89 Algorithm courses in Leeds delivered Live Online

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: A technical audience at an intermediate level Overview Using Amazon SageMaker, this course teaches you how to: Prepare a dataset for training. Train and evaluate a machine learning model. Automatically tune a machine learning model. Prepare a machine learning model for production. Think critically about machine learning model results In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment. Day 1 Business problem: Churn prediction Load and display the dataset Assess features and determine which Amazon SageMaker algorithm to use Use Amazon Sagemaker to train, evaluate, and automatically tune the model Deploy the model Assess relative cost of errors Additional course details: Nexus Humans Practical Data Science with Amazon SageMaker 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 Practical Data Science with Amazon SageMaker 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 4 Days 24 CPD hours This course is intended for Systems engineers Network engineers Field engineers Technical support personnel Channel partners and resellers Overview After taking this course, you should be able to: Describe the key concepts of segment routing Implement and verify IGP segment routing Migrate an existing Multiprotocol Label Switching (MPLS) LDP-based network to segment routing Implement and verify TI-LFA segment routing Instantiate segment routing policies Instantiate multidomain segment routing policies Configure and verify BGP prefix segments and SR-based services The Implementing Segment Routing on Cisco IOS XR (SEGRTE201) v2.0 course covers the fundamental concepts of Segment Routing (SR), how to configure and verify segment routing within an Interior Gateway Protocol (IGP), and the interworking of Label Distribution Protocol (LDP) with segment routing. You will learn how to implement Topology-Independent Loop-Free Alternate (TI-LFA) using segment routing, and how to instantiate and verify segment routing traffic engineering policies. You will also learn how to implement segment routing within Border Gateway Protocol (BGP). Introduction to Segment Routing Examining Unified Fabric Routing Exploring Segment Routing Concepts Examining Segment Types Examining the Segment Routing Global Block (SRGB) IGP Segment Routing Implementation and Verification Examining the IGP Control Plane Examining SRGB and IGP Interactions Examining Prefix and Adjacency SIDs Intermediate System to Intermediate System (IS-IS) Multilevel and Open Shortest Path First (OSPF) Multi-Area Configuring and Verifying IS-IS SR Operation Configuring and Verifying OSPF SR Operation Segment Routing and LDP Interworking SR and LDP Interworking Data Plane Mapping Server Function and Configuration Interworking Deployment Models Topology Independent ? Loop Free Alternate Examining Classic LFA Examining TI-LFA Fundamentals Implementing and Verifying TI-LFA for SR Traffic Implementing and Verifying SR TI-LFA for LDP Traffic TI-LFA and SR LDP Interworking Segment Routing Policies ? Traffic Engineering (SR-TE) Exploring SR Policies Introducing the Anycast and Binding SIDs Enabling and Verifying SR Policies Instantiating SR Policies Instantiating SR Policies using BGP Dynamic Multidomain SR Policies Configuring and Verifying a Path Computation Element (PCE) Configuring and Verifying BGP Link-State (LS) Configuring Multidomain SR Policies with a PCE Configuring Multidomain SR Policies with On Demand Next-Hop (ODN) Segment Routing?Based Services Examining the BGP Prefix-SID Operation Configuring and Verifying the BGP Prefix SID Examining Egress Peer Engineering Examining the BGP Prefix-SID Operation SR Flexible Algorithm and Performance Measurement (PM) Delay SR-Enabled VPNs

