IBM Cognitive Class
A Quick Introduction to Machine Learning
Machine Learning, a foundational component of Artificial Intelligence, is often shrouded in mystery. This short course will demystify and explain the subject of Machine Learning. In just a few hours, you will be able to understand the concepts and the processes behind this revolutionary technology. Let this course take you into the forefront of the Machine Learning revolution! By actively engaging with the course content, including video lectures, quizzes, and a hands-on lab, you will develop the skills and confidence necessary to effectively apply your newfound knowledge in Machine Learning. Course Syllabus Module 1 Introduction to Machine Learning for Everyone Machine Learning Model Lifecycle A Day in the life of a Machine Learning Engineer Tools for Machine Learning Module 2 Supervised vs Unsupervised Learning Classification Regression Evaluating Machine Learning Models Hands-on Lab: Machine Learning in Python
GratisData Visualization with R
Data visualization is the presentation of data with graphics. It's a way to summarize your findings and display it in a form that facilitates interpretation and can help in identifying patterns or trends. In this course you will learn how to create beautiful graphics and charts, customizing the look and feel of them as you wish. ABOUT THIS COURSE "A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large data sets. Data visualization plays an essential role in the representation of both small and large scale data. One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions. The main goal of this course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using the open source language R. COURSE SYLLABUS Module 1 - Basic Visualization Tools Bar Charts Histograms Pie Charts Module 2 - Basic Visualization Tools Continued Scatter Plots Line Plots and Regression Module 3 - Specialized Visualization Tools Word Clouds Radar Charts Waffle Charts Box Plots Module 4 - How to create Maps Creating Maps in R Module 5 - How to build interactive web pages Introduction to Shiny Creating and Customizing Shiny Apps Additional Shiny Features
GratisDeep Learning with TensorFlow
Majority of data in the world are unlabeled and unstructured data, for instance images, sound, and text data. Shallow neural networks cannot easily capture relevant structure in these kind of data, but deep networks are capable of discovering hidden structures within these data. In this course, you will use TensorFlow library to apply deep learning on different data types to solve real world problems. About This Course Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which consitutes the vast majority of data in the world. TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Course Syllabus Module 1 – Introduction to TensorFlow HelloWorld with TensorFlow Linear Regression Nonlinear Regression Logistic Regression Activation Functions Module 2 – Convolutional Neural Networks (CNN) CNN History Understanding CNNs CNN Application Module 3 – Recurrent Neural Networks (RNN) Intro to RNN Model Long Short-Term memory (LSTM) Recursive Neural Tensor Network Theory Recurrent Neural Network Model Module 4 - Unsupervised Learning Applications of Unsupervised Learning Restricted Boltzmann Machine Collaborative Filtering with RBM Module 5 - Autoencoders Introduction to Autoencoders and Applications Autoencoders Deep Belief Network
GratisIntroducing AI
Start your journey into the realm of Artificial Intelligence! Discover its endless possibilities, practical applications, and real-world impact in this captivating beginner-level course. Unveil the future today! Discover the fascinating world of Artificial Intelligence (AI) with this beginner-friendly short course. This mini-course provides an introduction to AI, explores its impact on various industries, and delves into its wide range of applications, including the exciting field of Generative AI. We will demystify the concepts behind AI and explain how it mimics human intelligence to solve complex problems. Gain insights into the transformative power of AI and how it is reshaping industries and society as a whole. Explore the vast array of applications that AI offers across different sectors. From healthcare to finance, from manufacturing to entertainment, AI is revolutionizing the way we work, communicate, and interact. Discover real-world examples that showcase the potential of AI-driven technologies and their positive impact on efficiency, decision-making, and innovation. Uncover the fascinating capabilities of Generative AI models, which have the ability to generate new content, including images, music, and text. Witness the creative potential of AI as we delve into the applications of Generative AI in fields like art, design, and storytelling. Designed with beginners in mind, this course provides a solid foundation in AI concepts without overwhelming technical jargon. Our expert instructors will guide you through engaging lessons, practical examples, and interactive exercises, ensuring an enjoyable and informative learning experience. Gain a comprehensive understanding of AI, explore its impact on society, and unlock the possibilities offered by Generative AI. Embrace the future of technology and enroll in this course today to embark on an exciting AI journey. Course Syllabus Module 1: AI Overview Introducing AI What is AI? Generative AI Overview Summary Module 2: Impact of AI Impact and Examples of AI Some Applications of AI Famous applications of AI from IBM Generative AI: Applications and Examples Summary
