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.

This is a collection of free resources beyond the regularly shared books, MOOCs, and courses, mostly from over the past year. They start from zero and progress accordingly, and are suitable for individuals looking to pick up some of the basic ideas, before hopefully branching out further (see the final 2 resources listed below for more on that). These resources are not presented in any particular order, so feel free to pursue those which look most enticing to you. All credit goes the the individual authors of the respective materials, without whose hard work we would not have the benefit of learning from such great content. For many good reasons, much of the highest quality machine learning educational resources tend to have a very strong focus on theory, especially at the beginning.

When we encounter artificial intelligence in the media, it's often discussed at extremes. At one end, there are films, books, games and even news commentary that paint a picture of a world-ending intelligence. At the other end, people picture algorithms so powerful they can solve every major problem facing mankind. In reality, the capabilities for AI lie somewhere in between. For example, some of Elsevier's products use machine-learning driven image identification to better diagnose life-threatening illnesses – but these are tools are designed to aid the deductive work of human experts, not replace them.

Data management and data analytics are two critical fundamental resources that should be used by Thai enterprises and tech startups to develop innovative services backed by artificial intelligence (AI) technology, instead of working to develop intelligent products or services to compete with global players. Chai Wutiwiwatchai, research unit director of the National Electronics and Computer Technology Center (Nectec), said local enterprises can benefit from the many data sets held by state agencies through 20 ministries and the private sector. "Intelligent products and services driven by AI may not be easy to enter for local enterprises and startups, as there are too many global tech players and AI tech-embedded tools available for free in the market," he said. But the government must urgently digitise the existing data sets of all agencies, 70% of which are stored on paper and in portable document format (PDF) files. Speaking on the sidelines of the "AI Shapes the Future" forum last week, Mr Chai said innovative products and services embedded with AI tech have been increasingly accessible in the global market for four years, especially through popular use cases of image recognition, biometrics, cybersecurity and smart speakers.

Statistics and machine learning are two very closely related fields. In fact, the line between the two can be very fuzzy at times. Nevertheless, there are methods that clearly belong to the field of statistics that are not only useful, but invaluable when working on a machine learning project. It would be fair to say that statistical methods are required to effectively work through a machine learning predictive modeling project. In this post, you will discover specific examples of statistical methods that are useful and required at key steps in a predictive modeling problem.