Duration 5 Days 30 CPD hours This course is intended for This course benefits individuals responsible for implementing, monitoring, and troubleshooting Layer 3 components of a service provider's network. Overview Describe the various OSPF link-state advertisement (LSA) types. Explain the flooding of LSAs in an OSPF network. Describe the shortest-path-first (SPF) algorithm. List key differences between OSPFv2 and OSPFv3. Describe OSPF area types and operations. Configure various OSPF area types. Summarize and restrict routes. Identify some scenarios in a service provider network that can be solved using routing policy or specific configuration options. Use routing policy and specific configuration options to implement solutions for various scenarios. Describe how to troubleshoot OSPF. Explain the concepts and operation of IS-IS. Describe various IS-IS link-state protocol data unit (LSP) types. List IS-IS adjacency rules and troubleshoot common adjacency issues. Configure and monitor IS-IS. Display and interpret the link-state database (LSDB). Perform advanced IS-IS configuration options. Implement IS-IS routing policy. Explain the default operation in multiarea IS-IS. Describe IS-IS address summarization methods. Configure and monitor a multiarea IS-IS network. Describe how to troubleshoot IS-IS. Describe basic BGP operation. List common BGP attributes. Explain the route selection process for BGP. Describe how to alter the route selection process. Configure some advanced options for BGP peers. Describe various BGP attributes in detail and explain the operation of those attributes. Manipulate BGP attributes using routing policy. Explain the causes for route instability. Describe the effect of damping on BGP routing. Explain the default behavior of damping on links. Control damping using routing policy. View damped routes using command-line interface (CLI) commands. Describe the operation of BGP route reflection. Configure a route reflector. Describe the operation of a BGP confederation. Configure confederations. Describe peering relationships in a confederation. Describe how to troubleshoot BGP. Describe how to troubleshoot routing policy. This five-day course is designed to provide students with detailed coverage of OSPF, IS-IS, BGP, and routing policy. Course Outline Course Introduction OSPF OSPFv2 Review Link-State Advertisements Protocol Operations OSPF Authentication OSPF Areas Review of OSPF Areas Stub Area Operation Stub Area Configuration NSSA Operation NSSA Configuration Route Summarization OSPF Case Studies and Solutions Virtual Links OSPF Multiarea Adjacencies External Reachability Troubleshooting OSPF Troubleshooting OSPF IS-IS Overview of IS-IS IS-IS PDUs Neighbors and Adjacencies Configuring and Monitoring IS-IS Advanced IS-IS Operations and Configuration Options IS-IS Operations IS-IS Configuration Options IS-IS Routing Policy Multilevel IS-IS Networks Level 1 and Level 2 Operations Multilevel Configuration Troubleshooting IS-IS Troubleshooting IS-IS BGP Review of BGP BGP Operations BGP Path Selection Options Configuration Options BGP Attributes and Policy?Part 1 BGP Policy Next Hop Origin and MED AS Path BGP Attributes and Policy?Part 2 Local Preference Communities Route Reflection and Confederations Route Reflection Operation Configuration and Routing Knowledge BGP Confederations BGP Route Damping Route Flap and Damping Overview Route Damping Parameters Configuring and Monitoring Route Damping Troubleshooting BGP Troubleshooting BGP Troubleshooting Policy Troubleshooting Policy

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 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. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brandnew version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative dversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions

Duration 4 Days 24 CPD hours This course is intended for This course is intended for: Developers Solutions Architects Data Engineers Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Overview In this course, you will learn to: Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Module 0: Introduction Pre-assessment Module 1: Introduction to Machine Learning and the ML Pipeline Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach Module 2: Introduction to Amazon SageMaker Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks Module 3: Problem Formulation Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects Module 4: Preprocessing Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects Module 5: Model Training Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker Module 6: Model Evaluation How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations Module 7: Feature Engineering and Model Tuning Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations Module 8: Deployment How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up Additional course details: Nexus Humans The Machine Learning Pipeline 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 The Machine Learning Pipeline 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.

Duration 4 Days 24 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 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. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm ? YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions

