine learning course and have enjoyed the practical applications and review.  

The entire class is taught in python, but on average, 20 percent of students take the class with no experience in python and mostly report success.  If you are not familiar with python, be extra sure to have everything installed in advance and consider doing a quick online tutorial.


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, come thirty minutes early and we'll help you with the installation or email us in advance.


Download code for the class from


There are instructions on this website for how to install all the necessary programs at - 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 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.




8:30 - 9:00 (Optional) Setup your laptop

Come early and get everything set up.  If you had any trouble installing software be sure to show up early!

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

We will take a break for lunch and have extra time for questions and other topics.

1:00-2:30 Deploying machine learning and try the Common Machine Learning Platforms

We will deploy our sentiment classifier in a webserver and talk about some of the common issues that come up.

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.

2:30-4:00 Introduction to Keras, TensorFlow and Deep Neural Networks

We will learn how deep neural networks work and actually build some.  We start with handwriting image recognition and build perceptrons, multi layer neural networks and convolutional neural networks.

4:00-5:00 Transfer Learning

We will go over taking common existing deep learning networks and repurposing them for other applications with a focus on vision.

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.”

Corporate Training:

We also do corporate trainings - send us an email for details.

Have questions about Technical Introduction to AI, Machine Learning & Deep Learning Part 1? Contact Engineered Education

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When & Where

San Francisco, CA 94105

Tuesday, November 7, 2017 from 9:00 AM to 7:00 PM (PST)

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Engineered Education

Engineered Education’s mission is to offer high-quality classes to engineers and founders.

By integrating education from proven entrepreneurs and industry experts, we give you the tools to take your skill to the next level.

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Technical Introduction to AI, Machine Learning & Deep Learning Part 1
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