162 SV courses delivered Online

Overview of Data Science & Machine Learning with Python Join our Data Science & Machine Learning with Python course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Data Science & Machine Learning with Python course has everything you need to get a great start in this sector. Improving and moving forward is key to getting ahead personally. The Data Science & Machine Learning with Python course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now! This Data Science & Machine Learning with Python Course will help you to learn: Learn strategies to boost your workplace efficiency. Hone your skills to help you advance your career. Acquire a comprehensive understanding of various topics and tips. Learn in-demand skills that are in high demand among UK employers This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Data Science & Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever. Details Perks of Learning with IOMH One-To-One Support from a Dedicated Tutor Throughout Your Course. Study Online - Whenever and Wherever You Want. Instant Digital/ PDF Certificate. 100% Money Back Guarantee. 12 Months Access. Process of Evaluation After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the Data Science & Machine Learning with Python course, you can order your CPD Accredited Digital / PDF Certificate for £5.99.  Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. Shipping Charges: Inside the UK: £3.99 International: £10.99 Who Is This Course for? This Data Science & Machine Learning with Python is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.  On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements You don't need any educational qualification or experience to enrol in the Data Science & Machine Learning with Python course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course. Career Path The certification and skills you get from this Data Science & Machine Learning with Python Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:04:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using NumPy 00:04:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:06:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Data Science & Machine Learning with Python 00:00:00

Duration 5 Days 30 CPD hours This course is intended for This course is designed for professionals in the following job roles: Network security engineer CCNP Security candidate Channel Partner Overview After taking this course, you should be able to: Introduce site-to-site VPN options available on Cisco router and firewalls Introduce remote access VPN options available on Cisco router and firewalls Review site-to-site and remote access VPN design options Review troubleshooting processes for various VPN options available on Cisco router and firewalls The Implementing Secure Solutions with Virtual Private Networks (SVPN) v1.0 course teaches you how to implement, configure, monitor, and support enterprise Virtual Private Network (VPN) solutions. Through a combination of lessons and hands-on experiences you will acquire the knowledge and skills to deploy and troubleshoot traditional Internet Protocol Security (IPsec), Dynamic Multipoint Virtual Private Network (DMVPN), FlexVPN, and remote access VPN to create secure and encrypted data, remote accessibility, and increased privacy. Course Outline Introducing VPN Technology Fundamentals Implementing Site-to-Site VPN Solutions Implementing Cisco Internetwork Operating System (Cisco IOS©) Site-to-Site FlexVPN Solutions Implement Cisco IOS Group Encrypted Transport (GET) VPN Solutions Implementing Cisco AnyConnect VPNs Implementing Clientless VPNs Lab Outline Explore IPsec Technologies Implement and Verify Cisco IOS Point-to-Point VPN Implement and Verify Cisco Adaptive Security Appliance (ASA) Point-to-Point VPN Implement and Verify Cisco IOS Virtual Tunnel Interface (VTI) VPN Implement and Verify Dynamic Multipoint VPN (DMVPN) Troubleshoot DMVPN Implement and Verify FlexVPN with Smart Defaults Implement and Verify Point-to-Point FlexVPN Implement and Verify Hub and Spoke FlexVPN Implement and Verify Spoke-to-Spoke FlexVPN Troubleshoot Cisco IOS FlexVPN Implement and Verify AnyConnect Transport Layer Security (TLS) VPN on ASA Implement and Verify Advanced Authentication, Authorization, and Accounting (AAA) on Cisco AnyConnect VPN Implement and Verify Clientless VPN on ASA

Learn to identify and communicate your boundaries with this practical framework for leaders and managers.

This Kaizen and Time Wastes Course is a great way to introduce people to the concept of Kaizen in the workplace. It's also a great way to build upon the knowledge of people already familiar with the subject.

