We deploy the real environment, you take the scenario-based labs on us. Hands-on, from anywhere, at any time.
Learn the Linux Academy basics! Discover how to start and connect to your six Linux lab servers, learn about the learning tools we made just for you, and find out the best way to put our content to work. This module updates as we add new features to the Linux Academy!
This is the Introduction to Amazon Machine Learning course.We will cover some basic Machine Learning (ML) concepts to start off and then dive into Amazon ML specifics. We will learn about the different types of modelling available and how to get the predictions
This course is for those new to the Elastic Stack to get an introductory overview of its core services (Elasticsearch, Logstash, Kibana, Beats), features, terms, and basic administration. This course will follow a real-world use case of setting up a log aggregation pipeline for web access logs and analyzing said logs with Kibana via search, visualizations, and dashboards.
This course covers all of the objectives to study and pass CompTIA's Cloud Essentials certification. The CompTIA Cloud Essentials certification focuses on the real-world issues and practical solutions of cloud computing in business and IT. It’s the preferred cloud certification for business professionals and non-IT staff. While it isn’t a technical-heavy certification, its coverage of cloud computing principles is anything but superficial.
Big Data Essentials is a comprehensive introduction to the world of Big Data. Starting with the definition of Big Data, we describe the various characteristics of Big Data and its sources. Using real world examples, we highlight the growing importance of Big Data. We discuss architectural requirements and principles of Big Data infrastructures and the intersection of cloud computing with Big Data. We also provide an overview of the most popular Big Data technologies including core Hadoop, the Hadoop ecosystem (Hive, Pig, Sqoop, Flume, Kafka, Storm, Ambari, Oozie, Zookeeper), NoSQL databases and Apache Spark. We conclude this lesson with a tour of the different types of Analytics that can be performed on Big Data and various techniques and tools used.
The wave of the Internet of Things has been awash of enterprises and DIY enthusiasts for some time now. Microsoft's Azure platform for IoT brings a comprehensive suite of offerings that is further extended by the Azure infrastructure itself. This course is designed to give the student a holistic understanding of the offering Azure has for IoT while also bringing a hands-on approach to utilizing those features and services. We will cover topics that include architecture, security, development, analytics, and create a simulated device during the duration of this course. The student will be able to, once the course is complete, understand what Azure IoT is capabable of and also design and implement a simple IoT Solution.
Follow right on the heels of the Elastic Stack Essentials course with the Elasticsearch Deep Dive. Get to understand and go hands-on with the core functionality of Elasticsearch (installing, indexing, querying). Next, learn how to configure it for production use with TLS encryption, user access control, monitoring, and alerting with X-Pack and automated management with Elasticsearch Curator. Get to understand best practices around heap and cluster sizing, hardware requirements, and performing live upgrades.
In this course, you will develop the skills that you need to write effective and powerful scripts and tools using Python. We will go through the necessary features of the Python language to be able to leverage its additional benefits in writing scripts and creating command line tools (data types, loops, conditionals, functions, error handling, and more). Beyond the language itself, you will go through the full development process including project set up, planning, and automated testing to build two different command line tools.
This course provides a comprehensive introduction to Apache Spark. Starting with an overview of Apache Spark and its usage in the Big Data Analytics industry we talk about some of the famous use cases of Apache Spark and its rise in popularity in the industry. The course gives an in-depth explanation of the core concepts of Spark, including runtime architecture and the primary data abstraction of RDDs. The course also guides you through the installation process of Spark and helps you get started with some examples in Spark's command line interface using Python and Scala. The course then gives brief description of Spark's libraries including Spark SQL, Spark MLlib, Spark streaming, Spark GraphX and SparkR. We provide hands-on exercises to practice basic programming in Spark that allows you to further explore the Spark programming API.
This course begins with explaining the need of Machine Learning and how it originated from Aritificial Intelligence and gave rise to deep learning. We explain important concepts in ML including categories of algorithms, statistical and computer science terms used in model creation, feature engineering, overfitting, generalization, underfitting and cross validation. We also dive into the topic of data science and discuss why ML is an important part of data science.
The course then provides hands on training on Azure Machine Learning, giving a tour of ML Studio, its various features and the concept of an experiment. We demonstrate the process of creating ML experiments and create predictive models to predice automobile prices and generate recommendations for movies.
The exercises in this course allow the student to get familiar with Azure Machine Leaning and gain confidence in exploring the tool further.
The Google Cloud Professional Data Engineer is able to harness the power of Google's big data capabilities and make data-driven decisions by collecting, transforming, and visualizing data. Through designing, building, maintaining, and troubleshooting data processing systems with a particular emphasis on the security, reliability, fault-tolerance, scalability, fidelity, and efficiency of such systems, a Google Cloud data engineer is able to put these systems to work.
This course will prepare you for the Google Cloud Professional Data Engineer exam by diving into all of Google Cloud's data services. With interactive demonstrations and an emphasis on hands-on work, you will learn how to master each of Google's big data and machine learning services and become a certified data engineer on Google Cloud.
IoT, or Internet of Things, may be considered a buzzword; however, this buzzword has now become the industry's next promise to a further connected and digital industry. Enterprises continue to move towards a cost-per-use model while ensuring security, integration, long-term ROI, and agility are present in their architecture. With the introduction of an end-to-end solution platform, Microsoft's Azure platform for IoT brings a comprehensive suite of offerings that is further extended by the Azure infrastructure itself. This course is designed to give advanced users of Azure and IoT platforms an understanding of the features and options the platform brings. We will focus on various internal realms such as DevOps, development, security, architecture, compliance, hardware, integration, and best practices to ensure a deeper in-depth look is given to tackle specific environments Enterprises deal with daily.
Big data is one of the most exciting and in-demand skills, powering large companies such as Google and Facebook. It is no wonder Amazon Web Services (AWS) has realeased a big data certification to catalyze the move to AWS big data solutions.
This in-depth course will provide you with the required knowledge needed to be prepared to take the AWS Big Data Specialty Certification. We will be covering the various big data products available and build highly scalable and secure big data applications.