Tue October 10, 2017
AI Education Series Part 1: Intro to AI, Machine Learning & Deep Learning
SEE EVENT DETAILS
at San Francisco
(see times)
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
Scikit-learn
Numpy
Pandas
TensorFlow
Keras
Intro to Cloud Machine Learning Platforms
Google Cloud ML
Azure ML
Amazon ML
Prerequisites:
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).
Preparation:
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 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.
Instructor:
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.
Curriculum:
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.
show less
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
Scikit-learn
Numpy
Pandas
TensorFlow
Keras
Intro to Cloud Machine Learning Platforms
Google Cloud ML
Azure ML
Amazon ML
Prerequisites:
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).
Preparation:
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 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.
Instructor:
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.
Curriculum:
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.
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
Scikit-learn
Numpy
Pandas
TensorFlow
Keras
Intro to Cloud Machine Learning Platforms
Google Cloud ML
Azure ML
Amazon ML
Prerequisites:
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).
Preparation:
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 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.
Instructor:
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.
Curriculum:
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.
read more
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
Scikit-learn
Numpy
Pandas
TensorFlow
Keras
Intro to Cloud Machine Learning Platforms
Google Cloud ML
Azure ML
Amazon ML
Prerequisites:
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).
Preparation:
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 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.
Instructor:
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.
Curriculum:
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.
show less
Date/Times:
San Francisco
San Francisco, San Francisco, CA 94103
The Best Events
Every Week in Your Inbox
From Our Sponsors
UPCOMING EVENTS
Great suggestion! We'll be in touch.
Event reviewed successfully.