Duration 5 Days 30 CPD hours This course is intended for Network Engineers Channel Partners System Engineers Overview By the end of this course, you will be able to: Describe how ONTAP 9 fits into NetApp?s Cloud and Data Fabric strategy Identify supported ONTAP platforms Define ONTAP cluster components Create a cluster Manage ONTAP administrators Configure and manage storage resources Configure and manage networking resources Describe a Storage Virtual Machine?s (SVM?s) role in NetApp?s storage architecture Create and configure an SVM Create and manage FlexVols Implement storage efficiency features Create protocol servers within an SVM Upgrade and revert ONTAP patches and releases Describe the levels on which ONTAP protects data Describe the ONTAP 9 data protection features Understand the various data mirroring relationships available with ONTAP 9 Configure and operate SnapMirror and SnapVault data replication Demonstrate Storage Virtual Machine data protection Explain the components and configuration involved with SyncMirror and MetroCluster Describe NDMP protocol operation, configuration and management Pre/Post Assessment The ONTAP 9.0 Cluster Administration and Data Protection combo course uses lecture and hands-on exercises to teach basic administration and configuration of a cluster as well as the core backup and restore technologies found in ONTAP 9. The hands-on labs allow you to practice working with ONTAP features and manage your storage and network resources using the cluster shell and OnCommand System Manager. You will learn how to implement and manage SnapMirror, SnapVault, and SnapLock technology which are used to replicate and restore mission-critical data in the enterprise. The course also surveys real-world scenarios and use cases to teach you when to use each of the NetApp protection solutions. Backup and restore operations are taught using the command line and OnCommand System Manager.Includes: ONTAP commands for software versions 8.3.x to 9.0 The ONTAP 9.0 Cluster Administration and Data Protection combo course uses lecture and hands-on exercises to teach basic administration and configuration of a cluster as well as the core backup and restore technologies found in ONTAP 9. The hands-on labs allow you to practice working with ONTAP features and manage your storage and network resources using the cluster shell and OnCommand System Manager. You will learn how to implement and manage SnapMirror, SnapVault, and SnapLock technology which are used to replicate and restore mission-critical data in the enterprise. The course also surveys real-world scenarios and use cases to teach you when to use each of the NetApp protection solutions. Backup and restore operations are taught using the command line and OnCommand System Manager. Includes: ONTAP commands for software versions 8.3.x to 9.0

Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

Duration 69 Days 414 CPD hours Cisco Learning Library: Security offers a subscription to all Cisco online cybersecurity and cyber operations training, including extensive sk This comprehensive technical training library offers full-length, interactive certification courses, product and technology training with labs, and thousands of reference materials. Security Library Certification Courses CCNP Security Implementing and Operating Cisco Security Core Technologies (SCOR) v1.0 Securing Networks with Cisco Firepower Next Generation Firewall (SSNGFW) v1.0 Securing Networks with Cisco Firepower Next-Generation IPS (SSFIPS) v4.0 Implementing and Configuring Cisco Identity Services Engine (SISE) v3.0 Securing Email with Cisco Email Security Appliance (SESA) v3.0 Securing the Web with Cisco Web Security Appliance (SWSA) v3.0 Implementing Secure Solutions with Virtual Private Networks (SVPN) v1.0 Implementing Automation for Cisco Security Solutions (SAUI) v1.0 CCIE Security Implementing and Operating Cisco Security Core Technologies (SCOR) v1.0 Product and Technology Training Implementing and Operating Cisco Security Core Technologies (SCOR) v1.0 Implementing Automation for Cisco Security Solutions (SAUI) v1.0 Understanding Cisco Cybersecurity Fundamentals (SECFND) v1.0 Implementing Cisco Cybersecurity Operations (SECOPS) v1.0 Implementing Secure Solutions with Virtual Private Networks (SVPN) v1.0 Implementing an Integrated Threat Defense Solution (SECUR201) v1.0 Integrated Threat Defense Investigation and Mitigation (SECUR202) v1.0 Securing Cisco Networks with Snort Rule Writing Best Practices (SSFRules) v2.0 Securing Cisco Networks with Open Source Snort (SSFSNORT) v3.0 Securing Networks with Cisco Firepower Next Generation Firewall (SSNGFW) v1.0 Securing Email with Cisco Email Security Appliance (SESA) v3.0 Securing the Web with Cisco Web Security Appliance (SWSA) v3.0 Securing Networks with Cisco Firepower Next-Generation IPS (SSFIPS) v4.0 Introduction to 802.1X Operations for Cisco Security Professionals (802.1X) v2.0 Securing Industrial IoT Networks with Cisco Technologies (ISECIN) v1.0 Implementing and Configuring Cisco Identity Services Engine (SISE) v3.0 Protecting Against Malware Threats with Cisco AMP for Endpoints (SSFAMP) v5.0 Introducing Cisco Cloud Consumer Security (SECICC) v1.0 Securing Cloud Deployments with Cisco Technologies (SECCLD) v1.0 Configuring Cisco ISE Essentials for SD-Access (ISESDA) v1.0 Securing Branch Internet and Cloud Access with Cisco SD-WAN (A-SDW-BRSEC)