GratisBuilding Cloud Native and Multicloud Applications
In this course, we will cover the core concepts and practices of building and running Cloud Native applications and how to run these applications in a multicloud environment. We will cover technologies and practices including; microservices, DevOps, CI/CD, Docker, Kubernetes, and OpenShift. About This Course In this course, we will cover the core concepts and practices of building and running Cloud Native applications and how to run these applications in a multicloud environment. We will cover technologies and practices including; microservices, DevOps, CI/CD, Docker, Kubernetes, and OpenShift. Learning Objectives After completing this course you will be able to: Understand the core principles and practice of build Cloud Native applications How to modernize existing applications to be Cloud Native How to deconstruct and monolithic application in a microservice architecture Build and deploy containers to a Kubernetes cluster Understand the guiding principles and benefits of a CI/CD pipeline How to build a CI/CD pipeline The benefits of adopting a hybrid-cloud and multicloud architectures Syllabus Module 1: Cloud Native and Multicloud Concepts and Goals Course Welcome Video Module Introduction and Learning Objectives What is Cloud Native What are Cloud Native Applications? Achieving Agility Maximizing Operability with DevOps Ensuring Observability for DevSecOps Increasing Resiliency Module Summary Practice Quiz Graded Quiz Module 2: Migrating Apps to Advantage Cloud Infrastructure Module Introduction and Learning Objectives Considerations and benefits of adopting Cloud Native Paths for Application Modernization Application Migration using Containers Data Migration to Cloud Demo: Application Migration Planning Demo: Data Migration (Aspera on IBM Cloud) Lab Module Summary Practice Quiz Graded Quiz Module 3: Modernizing applications to be CN Module Introduction and Learning Objectives What is Architectural Modernization and its Benefits? Adopting a Microservices Architecture Reactive programming with Serverless Why are Containers so important? Leveraging Container Orchestration Platforms Using the IBM Garage Method to Modernize Demo: Using the IBM Garage Method to Modernize Managing Cloud Native Applications on Kubernetes Module Summary Practice Quiz Graded Quiz Module 4: Applying CI/CD to CN applications Module Introduction and Learning Objectives What are the Benefits of employing CI/CD Automating Validation Configuring Infrastructure using GitOps Improving Observability through CI/CD Building and Deploying to the Cloud Demo: IBM Toolchains (IBM Cloud with Tekton) Building Pipelines with Tekton Module Summary Practice Quiz Graded Quiz Module 5: Managing Applications in Multicloud Deployments Module Introduction and Learning Objectives Understanding HC, MC, and Hybrid MC What is MCM and its use cases? What are the capabilities of MCM? Utilizing Hybrid Cloud Platforms to enable MCM Automating MC apps Demo: Cloud Pak for MultiCloud Manager Exploring the OpenShift Advanced Web Console Module Summary Practice Quiz Graded Quiz Module 6: Final Exam Final Exam
GratisR for Data Science
R is a powerful language for data analysis, data visualization, machine learning, statistics. Originally developed for statistical programming, it is now one of the most popular languages in data science. In this course, you'll be learning about the basics of R, and you'll end with the confidence to start writing your own R scripts. R is a powerful language for data analysis, data visualization, machine learning, and statistics. This isn't your typical textbook introduction to R. You're not just learning about R fundamentals; you'll be using R to solve problems related to movie data. Using a concrete example makes learning painless. You will learn about the fundamentals of R syntax, including assigning variables and doing simple operations with one of R's most important data structures -- vectors! You'll then learn about lists, matrices, arrays and data frames from vectors. Then, you'll jump into conditional statements, functions, classes and debugging. Once you've covered the basics - you'll learn about reading and writing data in R, whether it's a table format (CSV, Excel) or a text file (.txt). Finally, you'll end with some important functions for character strings and dates in R. Course Syllabus Module 1 - R basics Math, Variables, and Strings Vectors and Factors Vector operations Module 2 - Data structures in R Arrays & Matrices Lists Dataframes Module 3 - R programming fundamentals Conditions and loops Functions in R Objects and Classes Debugging Module 4 - Working with data in R Reading CSV and Excel Files Reading text files Writing and saving data objects to file in R Module 5 - Strings and Dates in R String operations in R Regular Expressions Dates in R
GratisBuild an IoT Blockchain Network for a Supply Chain
