2309 Learning & Development courses delivered Online

Duration 2 Days 12 CPD hours This course is intended for Data Modelers Participants will learn the full scope of the metadata modeling process, from initial project creation, to publishing a dynamic cube, and enabling end users to easily author reports and analyze data. Introduction to IBM Cognos Dynamic Cubes Define and differentiate Dynamic Cubes Dynamic Cubes characteristics Examine Dynamic Cube requirements Examine Dynamic Cube components Examine high level architecture IBM Cognos Dynamic Query Review Dimensional Data Structures Dynamic Cubes caching Create & Design a Dynamic Cube Explore the IBM Cognos Cube Designer Review the cube development process Examine the Automatic Cube Generation Manual development overview Create dimensions Model the cube Best practice for effective modeling Deploy & Configure a Dynamic Cube Deploy a cube Explore the Estimate Hardware Requirements Identify cube management tasks Examine Query Service administration Explore Dynamic Cube properties Schedule cube actions Use the DCAdmin comment line tool Advanced Dynamic Cube Modelling Examine advanced modeling concepts Explore modeling caveats Calculated measures and members Model Relative Time Explore the Current Period property Define period aggregation rules for measures Advanced Features of Cube Designer Examine multilingual support Examine ragged hierarchies and padding members Define Parent-Child Dimensions Refresh Metadata Import Framework Manager packages Filter measures and dimensions Optimize Performance with Aggregates Identify aggregates and aggregate tables In-memory aggregates Use Aggregate Advisor to identify aggregates User defined in-memory aggregates Optimize In-Memory Aggregates automatically Aggregate Advisor recommendations Monitor Dynamic Cube performance Model aggregates (automatically vs manually) Use Slicers to define aggregation partitions Define Security Overview of Dynamic Cube security Identify security filters The Security process - Three steps Examine security scope Identify scope rules Identify roles Capabilities and access permissions Cube security deep dive Model a Virtual Cube Explore virtual cubes Create the virtual cube Explore virtual cube objects Examine virtual measures and calculated members Currency conversion using virtual cubes Security on virtual cubes Introduction to IBM Cognos Analytics Define IBM Cognos Analytics Redefined Business Intelligence Self-service Navigate to content in IBM Cognos Analytics Interact with the user interface Model data with IBM Cognos Analytics IBM Cognos Analytics components Create reports Perform self-service with analysis and Dashboards IBM Cognos Analytics architecture (high level) IBM Cognos Analytics security Package / data source relationship Create Data modules Upload files Additional course details: Nexus Humans B6063 IBM Cognos Cube Designer - Design Dynamic Cubes (v11.0) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the B6063 IBM Cognos Cube Designer - Design Dynamic Cubes (v11.0) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for Incoming attendees are required to have current, hands-on experience in developing basic web applications. Student should have some experience with HTML and CSS and be well versed in JavaScript. Experience with coding for the server side would be helpful. Overview This skills-focused course is approximately 50% hands-on. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Learn server-side JavaScript coding through Node.js Explore the latest JavaScript features, and ECMAScript modules Walk through different stages of developing robust applications using Node.js Install and use Node.js for development Use the Express application framework Work with REST service development using the Restify framework Use data storage engines such as MySQL, SQLITE3, and MongoDB Node.js is a server-side JavaScript platform using an event-driven, non-blocking I/O model allowing users to build fast and scalable data-intensive applications running in real time.This fast-paced hands-on course provides the core skills required to develop web applications with Node.js. You will progress from a rudimentary knowledge of JavaScript and server-side development to being able to create, maintain and test your own Node.js applications. You will explore the importance of transitioning to functions that return Promise objects, and the difference between fs, fs/promises and fs-extra, as well as how to use the HTTP Server and Client objects, and data storage with both SQL and MongoDB databases. Overview of Node.js The capabilities of Node.js Why should you use Node.js? The Node.js event-driven architecture Embracing advances in the JavaScript language Developing microservices or maxiservices with Node.js Setting Up Node.js System requirements Installing Node.js using package managers Installing from the source on POSIX-like