Duration 5 Days 30 CPD hours This course is intended for Typical candidates for this course are IT Professionals who will deploy and manage networks based on HPE's ArubaOS-CX switches. Overview After you successfully complete this course, expect to be able to: Use NetEdit to manage switch configurations Use the Network Analytics Engine (NAE) to implement scripting solutions to provide for proactive network management and monitoring Compare and contrast VSX, VSF, and backplane stacking Explain how VSX handles a split-brain scenario Implement and manage a VSX fabric Define ACLs and identify the criteria by which ACLs select traffic Configure ACLs on AOS-CX switches to select given traffic Apply static ACLs to interfaces to meet the needs of a particular scenario Examine an ACL configuration and determine the action taken on specific packets Deploy AOS-Switches in single-area and multi-area OSPF systems Use area definitions and summaries to create efficient and scalable multiple area designs Advertise routes to external networks in a variety of OSPF environments Promote fast, effective convergence during a variety of failover situations Use virtual links as required to establish non-direct connections to the backbone Implement OSFP authentication Establish and monitor BGP sessions between your routers and ISP routers Advertise an IP block to multiple ISP routers Configure a BGP router to advertise a default route in OSPF Use Internet Group Management Protocol (IGMP) to optimize forwarding of multicast traffic within VLANs Describe the differences between IGMP and IGMP snooping Distinguish between PIM-DM and PIM-SM Implement PIM-DM and PIM-SM to route multicast traffic Implement Virtual Routing Forwarding (VRF) policies to contain and segregate routing information Create route maps to control routing policies Understand the use of user roles to control user access on AOS-CX switches Implement local user roles on AOS-CX switches and downloadable user roles using a ClearPass solution Implement 802.1X on AOS-CX switch ports Integrate AOS-CX switches with an Aruba ClearPass solution, which might apply dynamic role settings Implement RADIUS-based MAC Authentication (MAC-Auth) on AOS-CX switch ports Configure captive portal authentication on AOS-CX switches to integrate them with an Aruba ClearPass solution Combine multiple forms of authentication on a switch port that supports one or more simultaneous users Configure dynamic segmentation on AOS-CX switches This course teaches you the advanced skills necessary to implement and operate enterprise level Aruba campus switching solutions. You will build on the skills you learned at the Associate level to configure and manage modern, open standards-based networking solutions using Aruba's OS-CX routing and switching technologies. In this course, participants learn about ArubaOS-CX switch technologies including: securing port access with Aruba's dynamic segmentation, redundancy technologies such as Multiple Spanning Tree Protocol (MSTP), link aggregation techniques including Link Aggregation Protocol (LACP) and switch virtualization with Aruba?s Virtual Switching Extension (VSX) and Aruba's Virtual Switching Framework (VSF). This course is approximately 50% lecture and 50% hands-on lab exercises. Introduction to Aruba Switching Switches overview Architectures NetEdit Overview Centralized configuration Switch groups/templates AOS-CX mobile App Network Analytics Engine (NAE) Overview Configuration Core NAE feature lab sflow, local mirror, remote mirror VSX VSF vs. VSX: access and Agg/core design Stacking review VSF and uni/multi packet forwarding Stack fragments / split brain VSX Overview: roles, control, data, management planes VSX components (ISL, Keepalive, VSX LAG, Active Gateway, Active-Forwarding, Link Delay) Split Brain scenario Upstream Connectively Options (ROP single VRF, SVIs with multiple VRF, VSX Lag SVIs with multiple VRFs) Upstream/Downstream unicast traffic flow (South-North and North-South) VSX Configuration: VSX and Active Gateway VSX firmware updates ACLs Overview: types, components MAC ACL, Standard ACL, Extended ACL, Classifier-based Policies Configuration: wildcard bits, logging, pacl, vacl, racl Advanced OSPF Review basic OSPF Multi area: setup and aggregation Area-Types Stub, Totally Stub, NSSA, Totally NSSA External routes OSPF tuning: costs, bfd, gr, auth, vrrp, virt link BGP Overview: i/e bgp, as numbers Best path selection Configuration: route announcement Route filtering to prevent transit as IGMP Overview Querier Snooping Unknown multicasts Multicast Routing: PIM Overview PIM DM 802.1X Authentication Overview: roles, requirements, coa, accounting Dynamic port configuration: avp, acl, qos, VLAN Port-based vs. user-based: examples Radius service tracking, critical VLAN MAC Authentication Overview: Use cases Radius-based MAC Auth Dynamic Segmentation Leverage dynamic segmentation features Configure tunneled-node on AOS-CX switches Describe when and how to configure PAPI enhanced security, high availability, and fallback switching for tunneled-node Quality of Service Overview VoQ (Virtual Output Queue) QOS: queueing, QOS marks, dot1p, dscp Trust levels QOS configuration: port, VLAN, policies Interaction with user roles Queue configuration Rate limiters LLDP-MED Additional Routing Technologies VRF - Management VRF PBR MDNS PIM SM Capitve Portal Authentication Overview of guest solutions Built-in web auth ClearPass redirect with CPPM