Duration 3 Days 18 CPD hours This course is intended for Developers Administrators Overview Understand why Blockchain is needed and where Explore the major components of Blockchain Learn about Hyperledger Fabric v1.1 and the structure of the Hyperledger Architecture Lean the features of the Fabric model including chaincode, SDKs, Ledger, Security and Membership Services Perform comprehensive labs on writing chaincode Explore the architecture of Hyperledger Fabric v1.1 Understand and perform in depth labs on Bootstrapping the Network Gain a detailed understanding of the benefits, components and architecture of Hyperledger Composer Learn Hyperledger Explorer and Hyperledger Composer Playground Perform comprehensive labs to integrate/develop an application with Hyperledger Fabric running a smart contract Build applications on Hyperledger Fabric v1.1 This instructor-led Hyperledger training course is designed for developers and administrators who want to take a comprehensive deep dive on Hyperledger Fabric and Hyperledger Composer. This Hyperledger training course has several comprehensive labs, giving you real world experience.In 3 days, you will learn the need for blockchain applications, where blockchain is used, and about Hyperledger Fabric, the open source framework for developing blockchain applications and solutions with a modular architecture. Introduction to Blockchain Introduction to Blockchain What is Blockchain Types of network Public network Permissioned network Private network Need for Blockchain Components of Blockchain Consensus Provenance Immutability Finality Where can Blockchain be used Example on Blockchain How Blockchain Works How Blockchain Works Structure of Blockchain Block Hash Blockchain Distributed Lifecycle of Blockchain Smart Contract Consensus Algorithm Proof of Work Proof of Stake Practical Byzantine Fault Tolerance Actors of Blockchain Blockchain developer Blockchain operator Blockchain regulator Blockchain user Membership service provider Building A Small Blockchain Application Introduction to Hyperledger Fabric v1.1 Introduction to Hyperledger What is Hyperledger Why Hyperledger Where can Hyperledger be used Hyperledger Architecture Membership Blockchain Transaction Chaincode Hyperledger Fabric Features of Hyperledger Fabric Installation of prerequisite Getting Started With Fabric Model The Fabric Model Features of Fabric Model Chaincode SDKs Ledger Privacy through channels Security and Membership services Assets Consensus Components of Fabric Model Peer Orderer Certificate Authority Building your network Chaincode Chaincode Chaincode API How to write a Chaincode Lab Work Architecture of Hyperledger Fabric v1.1 Architecture of Hyperledger Fabric Transaction Ledger Nodes Peer Endorser Ordering Nodes Channels Certificate Authority Transaction Flow Lab Work Bootstrapping Bootstrapping the Network Introduction Lab Work Task 1 - Generate the crypto material for the various participants. Task 2 - Generate the genesis block for the Orderer node and start ordering service (solo node). Task 3 - Generated the configuration transaction block to create a new channel. Task 4 - Sign the configuration block and create the new channel. Task 5 - Make peers of all the organizations join the channel that we created in Task 4 Introdcution to Hyperledger Explorer Introduction To Hyperledger Explorer Block Details Peer List Chaincode List Transaction Details Installation of Hyperledger Explorer Starting the Explorer App Introduction to Hyperledger Composer Introduction Components of Hyperledger Composer Benefits of Hyperledger Composer Key Concepts Hyperledger Composer Solution Installation Hyperledger Composer Playground Hyperledger Composer Playground Introduction Playground Overview Lab Work Additional course details: Nexus Humans Hyperledger Training - Developing on Hyperledger Fabric 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 Hyperledger Training - Developing on Hyperledger Fabric 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 This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