systems Installing multiple Node.js instances with nvm Requirements for installing native code modules Choosing Node.js versions to use and the version policy Choosing editors and debuggers for Node.js Running and testing commands Advancing Node.js with ECMAScript 2015, 2016, 2017, and beyond Using Babel to use experimental JavaScript features Exploring Node.js Modules Defining a Node.js module Finding and loading modules using require and import Using npm ? the Node.js package management system The Yarn package management system HTTP Servers and Clients Sending and receiving events with EventEmitter Understanding HTTP server applications HTTP Sniffer ? listening to the HTTP conversation Web application frameworks Getting started with Express Creating an Express application to compute Fibonacci numbers Making HTTPClient requests Calling a REST backend service from an Express application Your First Express Application Exploring Promises and async functions in Express router functions Architecting an Express application in the MVC paradigm Creating the Notes application Theming your Express application Scaling up ? running multiple Notes instances Implementing the Mobile-First Paradigm Understanding the problem ? the Notes app isn't mobile friendly Learning the mobile-first paradigm theory Using Twitter Bootstrap on the Notes application Flexbox and CSS Grids Mobile-first design for the Notes application Using third-party custom Bootstrap themes Data Storage and Retrieval Remembering that data storage requires asynchronous code Logging and capturing uncaught errors Storing notes in a filesystem Storing notes with the LevelDB datastore Storing notes in SQL with SQLite3 Storing notes the ORM way with Sequelize Storing notes in MongoDB Additional course details: Nexus Humans Introduction to Node.js (TT4153) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Introduction to Node.js (TT4153) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 5 Days 30 CPD hours This course is intended for Administrators Developers Implementers Systems Administrators Overview Understand the PeopleSoft system architecture, application development methodology, and tool set so you can build and customize PeopleSoft applications efficiently to meet your organizations business requirements. Quickly and efficiently create functionality in PeopleSoft applications to take advantage of the unique capabilities of these applications. Gain Hands-On Experience Using PeopleSoft Application Designer Enrolling in this course will also give you hands-on experience with the Application Designer, the PeopleSoft integrated development environment (IDE). Learn to create and modify PeopleSoft definitions, including fields, records, pages and components. By the end of this course, you'll be able to use Application Designer to create and deploy PeopleSoft classic applications and fluid applications This PeopleTools I training introduces the PeopleSoft application development methodology. This 5-day course gives you a general overview of PeopleSoft system architecture, as well as the tool set used to develop new applications or customize existing PeopleSoft applications. Navigating PeopleSoft ApplicationsExplaining the PeopleSoft ArchitectureValidating DataUsing Application Designer to Develop ApplicationsDesigning the ApplicationCreating Record DefinitionsBuilding SQL TablesCreating Page DefinitionsRegistering ComponentsTesting ApplicationsEditing the Portal Registry StructureCreating Menu DefinitionsUnderstanding the Fluid User InterfaceCreating Fluid PagesUsing Delivered CSS Additional course details: Nexus Humans Oracle Peoplesoft PeopleTools I training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Oracle Peoplesoft PeopleTools I course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for Leaders, Managers, Individuals who lead meetings This course is designed to help leaders run effective virtual meetings as well as managing their team virtually. We will explore communication styles and understanding their team as well as productivity. This course involves a lot of open discussion as well as teaching leaders how to manage the virtual workplace and run productive meetings. Defining the Virtual Workplace What does it look like? Tools available Communication strategies Understanding communication styles Leading different communication styles Building a Virtual Workplace Strategy Goals & agenda Check-ins Communication strategies Virtual Leadership Strategies Making connections & check ins Managing virtual meetings with team members Defining availability & creating schedules Open Discussion & Action Plan

