This online course is designed to introduce you to the Spark platform and train you to use all the APIs it offers. This course is split into 8 interactive modules which cover over 7 Apache Spark topics with a Project at the end of the course – receive a certificate of completion at the end of the course.
BENGALURU: The humongous amount of digital data being generated, and companies' need to glean insights and make predictions from them have made skills in data visualisation, data science, and machine learning among the most valued for technology recruiters today. This is reflected in the number of working professionals signing up for specialised courses in these spaces. Candidates who complete the courses tend to get between 20% and 50% increase in salaries. Kashyap Dalal, chief business officer at online learning platform Simplilearn, says that big data and analytics courses were the big growth drivers in the past three years. While data science continues to remain popular, accounting for 30% of all learners, courses on visualisation tools and machine learning have become very attractive over the past six months, he said. Almost 25% of Simplilearn's applicants have opted for machine learning. Machine learning reduces the need for human intervention and speeds up analysis, and with ...
In particular, it is particularly amenable to machine learning and interactive data workloads, and can provide an order of magnitude greater performance than traditional Hadoop data processing tools. The course covers the fundamentals of Spark, it's core APIs and design, relational data processing with Spark SQL, the fundamentals of Spark job execution, performance tuning, tracking and debugging. Users will get hands-on experience with processing streaming data with Spark streaming, training machine learning algorithms with Spark ML and R Server on Spark, as well as HDInsight configuration and platform specific considerations such as remote developing and access with Livy and IntelliJ, secure Spark, multi-user notebooks with Zeppelin, and virtual networking with other HDInsight clusters.
It's now becoming common for me to hear that product owners/managers, technical managers and designers are turning to popular online courses to learn about machine learning (ML). I always encourage it -- in fact, I did one of those courses myself (and blogged about it). However, it's not always clear how much benefit someone whose goal is to design, support, manage, or plan for products that use machine learning will get from doing an online course in ML. These courses throw you into the deep end, asking you to start programming classifiers, when many non-technical team mates are only looking for sufficient knowledge to be able to work in teams that are creating an ML-driven product. It's a bit like wanting to drive a car, and'therefore' signing up to a course on combustion engines -- probably a little bit too detailed for practical day-to-day driving!
If you're a software engineer (or someone who's learning the craft), chances are that you've heard about deep learning (which we'll sometimes abbreviate as "DL"). It's an interesting and rapidly developing field of research that's now being used in industry to address a wide range of problems, from image classification and handwriting recognition, to machine translation and, infamously, beating the world champion Go player in four games out of five.