Machine Learning A-Z: Hands-On Python & R In Data Science by Kirill Eremenko, Hadelin de Ponteves and SuperDataScience Team will teach you Machine Learning using Python & R. These Python videos will teach you everything related to Machine Learning. You will build an army of powerful Machine Learning models. Then you will combine them to solve any problem. You will be able to handle different topics like Reinforcement Learning, NLP and Deep Learning. Using the knowledge you gain, you will know which Machine Learning model to use depending on the problem.

This half-day workshop aims at exploring emerging research in the areas of linked data in robotics and Industry 4.0. Industry 4.0 is a collective term (created in Germany) for the technological concepts of cyber-physical systems, the Internet of Things and the Internet of Services, leading to the vision of the Smart Factory. Within a modular structured Smart Factory, cyber-physical systems monitor physical processes, and make decentralized decisions. Over the Internet of Things, cyber-physical systems communicate and cooperate with each other and humans in real time. In addition, one of the aims in robotics is to build smarter robots that can communicate, collaborate and operate more naturally and safely.

Earlier this year, Mattel announced that it was partnering with Tynker to bring Barbie-themed coding lessons to young kids. As of today, six free coding experiences are now available as is a new STEM-themed doll -- Robotics Engineer Barbie. The lessons are geared towards beginners, kindergarten-aged and older, and aim to teach logic, problem-solving and the basics of coding. While they learn, kids can also take on different career roles alongside Barbie, including musician, astronaut, pastry chef, robotics engineer, farmer and beekeeper. "Our mission is to empower youth to become the makers of tomorrow through coding, and the Barbie brand is an ideal partner to help us introduce programming to a large number of kids in a fun, engaging way," Tynker CEO Krishna Vedati said in a statement.

Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples. If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass is the only course that you need for machine learning on iOS.

Did you know that you can write R and Python code within your T-SQL statements? Machine Learning Services in SQL Server eliminates the need for data movement. Instead of transferring large and sensitive data over the network or losing accuracy with sample csv files, you can have your R/Python code execute within your database. Easily deploy your R/Python code with SQL stored procedures making them accessible in your ETL processes or to any application. You can install and run any of the latest open source R/Python packages to build Deep Learning and AI applications on large amounts of data in SQL Server.

Imagine having to narrow down a video of passing vehicles captured on a traffic camera to red SUVs that are exceeding the speed limit. Such queries over images, videos and text are an increasingly common use-case for data analytics platforms. A query to identify in-a-hurry red SUVs has to execute several machine learning modules on the video, for example, to detect parts of a video frame that contain vehicles, to classify the vehicles by type (SUV, truck, and so on), to track vehicles across frames and to estimate the speed of every moving vehicle. Eventually, a predicate picks the frames that have red SUVs traveling above the speed-limit. Unfortunately, such queries are slow today because the machine learning modules can be computationally intensive.

AI, machine learning and deep learning are everywhere nowadays. But can these technologies be applied to protect intellectual property online? How can they be brought to IP enforcement in real life? Red Points has been applying machine-learning technology in features such as keyword monitoring and image recognition. They are great examples of how technology can improve the quality of the brand protection tasks, while decreasing costs over time.

We specialize the decoupled extended Kalman filter (DEKF) for online parameter learning in factorization models, including factorization machines, matrix and tensor factorization, and illustrate the effectiveness of the approach through simulations. Learning model parameters through the DEKF makes factorization models more broadly useful by allowing for more flexible observations through the entire exponential family, modeling parameter drift, and producing parameter uncertainty estimates that can enable explore/exploit and other applications. We use a more general dynamics of the parameters than the standard DEKF, allowing parameter drift while encouraging reasonable values. We also present an alternate derivation of the regular extended Kalman filter and DEKF that connects these methods to natural gradient methods, and suggests a similarly decoupled version of the iterated extended Kalman filter.

Pay what you want for the Total Python Machine Learning Bundle and you'll get the Easy Introduction To Recommendation Systems course which teaches you about what goes into designing and implementing recommendation systems. If you beat the average price ($9.08 at the time of writing), you unlock 7 more courses focusing on Machine Learning. The courses include An Easy Introduction To Python, Deep Learning On The Google Cloud ML Engine, Advanced Deep Learning With Neural Networks, Unsupervised Deep Learning, AI And Deep Learning, Machine Learning And NLP in Python, and Machine Learning Using Scikit-Learn. Note: The Techdirt Deals Store is powered and curated by StackCommerce. A portion of all sales from Techdirt Deals helps support Techdirt.