IBM Cognitive Class
Solr 101
Learn the basics of Solr (pronounced "solar"), an open source enterprise search platform, written in Java, from the Apache Lucene project. Solr is a standalone full-text search server that uses the Lucene Java search library at its core for full-text indexing and search, and has REST-like HTTP/XML and JSON APIs that make it usable from most popular programming languages. Learn about Solr's major features, including full-text search, hit highlighting, faceted search, real-time indexing, dynamic clustering, database integration, NoSQL features and rich document (e.g., Word, PDF) handling. Learn how Solr is highly scalable and fault tolerant in providing distributed search and index replication. Learn why Solr is the most popular enterprise search engine. COURSE SYLLABUS Module 1 - Search Engines Understand the importance of text search engines Understand the Solr search procedure Identify Solr components Module 2 - Configure and Add Documents to Solr Identifying the important files in a Solr installation Define the schema for documents in the index Understand the various ways to add documents to Solr Module 3 - Analyzers and Queries Use analyzers, tokenizers, and filters Construct queries Module 4 - SolrJ and Customization Create SolrJ applications Understand the customization options available in Solr GENERAL INFORMATION This course is self-paced. It can be taken at any time. It can be audited as many times as you wish. Labs can be performed on the Cloud, or using a 64-bit system. If using a 64-bit system, you can install the required software (Linux-only), or use the supplied VMWare image. More details are provided in the section "Labs setup". RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE Basic knowledge of operating systems (UNIX/Linux). Basic understanding of SQL and Java would be helpful.
GratisBuild a Neural Network with PyTorch
In this course, you will be focusing on how PyTorch creates and Neural Network optimizes models. We will quickly iterate through different aspects of PyTorch Neural Networks, giving you strong foundations and all the prerequisites you need to build deep learning models. Designed for students and professionals interested in machine learning and deep learning, this course offers a comprehensive understanding of the theory and practical applications of building and deploying neural networks. Note, this course is a part of a PyTorch Learning Path, check Prerequisites section. In this course, participants will dive into the fundamentals of neural networks and gain hands-on experience with PyTorch, one of the most popular frameworks for deep learning. Through a series of interactive lectures, coding exercises, and projects, students will develop a solid foundation in building and training neural networks. Throughout the course, participants will have the opportunity to apply their knowledge through hands-on coding exercises. By the end of the course, students will have the skills and confidence to build, train, and deploy neural networks using PyTorch, enabling them to tackle real-world machine learning challenges and contribute to the advancement of AI applications. Syllabus Module 1 - Neural Networks Introduction to Networks Network Shape: Depth vs Width Back Propagation Activation Functions Module 2- Deep Networks Dropout Initialization Batch Normalization Other Optimization Methods Prerequisites Note: this course is a part of the PyTorch Learning Path requires the completion of the following courses: PyTorch: Tensor, Dataset and Data Augmentation Linear Regression with PyTorch Classification with PyTorch Alternatively, the student must have a good understanding of PyTorch Tensors and DataSets, linear regression, and classification. Skills Prior to Taking this Course Basic knowledge of the Python programming language
GratisAI Biomedical Applications Workshop
In three fascinating projects, learn how to create biomedical AI applications and deploy them. First, you'll discover the basics of AI and machine learning using Python and Scikit-Learn, building a model to detect Parkinson's disease from voice patterns. Next, you'll dive into deploying a Parkinson's detection app using Docker and Kubernetes, no prior knowledge is needed. Finally, using PyTorch and computer vision techniques, you'll develop an algorithm that identifies metastatic cancer from digital pathology scans. By the end, you'll have the skills to tackle real-world biomedical problems. This course covers machine learning in the biomedical field through three projects. You will learn how to build and deploy AI models, including detecting Parkinson's Disease and identifying metastatic cancer. By the end, you'll be equipped with practical skills to apply machine learning to real-world problems and contribute to healthcare innovation.Part 1. parkinson detection; Part 2. deploy the docker container of the parkinson app; Part 3. cancer detection with pytorch. Course Syllabus Part 1: Using Machine Learning to Analyze Voice Disorders for Parkinson's Disease Detection Introduction to machine learning and its applications in Biomedicine Understanding voice disorders and Parkinson's disease Implementing different machine learning algorithms such as decision trees and support vector machines Conducting grid search to optimize model parameters Visualizing the models for interpretation and feature identification Building a machine learning model that can accurately predict Parkinson's disease based on voice recordings Part 2: Deploying AI Application on IBM Code Engine Introduction to IBM Code Engine and its features Understanding serverless platforms and their advantages A step-by-step guide to deploying the AI application on IBM Cloud using IBM Code Engine Using Parkinson's detection model as an example Creating a Docker container image with Kubernetes for app deployment Part 3: Cancer Detection with PyTorch Introduction to the convolutional neural network and transfer learning Understanding pre-trained CNNs Dataset preparation for PCAM images Training and testing the model Improving model performance using transfer learning Overall, this course will provide an in-depth understanding of machine learning applications in biomedicine, from detecting Parkinson's disease to identifying metastatic cancer. By the end of the course, students will have the skills to build and deploy AI models and contribute to healthcare innovation.
