IBM Cognitive Class
Simplifying data pipelines with Apache Kafka
When you hear the terms, producer, consumer, topic category, broker, and cluster used together to describe a messaging system, something is brewing in the Kafka pipelines. Get connected and learn what that is, and what it means! Many Big Data use cases have one thing in common - the use of Apache Kafka somewhere in the mix. Whether the distributed, partitioned, replicated commit log service is being used for messaging, website activity tracking, stream processing, or more, there's no denying it is a hot technology. In this course, you will learn how Kafka is used in the real world and its architecture and components. You will quickly get up and running, producing and consuming messages using both the command line tools and the Java APIs. You also will get hands-on experience connecting Kafka to Spark, and working with Kafka Connect. Course Syllabus Lesson 1 - Introduction to Apache Kafka What Kafka is and why it was created The Kafka Architecture The main components of Kafka Some of the use cases for Kafka Lesson 2 - Kafka Command Line The contents of Kafka's /bin directory How to start and stop Kafka How to create new topics How to use Kafka command line tools to produce and consume messages Lesson 3 - Kafka Producer Java API The Kafka producer client Some of the KafkaProducer configuration settings and what they do How to create a Kafka producer using the Java API and send messages both synchronously and asynchronously Lesson 4 - Kafka Consumer Java API The Kafka consumer client Some of the KafkaConsumer configuration settings and what they do How to create a Kafka consumer using the Java API Lesson 5 - Kafka Connect and Spark Streaming Kafka Connect and how to use a pre-built connector Some of the components of Kafka Connect How to use Kafka and Spark Streaming together
GratisAI Concepts
Learn the foundational concepts of Artificial Intelligence. Understand and discuss terms like machine learning, deep learning, and neural networks. Become familiar with fields of applications in AI including NLP, speech recognition, computer vision, and self-driving cars. This course is perfect for everyone including professionals, managers, executives and students. Immerse yourself in the realm of Artificial Intelligence (AI) with our comprehensive short course. This course is designed to provide a solid foundation in AI concepts, terminology, and its wide array of application areas. From Machine Learning to Neural Networks, from Natural Language Processing (NLP) to Self-driving Cars, this course covers it all. Delve into the fundamental concepts of AI and understand how it enables machines to learn, reason, and make intelligent decisions. Explore the key terminologies associated with AI, including Machine Learning, Deep Learning, Neural Networks, and more. Gain insights into the underlying principles that drive these technologies and their practical applications. Discover the diverse application areas of AI that are revolutionizing industries and transforming our daily lives. Dive into the fascinating world of NLP, where machines are trained to understand and interpret human language, opening up possibilities in chatbots, language translation, and sentiment analysis. Explore the realm of computer vision, where AI algorithms enable machines to recognize objects, faces, and even emotions. Uncover the cutting-edge advancements in speech recognition and synthesis, empowering AI to interact with humans through spoken language. And venture into the realm of self-driving cars, where AI is reshaping transportation and paving the way for autonomous vehicles. Enrol today to unlock the potential of AI and embark on an exciting journey into the realm of intelligent machines. Course Syllabus Module 1: Machine Learning, Deep Learning and neural Networks Terminology and Related Concepts Machine Learning Machine Learning Techniques and Training Deep Learning Neural Networks Summary Module 2: AI Application Areas Key Fields of Application in AI Natural Language Processing, Speech, Computer Vision Self Driving Cars Summary
GratisMachine Learning – Dimensionality Reduction
Welcome to this machine learning course on Dimensionality Reduction. Dimensionality Reduction is a category of unsupervised machine learning techniques used to reduce the number of features in a dataset. Dimension reduction can also be used to group similar variables together. In this course, you will learn the theory behind dimension reduction, and get some hands-on practice using Principal Components Analysis (PCA) and Exploratory Factor Analysis (EFA) on survey data. The code used in this course is prepared for you in R. About This Course Welcome to this machine learning course on Dimensionality Reduction. Dimensionality Reduction is a category of unsupervised machine learning techniques used to reduce the number of features in a dataset. Dimension reduction can also be used to group similar variables together. In this course, you will learn the theory behind dimension reduction, and get some hands-on practice using Principal Components Analysis (PCA) and Exploratory Factor Analysis (EFA) on survey data. The code used in this course is prepared for you in R.