Learn how to use a an IoT Asset Tracking device, build a blockchain network, and configure a Node-RED dashboard to monitor a perishable network supply chain. You can also use a simulated tracker if you don't want to purchase and set up the hardware. When multiple participants, such as farms, manufacturers, processing plants, trucks, ports, ships, distribution centers, and consumer retail outlets, are involved in the safe shipment and payment of cargo, you can use a blockchain network to record immutable transactions as the cargo shipment progresses through its delivery journey. In this course, you'll do the following main tasks: - Configure an asset tracker IoT device (or use the simulator) - Build a blockchain business network in Hyperledger Composer - Build an IoT application to visualize and analyze the data from the asset tracker: -- Create an IoT starter app in IBM Cloud -- Create Node-RED flows to control and receive events from Particle.io, write events to the blockchain network, and load blockchain transaction history -- Use Node-RED to build an asset tracking dashboard
GratisHadoop 101
This beginner Apache Hadoop course introduces you to Big Data concepts, and teaches you how to perform distributed processing of large data sets with Hadoop. About This Course Learn the basics of Apache Hadoop, a free, open source, Java-based programming framework. Why was it invented? Learn about Hadoop's architecture and core components, such as MapReduce and the Hadoop Distributed File System (HDFS). Learn how to add and remove nodes from Hadoop clusters, how to check available disk space on each node, and how to modify configuration parameters. Learn about other Apache projects that are part of the Hadoop ecosystem, including Pig, Hive, HBase, ZooKeeper, Oozie, Sqoop, Flume, among others. BDUprovides separate courses on these other projects, but we recommend you start here. Course Syllabus Module 1 - Introduction to Hadoop Understand what Hadoop is Understand what Big Data is Learn about other open source software related to Hadoop Understand how Big Data solutions can work on the Cloud Module 2 - Hadoop Architecture Understand the main Hadoop components Learn how HDFS works List data access patterns for which HDFS is designed Describe how data is stored in an HDFS cluster Module 3 - Hadoop Administration Add and remove nodes from a cluster Verify the health of a clusterStart and stop a clusters components Modify Hadoop configuration parameters Setup a rack topology Module 4 - Hadoop Components Describe the MapReduce philosophy Explain how Pig and Hive can be used in a Hadoop environment Describe how Flume and Sqoop can be used to move data into Hadoop Describe how Oozie is used to schedule and control Hadoop job execution
GratisPrompt Engineering for Everyone
Master the language of AI and unleash its full potential with our prompt engineering course. Gain the skills to craft compelling prompts that yield better, more accurate responses. Learn how to create engaging prompts that generate better and more accurate responses. From understanding contextual cues to mitigating biases, we provide you with techniques to help you seamlessly interact with AI systems. Whether you're a techie or a business professional, this course will revolutionize how you interact with AI. Register now and become a compelling AI communicator in the digital age! Artificial Intelligence (AI) is revolutionizing the world at an astonishing pace, surpassing our optimistic expectations. The advent of conversational AIs powered by so-called LLM/GPT models has given us a real taste of the human-like capabilities of AI. This remarkable tool has the potential to inform, educate, and empower us to make optimal decisions. However, the efficacy of our interactions with AI systems hinges upon our ability to communicate effectively. That's why it is crucial to acquire the skill of conversing with AI through prompt engineering, which enhances our outcomes by craftily refining our messages. In this quick Prompt Engineering course, you will gain invaluable insights into the art of talking effectively to AI. Whether proficient in English or prefer your native language, this course equips you with the knowledge and techniques necessary to essentially "program" AI. You'll also become acquainted with watsonx's powerful Prompt Lab tool. By the end of this course, you'll possess the expertise to leverage AI's potential to its fullest, unlocking new realms of productivity and creativity. Start your journey toward mastering prompt engineering today! Course Syllabus Module 1: Introduction to Prompt Engineering Lab 1: What is Prompt Engineering, and why do we care? Lab 2: English as a new programming language Quizzes Module 2: Getting Started with Prompt Engineering Lab 3: Getting to know our GPT-based AI tool Lab 4: The Naive Prompting Approach and the Persona Pattern Lab 5: The Interview Pattern Quizzes Module 3: The Chain-of-Thought Approach Lab 6: The Chain-of-Thought Approach in Prompt Engineering Quizzes Module 4: Advanced Techniques Lab 7: The Tree-of-Thought Approach in Prompt Engineering Lab 8: Controlling Verbosity and the Nova System Lab 9: Getting to Know watsonx Prompt Lab Quizzes Module 5: Final Project Course Summary Final Project (optional)
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