GratisIntroduction to Cognos Analytics
IBM Cognos Analytics is designed to help you make confident business decisions informed by real metrics and insights and take the guesswork out of decision making. Take this course and learn about topical subjects of IBM Cognos Analytics, see demos or follow step-by-step instructional videos. To keep pace with changing market dynamics and make better strategic decisions, you need ready access to up-to-date, high-quality data and analysis. IBM Cognos Analytics is designed to help you make confident business decisions informed by real metrics and insights and take the guesswork out of decision making. Take this course and learn about topical subjects of IBM Cognos Analytics, see demos or follow step-by-step instructional videos. Course Syllabus Overview Module 1 Module 2 Module 3 Module 4 Module 5 Module 6 Module 7 General Information The course consists of 8 modules, each of them with review questions. There's no time limit for each review question, and you can have unlimited attempts! Pass the course with a minimum passing mark of 50%. If you pass the course the item to claim your completion certificate will be enabled. Recommended Skills Prior to Taking this Course Join the Business Analytics community to connect, learn and share with over 10000 users across the IBM Business Analytics Community: https://community.ibm.com/community/user/businessanalytics/communities/community-home?CommunityKey=6b10df83-0b3c-4f92-8b1f-1fd80d0e7e58%E2%80%8B
GratisKubernetes Operators Intermediate
This course serves as an introduction to Kubernetes operators. Operators are a design pattern for building and deploying an application onto a Kubernetes cluster such that it can be used in the same manner as core Kubernetes resources. It covers the internal architecture of operators and how they fit into the larger Kubernetes ecosystem, as well as how to build basic operators using Operator-sdk. Operator-sdk is a command line tool for scaffolding operators, generating the code skeleton and Kubernetes yaml needed to deploy the operator. This course is of interest to anyone who wants to learn how to deploy and manage applications on Kubernetes using a more Kubernetes-native style, such as Kubernetes application devlopers, architects, or operators. It is also of interest to consumers of Kubernetes applications users interested in learning how some of the applications they use work internalally. It features hands-on labs that have you constructing and deploying real operators on a live Kubernetes cluster. LEARNING OBJECTIVES In this course you will learn about: What a Kubernetes operator is Why you would want to use an operator The basic internal architecture of an operator How to build a basic operator using Golang Helm Ansible COURSE SYLLABUS Module 1 - Intro to Operators and Building your first Golang operator Overview of operators Overview of basic Kubernetes architecture Architecture of an operator Hands-on Lab Build a Golang operator with Operator-sdk Module 2 - Building your first Helm operator Overview of Helm operators Hands-on Lab Build a Helm operator with Operator-sdk Module 3 - Building your first Ansible operator Overview of Ansible operators Hands-on Lab Build an Ansible operator with Operator-sdk REQUIREMENTS Basic familiarity with Kubernetes (i.e. creating/updating/deleting Kubernetes resources such as Pods and Services) The labs in this course cover the creation, deployment, and use of operators on a Kubernetes cluster. It assumes you are already familiar with the basics of Kubernetes, such as using kubectl to create resources on a Kubernetes cluster. The requirements for the hands-on labs are as follows: Requirements for all hands-on labs Operator-sdk v1.5.0+ installed Kubectl v1.17.0+ installed Admin access to a Kubernetes cluster. The next section covers how to provision a small cluster for free on IBM Cloud if you do not have a one already. Docker v3.2.2+ installed Access to a Docker image repository such as Docker Hub or quay.io
GratisPyTorch: Tensor, Dataset and Data Augmentation
Data preparation plays a crucial role in effectively solving machine learning (ML) problems. PyTorch, a powerful deep learning framework, offers a plethora of tools to make data loading easy. The PyTorch: Tensor, Dataset and Data Augmentation course will provide you with a solid understanding of the basics and core principles of PyTorch, specifically focusing on tensor manipulation, dataset management, and data augmentation techniques. "PyTorch: Tensor, Dataset and Data Augmentation" course equips you with the essential skills to handle and transform data efficiently for machine learning tasks. In this course, students will delve into the essential aspects of working with tensors in PyTorch. They will learn how to efficiently manipulate tensors, perform mathematical operations, and leverage tensor-based operations for tasks like data preprocessing and model training. Through a series of lectures and hands-on exercises, you will gain a deep understanding of PyTorch's data loading capabilities, PyTorch Dataset Object and learn how to preprocess and augment data to maximize model performance. Syllabus Overview of Tensors Tensors 1D Two-Dimensional Tensors Derivatives in PyTorch Simple Dataset Dataset and Data Augmentation Recommended Skills Prior to Taking this Course Basic knowledge of Python programming language.