GratisAI Ethics
Artificial Intelligence (AI) presents many exciting opportunities for businesses. At the same time it has also put in focus potential ethical problems. In this course on AI Ethics as we delve into today's AI concerns, explore biases, navigate regulations, and learn the concept of trust in the AI world. Explore the ethical and regulatory landscape of Artificial Intelligence (AI) with our thought-provoking short course. Designed for individuals of all backgrounds, this program delves into critical issues, concerns, and considerations surrounding AI, including ethics, bias, regulations, and the concept of Trustworthy AI. In this course, you will embark on a journey to understand the broader implications of AI beyond its technical aspects. Gain insights into the ethical challenges that arise when AI systems make decisions that impact individuals and society as a whole. Explore the potential biases that can emerge in AI algorithms and learn how to mitigate them to ensure fairness and inclusivity. Discover the existing and emerging regulations and guidelines governing AI development and deployment. Understand the ethical frameworks and principles that are being established to guide responsible AI practices. Explore case studies and real-world examples that shed light on the practical implementation of ethical guidelines in various industries and sectors. Learn about Trustworthy AI and the principles and practices that contribute to building AI systems that are reliable, transparent, and accountable. Understand the importance of ensuring privacy, security, and interpretability in AI algorithms and models. Gain insights into the role of human oversight and governance in maintaining the trustworthiness of AI systems. Whether you are an AI practitioner, a decision-maker, or simply an individual interested in the ethical implications of AI, this course will equip you with the knowledge and tools to navigate the ethical challenges associated with AI. Course Syllabus Module 1: AI Concerns Exploring Today's AI Concerns Exploring AI Ethics Defining AI Ethics Summary Module 2: AI Bias, Regulations, and Trustworthy AI Understanding Bias and AI Lab: Detect the Bias AI Ethics and Regulations AI Ethics, Goverenance, and ESG Foundations of Trustworthy AI Summary
GratisSpark Fundamentals II
Building on your foundational knowledge of Spark, take this opportunity to move your skills to the next level. With a focus on Spark Resilient Distributed Data Set operations this course exposes you to concepts that are critical to your success in this field. ABOUT THIS COURSE Expand your knowledge of the concepts discussed in Spark Fundamentals I with a focus on RDDs (Resilient Distributed Datasets). RDDs are the main abstraction Spark provides to enable parallel processing across the nodes of a Spark cluster. Get in-deptth knowledge on Spark’s architecture and how data is distributed and tasks are parallelized. Learn how to optimize your data for joins using Spark’s memory caching. Learn how to use the more advanced operations available in the API. The lab exercises for this course are performed exclusively on the Cloud and using a Notebook interface. IBM Data Science Experience provides you with Jupyter notebooks that is already connected to Spark and supports Python, R, and Scala so that you start creating your Spark projects and collaborating with other data scientists. When you sign up, you get free access to Data Science Experience and all other IBM services for 30 days. Start now and take advantage of this offer. COURSE SYLLABUS Module 1 - Introduction to Notebooks Understand how to use Zeppelin in your Spark projects Identify the various notebooks you can use with Spark Module 2 - Spark RDD Architecture Understand how Spark generates RDDs Manage partitions to improve RDD performance Module 3 - Optimizing Transformations and Actions Use advanced Spark RDD operations Identify what operations cause shuffling Module 4 - Caching and Serialization Understand how and when to cache RDDs Understand storage levels and their uses Module 5 - Develop and Testing Understand how to use sbt to build Spark projects Understand how to use Eclipse and IntelliJ for Spark development
GratisNoSQL and DBaaS 101
In this NoSQL course, we will provide an overview of the NoSQL database landscape, the benefits of using a Database-as-a-Service offering, and where Cloudant fits into the picture. Additionally, we’ll get you started with using Cloudant by providing tutorials on account sign up, creating and replicating databases, loading and querying data, and conclude by pointing you to additional resources to continue on your education. About This Course Are you building a new application and want to utilize an operational datastore that has a flexible schema for fast and simple development? Do you need to ensure your entire application stack can scale elastically to accommodate a fast-growing dataset and a surge in concurrent users? Are you struggling with the management of an existing datastore and want to offload administration to a service provider? Do you require high availability and disaster recovery redundancy across nodes, data centers, geographies or asynchronous mobile/client access to application data? If you answered yes to any of the questions above, then you have probably started to explore NoSQL and/or Database-as-a-Service offerings. In this NoSQL course, we will provide an overview of the NoSQL database landscape, the benefits of using a Database-as-a-Service offering, and where Cloudant fits into the picture. Additionally, we’ll get you started with using Cloudant by providing tutorials on account sign up, creating and replicating databases, loading and querying data, and conclude by pointing you to additional resources to continue on your education. Course Syllabus After completing this course, you should be able to: Define NoSQL, its characteristics and history, and the primary benefits for using NoSQL databases. Define the major types of NoSQL databases including a primary use case and advantages/disadvantages of each type. Describe the factors affecting return on investment for using locally hosted database vs. database-as-a-service. List the key benefits of IBM Cloudant, a NoSQL Database-as-a-Service for Web and mobile applications. Create a document database, add documents, and run queries using IBM Cloudant. Access additional resources including training, documentation, articles, and books to continue to learn about NoSQL databases.
