About this course: The value of IoT can be found within the analysis of data gathered from the system under observation, where insights gained can have direct impact on business and operational transformation. Through analysis data correlation, patterns, trends, and other insight are discovered. Insight leads to better communication between stakeholders, or actionable insights, which can be used to raise alerts or send commands, back to IoT devices. With a focus on the topic of Exploratory Data Analysis, the course provides an in-depth look at mathematical foundations of basic statistical measures, and how they can be used in conjunction with advanced charting libraries to make use of the world's best pattern recognition system – the human brain. Learn how to work with the data, and depict it in ways that support visual inspections, and derive to inferences about the data.
The ground breaking advancements in Artificial Intelligence (AI) is creating tremendous opportunities for transforming industries and virtually every aspect of our existence. The demand for people well versed with AI skill-set is exploding and it is expected to generate over 50 million new jobs in the coming years. There are thousands of educational materials out there for learning Artificial Intelligence (AI). However, much of AI Tutorials and Courses are without Graded Assignments or Peer Feedback and have a poor quality. So, I decided to inspire your AI journey by compiling the best courses from the world-class educators, so that you can learn to create AI powered applications that can give you an edge in becoming a skilled AI -- Engineer/ Scientist/ Researcher/ Practitioner.
This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer. Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner. Therefore an applied knowledge of working with Apache Spark is a great asset and potential differentiator for a Machine Learning engineer. After completing this course, you will be able to: - gain a practical understanding of Apache Spark, and apply it to solve machine learning problems involving both small and big data - understand how parallel code is written, capable of running on thousands of CPUs.
In our latest inspection of Github repositories, we focus on "data science" projects. Unlike other searches we have performed over the past several months, nearly all of the repositories which show up (listed by number of stars* in descending order) are resources for learning data science, as opposed to tools for doing. As such, this is much less a software listing than it is a collection of tutorials and educational resources. There are, however, a few software surprises in here as well, such as a data science-oriented IDE and a great notebook-related project. We include, however, the standard informational notification we have placed on our previous Github Top 10 lists: open source tools have been used by 73% of data scientists in the past 12 months, according to a recent KDnuggets survey (and accounting for the 12 months prior to the survey).
IBM has a clear strategy towards Open Tech AI. I'm sure you remember back in 2011 when IBM Watson defeated the two world champions in Jeopardy. Apart from being backed by the awesome number of 2,880 IBM POWER7 cores providing 11,520 hyper-threads and 16 TB main memory, IBM Watson was running on Linux and using Hadoop. A lot of things have happened since then, and we are at a stage where IBM is one of the world leaders in AI Technology and Services. But what I'm especially proud of is IBM's clear strategy to create, support, and enhance Open Tech AI.