176 SV courses

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

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

ITIL® 4 Specialist: Create, Deliver and Support: Virtual In-House Training The ITIL® 4 Specialist: Create, Deliver, and Support module is part of the Managing Professional stream for ITIL® 4. Candidates need to pass the related certification exam for working towards the Managing Professional (MP) designation. This course is based on the ITIL® 4 Specialist: Create, Deliver, and Support exam specifications from AXELOS. With the help of ITIL® 4 concepts and terminology, exercises, and examples included in the course, candidates acquire the relevant knowledge required to pass the certification exam. What You Will Learn The learning objectives of the course are based on the following learning outcomes of the ITIL® 4 Specialist: Create, Deliver, and Support exam specification: Understand how to plan and build a service value stream to create, deliver, and support services Know how relevant ITIL® practices contribute to the creation, delivery, and support across the SVS and value streams Know how to create, deliver, and support services Organization and Culture Organizational Structures Team Culture Continuous Improvement Collaborative Culture Customer-Oriented Mindset Positive Communication Effective Teams Capabilities, Roles, and Competencies Workforce Planning Employee Satisfaction Management Results-Based Measuring and Reporting Information Technology to Create, Deliver, and Support Service Integration and Data Sharing Reporting and Advanced Analytics Collaboration and Workflow Robotic Process Automation Artificial Intelligence and Machine Learning CI / CD Information Model Value Stream Anatomy of a Value Stream Designing a Value Stream Value Stream Mapping Value Stream to Create, Deliver, and Support Services Value Stream for Creation of a New Service Value Stream for User Support Value Stream Model for Restoration of a Live Service Prioritize and Manage Work Managing Queues and Backlogs Shift-Left Approach Prioritizing Work Commercial and Sourcing Considerations Build or Buy Sourcing Models Service Integration and Management