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Control of Mobile Robots Coursera

@machinelearnbot

About this course: Control of Mobile Robots is a course that focuses on the application of modern control theory to the problem of making robots move around in safe and effective ways. The structure of this class is somewhat unusual since it involves many moving parts - to do robotics right, one has to go from basic theory all the way to an actual robot moving around in the real world, which is the challenge we have set out to address through the different pieces in the course.


SQL Server Machine Learning Services – Part 2: Python Data Frames - Simple Talk

#artificialintelligence

If you've spent any time with the R language in SQL Server R Services or Machine Learning Services (MLS), you're no doubt aware of the important role that data frames can play in your scripts, whether working with data that comes from a SQL Server database or from another source. The same is true for Python. You use data frames when passing data sets into and out of a script as well as when manipulating and analyzing data within that script. This article focuses on using data frames in Python. It is the second article in a series about MLS and Python. The first article introduced you briefly to data frames. This article continues that discussion, describing how to work with data frame objects and the data within those objects.


?siteID=.YZD2vKyNUY-fQmX6du.oGh0QvXhsQ9Czg&LSNPUBID=*YZD2vKyNUY

@machinelearnbot

Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.


neural networks for beginners from scatch Udemy

@machinelearnbot

What is machine learning / ai? How to lean machine learning in practice? Machine learning and neural networks are got a lot of attention recently. Self driving cars, predictive analytics and other highly advanced topics are closely related to those topics. Therefore it's of the utmost of importance to familiarize oneself with machine learning and neural networks.


Do L.A. Unified's daily random searches keep students safe, or do they go too far?

Los Angeles Times

L.A. Unified requires daily random searches for weapons using metal-detector wands at all of its middle and high school campuses, including Hamilton High. L.A. Unified requires daily random searches for weapons using metal-detector wands at all of its middle and high school campuses, including Hamilton High. Kevin Castillo was in his freshman year at Hamilton High School when administrators carrying hand-held metal detectors interrupted his English class to conduct a random search. They asked a student to pick a number between 1 and 10. The student chose 7, so every seventh person in the class had to gather up belongings and step out of the classroom.


Applied Machine Learning in R Udemy

@machinelearnbot

They are powerful data mining techniques that allow you to detect patterns in your data or variables. For each technique, a number of practical exercises are proposed. By doing these exercises you'll actually apply in practice what you have learned. This course is your opportunity to become a machine learning expert in a few weeks only! With my video lectures, you will find it very easy to master the major machine learning techniques. Everything is shown live, step by step, so you can replicate any procedure at any time you need it. So click the "Enroll" button to get instant access to your machine learning course. It will surely provide you with new priceless skills. And, who knows, it could give you a tremendous career boost in the near future.


?siteID=.YZD2vKyNUY-xSSv.Zk45H0.B8e6hBZs6w&LSNPUBID=*YZD2vKyNUY

@machinelearnbot

We've talked about, speculated and often seen different applications for Artificial Intelligence - But what about one piece of technology that will not only gather relevant information, better customer service and could even differentiate your business from the crowd? ChatBots are here, and they came change and shape-shift how we've been conducting online business. Fortunately technology has advanced enough to make this a valuable tool something accessible that almost anybody can learn how to implement. If you want to learn one of the most attractive, customizable and cutting edge pieces of technology available, then this course is just for you!


Machine Learning with TensorFlow for Business Intelligence

@machinelearnbot

The best job to have in 2017 according to Glassdoor? The #1 skill you need to start a career in Data Science? So, if you are interested in a career in data science, algorithmic trading, robotics, or any industry where human labor is getting replaced by machines, you have come to the right place! We have prepared an amazing course not only to get you acquainted with, but help you understand how deep machine learning works! Worried you have no experience?


Convolutional Neural Networks Coursera

@machinelearnbot

About this course: This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. This is the fourth course of the Deep Learning Specialization.


Data Science with Python: Exploratory Analysis with Movie-Ratings and Fraud Detection with Credit-Card Transactions

@machinelearnbot

The following problems are taken from the projects / assignments in the edX course Python for Data Science and the coursera course Applied Machine Learning in Python (UMich). The IMDB Movie Dataset (MovieLens 20M) is used for the analysis. The dataset is downloaded from here . This dataset contains 20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users and was released in 4/2015. Understand the trend in average ratings for different movie genres over years (from 1995 to 2015) and Correlation between the trends for different genres (8 different genres are considered: Animation, Comedy, Romance, Thriller, Horror, Sci-Fi and Musical).