Duration 5 Days 30 CPD hours This course is intended for This course benefits individuals responsible for configuring and monitoring devices running the Junos OS. Overview After successfully completing this course, you should be able to: Describe the various OSPF link-state advertisement (LSA) types. Explain the flooding of LSAs in an OSPF network. Describe the shortest-path-first (SPF) algorithm. Describe OSPF link metrics. Describe the various OSPF authentication methods. Explain the differences between OSPFv2 and OSPFv3. Describe OSPF area types and operations. Configure various OSPF area types. Summarize and restrict routes. Configure OSPF multi-area adjacencies. Configure OSPF virtual links. Explain OSPF external reachability. List useful commands that are used to troubleshoot and verify OSPF. Isolate different OSPF issues. Describe BGP operations. Configure various BGP options. Explain the route selection process for BGP. Describe how to alter the route selection process. Explain the use of routing policies in BGP. Explain how BGP routes are processed. Describe the various BGP attributes and their use. Manipulate common BGP attributes. Review common BGP troubleshooting procedures. List common BGP troubleshooting commands. Identify issues with BGP peering. Explain reasons to use BGP in the Enterprise. Explain how ISP policies can influence external connectivity. Describe three common routing policies for external connectivity in the enterprise. Identify common commands for troubleshooting routing policy. Describe basic multicast terminology. Describe the multicast address space. Describe how RPF is used in a multicast network. Describe the basic functionality of IGMP. Describe the multicast service models and modes. Describe PIM-SM operation and configuration when using the ASM model. Describe PIM-SM operation and configuration when using the SSM model. Verify and troubleshoot multicast. Identify environments that may require a modified CoS implementation. Describe the various CoS components and their respective functions. Explain the CoS processing along with CoS defaults on SRX Series devices. Describe situations in which some CoS features are used in the enterprise. Describe the use of the Real-Time Performance Monitoring tool. Verify and troubleshoot CoS. Describe a traditional Campus network design. Understand the need for a new architectural design. Describe the five key concepts of the Evolved Core. Describe the benefits of a Layer 3-based Campus Networks. Describe Layer 2 tunneling. Explain VXLAN functionality. Describe VXLAN gateways. Describe EVPN features. Describe EVPN operations. This five-day course is designed to provide students with the tools required for implementing, monitoring, and troubleshooting Layer 3 components in an enterprise network. Chapter 1: Course Introduction Course Introduction Chapter 2: OSPF OSPFv2 Review Link-State Advertisements Protocol Operations OSPF Authentication OSPFv3 Lab 1: Configuring and Monitoring OSPF Chapter 3: OSPF Areas Review of OSPF Areas Stub Area Operation Stub Area Configuration NSSA Operation NSSA Configuration Route Summarization Lab 2: Configuring and Monitoring OSPF Areas and Route Summarization Chapter 4: Advanced OSPF Options OSPF Multi-Area Adjacencies Virtual Links External Reachability Lab 3: Configuring and Monitoring Routing Policy and Advanced OSPF Options Chapter 5: Troubleshooting Troubleshooting LSDB Consistency Issues Case Study: Adjacency Issues Lab: Troubleshooting OSPF Chapter 6: BGP Review of BGP BGP Configuration Options BGP Operations BGP Path Selection and Options Lab: Implementing BGP Chapter 7: BGP Attributes and Policy Policy and BGP BGP Attributes Details and Manipulation of Common BGP Path Attributes Lab: BGP Attributes Chapter 8: Troubleshooting BGP BGP Troubleshooting BGP Case Study Lab: Troubleshooting BGP Chapter 9: Enterprise Routing Policies Enterprise BGP Core Network Design Enterprise External Network Deployment Lab: Implementing Enterprise Routing Policies Chapter 10: Troubleshooting Policies Routing Policy Structure Using RegEx Routing Policy Troubleshooting Case Study Lab: Troubleshooting Routing Policies Chapter 11: Introduction to Multicast Overview of Multicast Multicast Addressing RPF IGMP Lab: PIM-SM Chapter 12: Multicast Routing Protocols and SSM Overview of Multicast Routing Protocols PIM-SM Using the ASM Model PIM-SM Using the SSM Model Lab: Implementing PIM-SM Lab: Implementing SSM Chapter 13: Troubleshooting Multicast Multicast Troubleshooting Multicast Case Study Lab: Troubleshooting Multicast Chapter 14: Class of Service CoS Components Review and Case Study CoS Processing and CoS Defaults on the SRX Series Device Policing Virtual Channels Monitoring with Resource Performance Monitoring Lab 9: Implementing CoS Features in the Enterprise Chapter 15: Troubleshooting Class of Service CoS Troubleshooting CoS Case Study Lab: Troubleshooting Class of Service Chapter 16: Enterprise Architectures Traditional Enterprise Networks A New Architecture Key Concepts of the Evolved Core IP Fabric Campus Design Chapter 17: VXLAN Layer 2 Connectivity over a Layer 3 Network VXLAN Overview VXLAN Gateways Chapter 18: EVPN-VXLAN Overview of EVPN EVPN Operations EVPN and VXLAN Chapter 19: Configuring EVPN-VXLAN Configuring EVPN-VXLAN Spine Only network Add IP Fabric leaf nodes to a Spine Only design Configuring a new IP Fabric EVPN-VXLAN network Chapter 20: Migrating to an IP Fabric EVPN Routes Useful EVPN Commands Appendix A: BGP Route Reflection Route Reflection Operation Configuration and Routing Knowledge Lab: BGP Route Reflection (Optional) Appendix B: Troubleshooting IS-IS IS-IS Troubleshooting Lab: Troubleshooting IS-IS and Mixed Environments Additional course details: Nexus Humans AJER - Advanced Junos Enterprise Routing 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 AJER - Advanced Junos Enterprise Routing 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.