Duration 3 Days 18 CPD hours This course is intended for Students receive comprehensive Microsoft Dynamics exam preparation, becoming familiarized with the Dynamics CRM customization and configuration tools. Aspirants also learn to leverage the platform tools to create custom objects, automate tasks, modify user interface, and perform other such customizations. Overview Configure the Dynamics CRM settingsConfigure different entities and fieldsImplement entity relationships, custom actions, workflows, and dialogsIdentify scenarios for utilizing multiple forms, and design considerations for chartsSet default share views and public views, and configure and manage dashboardsIdentify role-based business processesIdentify and manage business requirements and teams This course explains everything you need to know about customizing and configuring the Dynamics CRM 365 system in accordance with a company?s specific requirements. Introduction to Customization and Configuring Dynamics CRM Talent and Course Introduction Module Overview CRM Overview What is Dynamics Customization and Configuration? CRM Architecture Customization Methodology Module review Obtaining a Dynamics CRM Trial TEST YOUR KNOWLEDGE MODULE 1' Manage Microsoft Dynamics CRM Online Subscriptions Module Overview Configuring CRM Overview of CRM Security User Administration Mailboxes Teams CRM Security Model Module Overview Purpose of the CCRM Security Model Privileges Access Levels Security Roles Hierarchy Security Hierarchy Types Module review Introduction to Solutions Module Overview Solutions Overview Solution Detail Creating and Working with Solutions Working with Solution Assets Exporting Solutions Importing Solutions Module review Entity and Field Customization Module Overview Types Entities Entity Ownership Entity Properties System vs Custom Entities Custom Entities and Security Roles Overview of Fields Field Properties Module review Additional Field Customization Module Overview Creating Fields to Meet Client Needs Calculated Fields Rollup Fields CRM Option Sets Alternate Keys Field Level Security State and Status Reason Transitions Module Review Configure mobile devices Module Overview Types of Relationships How and where they are created Many to Many Relationships Hierarchical Data Entity Mapping Connection and Connection Roles Module Review Customizing Forms Module Overview Form types Qualities of a good form Building a Form Specialized Form Components Access Teams and Sub Grids Working with Navigation Additional Form Types Multiple Forms Form customizations and Mobile Clients Module Review Business Rules Module Overview Business Rules Business Rule Scope Trigger Rules Condition and Actions Else Conditions and Actions Occur When Conditions Are True Module review Views and Visualizations Module Overview Using Views View Customization System View Types Quick Find Customization Charts Customizing Dashboard Themes Module Review Introduction to Processes Module Overview Processes and Automation Workflow Basics Module review Business Process Flows What are CRM Business Process Flows Enabling Business Process Flows Steps Stages and Categories Conditional Branching Module Review Bringing it all Together Module Overview Review of Customization Topics Covered Senario Packaging in a Solution Module review

Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies

Duration 5 Days 30 CPD hours This course is intended for This class is designed for experienced BizTalk Server Developers who have at least one year of hands-on experience developing BizTalk Server applications. Overview In this 5-day course, you will learn how to apply best practices and design patterns to build smarter BizTalk Server applications. Furthermore, this course provides extensive coverage of BizTalk Server's extensibility, including such topics as: custom functoids, custom pipeline components, and invoking external .NET methods. This course is designed specifically for experienced BizTalk Server developers and focuses on best practices & pattern-based design while pulling back the curtain on some of BizTalk Server's eccentricities. Review of BizTalk Server Fundamentals The BizTalk Server Architecture Inner Workings of the Messaging Engine Messaging Engine Deep Dive Two-way Messaging Without Orchestrations Designing and Testing Schemas Schema Design Enabling Unit Testing for BizTalk Projects Data Translation and Transformation Custom Data Transformation Creating Custom Pipeline Components Working with Message Interchanges Debatching Message Interchanges Advanced Concepts of WCF Adapters Connecting to External Systems Using WCF LOB Adapters in BizTalk Server Publishing and Consuming WCF and RESTful Services Overview of Service Integration Using WCF Implementing WCF Services Preprocessing Messages with IIS Modules Consuming Services Advanced Orchestration Communication Patterns Orchestration Engine Deep Dive Splitting and Aggregating Messages using Orchestrations Orchestration Communication Bridging the Synchronous/Asynchronous Gap Across Multiple Channels Correlating Messages in Orchestration Instances Building Convoy Orchestrations Handling Orchestration Faults and Exceptions Exception Handling in Orchestrations Implementing Transactions and Compensation Creating Transactional Processes Designing Custom Tracking Models for BizTalk Applications Introduction to Business Activity Monitoring Enabling Business Activity Monitoring Extending BAM Beyond BizTalk Building Declarative Logic Using the Business Rules Engine Concepts of Declarative Logic Fundamentals of BizTalk BRE Integrating Policies with BizTalk Advanced Concepts of the Business Rules Engine Advanced Business Rule Concepts Working with Advanced Facts Integrating Across Business Boundaries Using Parties, Roles, and EDI Port Binding Option Review Role-Based Integration What is EDI? Enabling EDI-Based Messaging