GratisUsing R with Databases
The intent of this course is to teach you how to unlock the power and magic of R to analyze data in relational databases. It will show you how to connect to relational databases, access and query the database, update and modify the data, analyze it and perform simple visualizations. About This Course Do you want to leverage the power of R to unlock the value of data in relational databases? Are you a SQL professional and looking to get skilled in data science? Are the limits of R programming language preventing you from analyzing very large data sets? If you answered yes to any of these questions, this course is for you. It will introduce you to the benefits of using R with databases. Teach you how to connect to databases from R. Show you how to create database objects, populate the database, and issue SQL queries to retrieve and modify your data from R. The course will also delve into advanced topics of using stored procedures and utilizing in-database analytics with R. Course Syllabus Module 1 - R and Relational Databases Module 2 - Connecting to Relational Databases using RJDBC and RODBC Module 3 - Database Design and Querying Data Module 4 - Modifying Data and Using Stored Procedures Module 5 - In-Database Analytics with R
GratisGetting started with Microservices with Istio and IBM Cloud Kubernetes Service
Discover how microservices and Istio pair together for cloud-native apps. Learn how Istio and IBM Cloud Kubernetes Service help you securely and seamlessly deploy containers and apps. About this course In this course, you learn about the twelve-factor app methodology, microservices, and Istio foundational technologies. These cloud-native technologies are the essentials as you build new apps for the cloud. Microservices are the building blocks for your cloud architecture. Then, you layer on Istio to help you connect, manage, and secure those microservices. All of those are then put together in IBM Cloud Kubernetes Service. What will I get after passing this course? You will receive a completion certificate. Course syllabus Twelve-factor app Twelve-factor methodology The twelve factors Codebase Dependencies Configuration Backing services Build, release, run Processes Port binding Concurrency Disposability Development and production parity Logs Admin processes Summary of the twelve-factor app Microservices Unmaintainable, monolithic apps What are microservices? Microservices: Making developers more efficient Microservices architecture Example app that uses microservices Key tenets of a microservices architecture Comparing monolithic and microservices architectures Emergence of microservices from modern development tools and processes Operational requirements for microservices Summary: Advantages of microservices Application architecture evolution: From SOA to microservices Layered application architecture SOA stack Microservices and SOA Monolithic architecture versus microservices architecture Microservices and IBM Cloud Kubernetes Service Microservices component architecture Microservices architecture mapped to the SOA stack Microservices types hierarchy Language decisions Backend for frontend (BFF) Business service microservices dependencies: Typical Business service microservices dependencies: Death Star Microservices integration Communication among services Synchronous versus asynchronous communication Microservices intercommunication Microservices communication in IBM Cloud Kubernetes Service IBM Message Hub service Service mesh Comparison of operations for monolithic and microservice architectures A service mesh can help Service registry Service discovery and service proxy Client-side discovery Server-side discovery Automated testing Circuit breaker Bulkhead Istio: An intelligent service mesh for microservices Service mesh implementation How Istio works Istio mesh request flow What Istio provides for microservices architectures Discovery and load balancing Handling failures Fault injection Mutual TLS Authentication Final exam
GratisPredictive Modeling Fundamentals I
Predictive Analytics brings together advanced analytics capabilities spanning ad-hoc statistical analysis, predictive modeling, data mining, text analytics, entity analytics, optimization, real-time scoring, machine learning and more. IBM SPSS Modeler puts these capabilities into the hands of business users, data scientists, and developers. About This Course In this course you will learn the basics to get started with Predictive Modeling. Course Syllabus After completing this course, you should be able to: - Describe what Predictive Modeling is all about and know why you would want to use it - Understand the CRISP-DM methodology and the IBM SPSS Modeler Workbench - Understand Common Modeling Techniques - Use IBM SPSS Modeler to solve a Kaggle competition - Explore, Prepare, Model and Evaluate your data using IBM SPSS Modeler
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