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 2 Days 12 CPD hours This course is intended for NetApp Customers, IT Generalists, Academic Alliance Students Overview Explain ONTAP operation system, Use the CLI and OnCommand System Manager to identify storage components, configure storage systems and storage virtual machines for NAS and SAN client access, create FlexVol volumes, qtrees, and LUNs, manage snapshot copies Introduces introductory concepts covered through instructor led discussions and hands-on labs are how to create aggregates, virtual interfaces, snapshots, volumes, qtrees, and storage virtual machines. Getting Started with Data ONTAP List basic storage concepts such as aggregates, RAID groups, volumes, qtrees, and LUNs Describe Data ONTAP features such as Snapshot copies, unified storage, and storage efficiency Describe the similarities and differences between the 7-Mode and clustered Data ONTAP operating systems Use the CLI and GUI for administrative purposes Hardware Basics Describe the NetApp storage system hardware platforms and the types of disks that they support Describe the hardware components of NetApp storage controllers Use OnCommand System Manager or the CLI to identify hardware components in Data ONTAP operating in 7-Mode and the clustered Data ONTAP operating system Creating & Managing Aggregates Describe aggregates and RAID groups Create aggregates in Data ONTAP operating in 7-Mode Create aggregates in the clustered Data ONTAP operating system Manage aggregates Managing NAS Client Access Configure NAS client access in Data ONTAP operating in 7-Mode Configure data storage virtual machines (SVMs*) for NAS client access in clustered Data ONTAP Create FlexVol volumes and qtrees Managing SAN Client Connections Describe SAN protocol implementation in Data ONTAP operating in 7-Mode and the clustered Data ONTAP operating system Use OnCommand System Manager to create iSCSI-attached LUNs Use NetApp SnapDrive for Windows to create and format iSCSI-attached LUNs Access and manage a LUN from a Windows host Managing Volumes Explain the relationship between space guarantees, volumes, and aggregates Define thin provisioning and explain how it is used Define deduplication and describe the benefits that it provides Use OnCommand System Manager to set quotas Managing Snapshot Copies Define the function of Snapshot copies Create and delete a Snapshot copy Create Snapshot policies in the clustered Data ONTAP operating system Restore a volume from a Snapshot copy Create FlexClone volume clones that are backed by Snapshot copies Steps to Certification Recall the steps to NetApp Certification