Duration 0.5 Days 3 CPD hours This course is intended for This course is intended for: Line of Business (LoB) owners, IT leaders, and executives Overview In this course, you will learn to: Explain the role of information technology (IT) in an organization for business transformation Explain the customer value proposition for using the cloud across industries Define key characteristics of cloud computing Explain the cloud business model Identify key security practices of cloud computing Frame the cloud business value using the Cloud Value Framework In this course, you will learn the fundamental concepts of cloud computing and how a cloud strategy can help companies meet business objectives. It explores the advantages and possibilities of cloud computing. It also introduces addresses concepts such as security and compliance to help facilitate better discussions with line of business (LOB) professionals, information technology (IT) LoB, IT leaders, and executives. Module 1: Course Introduction Course Introduction Module 2: Information Technology for Business Transformation Role of IT in an organization for business transformation Brief history of IT Legacy approach to IT What drives customers to move from traditional infrastructure to the cloud Module 3: Cloud Computing Define cloud computing Key characteristics of cloud technology The cloud business model Key security practices within the cloud Module 4: Business Value of the Cloud The customer value proposition Identify who is using cloud computing Industry trends Customer examples Module 5: The Cloud Value Framework Introduction to the Cloud Value Framework Cost Savings Staff Productivity Operational Resilience Business Agility Module 6: Business Value Activity Business Value Activity Additional course details: Nexus Humans AWS Cloud Essentials for Business Leaders training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the AWS Cloud Essentials for Business Leaders course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Machine Learning Essentials with Python (TTML5506-P) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Solutions architects IT professionals Overview In this course, you will learn to: Apply data lake methodologies in planning and designing a data lake Articulate the components and services required for building an AWS data lake Secure a data lake with appropriate permission Ingest, store, and transform data in a data lake Query, analyze, and visualize data within a data lake In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake architectures. Module 1: Introduction to data lakes Describe the value of data lakes Compare data lakes and data warehouses Describe the components of a data lake Recognize common architectures built on data lakes Module 2: Data ingestion, cataloging, and preparation Describe the relationship between data lake storage and data ingestion Describe AWS Glue crawlers and how they are used to create a data catalog Identify data formatting, partitioning, and compression for efficient storage and query Lab 1: Set up a simple data lake Module 3: Data processing and analytics Recognize how data processing applies to a data lake Use AWS Glue to process data within a data lake Describe how to use Amazon Athena to analyze data in a data lake Module 4: Building a data lake with AWS Lake Formation Describe the features and benefits of AWS Lake Formation Use AWS Lake Formation to create a data lake Understand the AWS Lake Formation security model Lab 2: Build a data lake using AWS Lake Formation Module 5: Additional Lake Formation configurations Automate AWS Lake Formation using blueprints and workflows Apply security and access controls to AWS Lake Formation Match records with AWS Lake Formation FindMatches Visualize data with Amazon QuickSight Lab 3: Automate data lake creation using AWS Lake Formation blueprints Lab 4: Data visualization using Amazon QuickSight Module 6: Architecture and course review Post course knowledge check Architecture review Course review Additional course details: Nexus Humans Building Data Lakes on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Building Data Lakes on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.