GratisApache Pig 101
Learn about the two major components of Apache Pig: the first is the language itself, which is called PigLatin, and the second is the runtime environment where PigLatin programs are executed. Apache Pig was initially developed at Yahoo! to allow people using Hadoop® to focus more on analyzing large data sets and spend less time having to write mapper and reducer programs. Like actual pigs, who eat almost anything, the Pig programming language is designed to handle any kind of data—hence the name! Get an overview of Pig's data structures supported and how to access data using the LOAD operator. Learn Pig's relational operators. Learn Pig's evaluation functions, as well as math and string functions. COURSE SYLLABUS Module 1 - Pig data types Module 2 - Built-in functions used with the LOAD and STORE operators Module 3 - The difference between storing and dumping a relation Module 4 - The Pig operators Module 5 - Pig evaluation functions GENERAL INFORMATION This course is self-paced. It can be taken at any time. It can be audited as many times as you wish. RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE Basic understanding of Apache Hadoop and Big Data. Basic Linux Operating System knowledge. Basic programming skills.
GratisReactive Architecture: Building Scalable Systems
Building Reactive Systems requires a balance between Consistency and Availability. This course will explain why that balance exists. We will discuss the CAP theorem as well as the Laws of Scalability that dictate what is possible to achieve when we build distributed systems. We will also introduce some techniques we can use to strike the right balance. All of this will be grounded in a discussion which starts first in the real world. What will I get after passing this course? You will receive a completion certificate. Course Syllabus Consistency, Availability, and Scalability Definitions for Consistency, Availability, and Scalability. A discussion of the difference between Performance and Scalability. Consistency in Distributed Systems An indepth discussion of how physics impacts our ability to provide consistency in a distributed system. A definition, and discussion, of Eventual Consistency. A definition, and discussion, of Strong Consistency. Laws of Scalability Contention in Distributed Systems. Amdahl's Law. Coherency in Distributed Systems./li> Gunther's Law of Universal Scalability CAP Theorem Defining Partition Tolerance. Discussing the balance between Consistency and Availability. Sharding Using Sharding for Consistency. Explanation of how Sharding works. Advantages/Disadvantages of Sharding. Conflict-Free Replicated Data Types (CRDTs) Using CRDTs for Availability. Explanation of how CRDTs work. Advantages/Disadvantages of CRDTs. Consistency or Availability How do we decide which one to pick? General Information It is self-paced. It can be taken at any time. It can be taken as many times as you wish. Recommended skills prior to taking this course Experience in the design of software systems. Grading scheme The minimum passing mark for the course is 75%, where the review questions are worth 40%, the case study exercises are worth 30% and the final exam is worth 30% of the course mark. You have 1 attempt to take the final exam with multiple attempts per question. Requirements Reactive Architecture: Introduction to Reactive Systems, Reactive Architecture: Domain Driven Design and Reactive Architecture: Reactive Microservices.
GratisReinforcement Learning and Deep Learning Essentials
Reinforcement Learning and Deep Learning are more advanced techniques and areas of Machine Learning. These techniques, centred around multi-layer neural networks, are important drivers behind the evolution of Artificial Intelligence (AI). In just a couple of hours, this course will provide a quick introduction to both Reinforcement Learning and Deep Learning and will even get you to apply these techniques in a hands-on exercise. 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. Course Syllabus Module 1 Reinforcement Learning Module 2 Introduction to Deep Learning Key Concepts in Neural Networks and Training Module 3 Deep Learning Frameworks in Python Hands-on Lab: Deep Learning in Python Recommended Skills Prior to Taking this Course Basic knowledge of Python programming language.
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