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 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
Duration 5 Days 30 CPD hours This course is intended for System administrators System engineers Overview By the end of the course, you should be able to meet the following objectives: Install and configure ESXi hosts Deploy and configure vCenter Use the vSphere Client to create the vCenter inventory and assign roles to vCenter users Configure vCenter High Availability Create and configure virtual networks using vSphere standard switches and distributed switches Create and configure datastores using storage technologies supported by vSphere Use the vSphere Client to create virtual machines, templates, clones, and snapshots Configure and manage a VMware Tools Repository Create content libraries for managing templates and deploying virtual machines Manage virtual machine resource use Migrate virtual machines with vSphere vMotion and vSphere Storage vMotion Create and configure a vSphere cluster that is enabled with vSphere High Availability and vSphere Distributed Resource Scheduler Manage the life cycle of vSphere to keep vCenter, ESXi hosts, and virtual machines up to date Configure and manage vSphere networking and storage for a large and sophisticated enterprise Use host profiles to manage VMware ESXi host compliance Monitor the vCenter, ESXi, and VMs performance in the vSphere client This five-day, extended hour course takes you from introductory to advanced VMware vSphere© 8 management skills. Building on the installation and configuration content from our best-selling course, you will also develop advanced skills needed to manage and maintain a highly available and scalable virtual infrastructure. Through a mix of lecture and hands-on labs, you will install, configure, and manage vSphere 7. You will explore the features that build a foundation for a truly scalable infrastructure and discuss when and where these features have the greatest effect. This course prepares you to administer a vSphere infrastructure for an organization of any size using vSphere 8, which includes VMware ESXi? 8 and VMware vCenter Server© 8. Course Introduction Introductions and course logistics Course objectives vSphere and Virtualization Overview Explain basic virtualization concepts Describe how vSphere fits in the software-defined data center and the cloud infrastructure Recognize the user interfaces for accessing vSphere Explain how vSphere interacts with CPUs, memory, networks, storage, and GPUs Install an ESXi host vCenter Management Recognize ESXi hosts communication with vCenter Deploy vCenter Server Appliance Configure vCenter settings Use the vSphere Client to add and manage license keys Create and organize vCenter inventory objects Recognize the rules for applying vCenter permissions View vSphere tasks and events Create a vCenter backup schedule Recognize the importance of vCenter High Availability Explain how vCenter High Availability works Deploying Virtual Machines Create and provision VMs Explain the importance of VMware Tools Identify the files that make up a VM Recognize the components of a VM Navigate the vSphere Client and examine VM settings and options Modify VMs by dynamically increasing resources Create VM templates and deploy VMs from them Clone VMs Create customization specifications for guest operating systems Create local, published, and subscribed content libraries Deploy VMs from content libraries Manage multiple versions of VM templates in content libraries Configure and Manage vSphere Networking Configure and view standard switch configurations Configure and view distributed switch configurations Recognize the difference between standard switches and distributed switches Explain how to set networking policies on standard and distributed switches Configure and Manage vSphere Storage Recognize vSphere storage technologies Identify types of vSphere datastores Describe Fibre Channel components and addressing Describe iSCSI components and addressing Configure iSCSI storage on ESXi Create and manage VMFS datastores Configure and manage NFS datastores Discuss vSphere support for NVMe and iSER technologies Managing Virtual Machines Recognize the types of VM migrations that you can perform within a vCenter instance and across vCenter instances Migrate VMs using vSphere vMotion Describe the role of Enhanced vMotion Compatibility in migrations Migrate VMs using vSphere Storage vMotion Take a snapshot of a VM Manage, consolidate, and delete snapshots Describe CPU and memory concepts in relation to a virtualized environment Describe how VMs compete for resources Define CPU and memory shares, reservations, and limits Recognize the role of a VMware Tools Repository Configure a VMware Tools Repository Recognize the backup and restore solution for VMs vSphere Monitoring Monitor the key factors that can affect a virtual machine's performance Describe the factors that influence vCenter performance Use vCenter tools to monitor resource use Create custom alarms in vCenter Describe the benefits and capabilities of VMware Skyline Recognize uses for Skyline Advisor Pro Deploying and Configuring vSphere Cluster Use Cluster Quickstart to enable vSphere cluster services and configure the cluster View information about a vSphere cluster Explain how vSphere DRS determines VM placement on hosts in the cluster Recognize use cases for vSphere DRS settings Monitor a vSphere DRS cluster Describe how vSphere HA responds to different types of failures Identify options for configuring network redundancy in a vSphere HA cluster Recognize the use cases for various vSphere HA settings Configure a cluster enabled for vSphere DRS and vSphere HA Recognize when to use vSphere Fault Tolerance Describe the function of the vCLS Recognize operations that might disrupt the healthy functioning of vCLS VMs ESXi Operations Use host profiles to manage ESXi configuration compliance Recognize the benefits of using configuration profiles Managing the vSphere Lifecycle Generate vCenter interoperability reports Recognize features of vSphere Lifecycle Manager Describe ESXi images and image depots Enable vSphere Lifecycle Manager in a vSphere cluster Validate ESXi host compliance against a cluster image and remediate ESXi hosts using vSphere Lifecycle Manager Describe vSphere Lifecycle Manager automatic recommendations Use vSphere Lifecycle Manager to upgrade VMware Tools and VM hardware Network Operations Configure and manage vSphere distributed switches Describe how VMware vSphere Network I/O Control enhances performance Define vSphere Distributed Services Engine Describe the use cases and benefits of vSphere Distributed Services Engine Storage Operations Describe the architecture and requirements of vSAN configuration Describe storage policy-based management Recognize components in the vSphere Virtual Volumes architecture Configure Storage I/O Control
Duration 5 Days 30 CPD hours This course is intended for System architects and system administrators Overview By the end of the course, you should be able to meet the following objectives: Introduce troubleshooting principles and procedures Use command-line interfaces, log files, and the vSphere Client to diagnose and resolve problems in the vSphere environment Explain the purpose of common vSphere log files Identify networking issues based on reported symptoms Validate and troubleshoot the reported networking issue Identify the root cause of networking issue Implement the appropriate resolution to recover from networking problems Analyze storage failure scenarios using a logical troubleshooting methodology identify the root cause of storage failure Apply the appropriate resolution to resolve storage failure problems Troubleshoot vSphere cluster failure scenarios Analyze possible vSphere cluster failure causes Diagnose common VMware vSphere High Availability problems and provide solutions Identify and validate VMware ESXiTM host and VMware vCenter problems Analyze failure scenarios of ESXi host and vCenter problems Select the correct resolution for the failure of ESXi host and vCenter problems Troubleshoot virtual machine problems, including migration problems, snapshot problems, and connection problems Troubleshoot performance problems with vSphere components This five-day training course provides you with the knowledge, skills, and abilities to achieve competence in troubleshooting the VMware vSphere© 8 environment. This course increases your skill level and competence in using the command-line interface, VMware vSphere© Client?, log files, and other tools to analyze and solve problems. Course Introduction Introductions and course logistics Course objectives Introduction to Troubleshooting Define the scope of troubleshooting Use a structured approach to solve configuration and operational problems Apply troubleshooting methodology to logically diagnose faults and improve troubleshooting efficiency Troubleshooting Tools Discuss the various methods to run commands Discuss the various ways to access ESXi Shell Use commands to view, configure, and manage your vSphere components Use the vSphere CLI Use ESXCLI commands from the vSphere CLI Use Data Center CLI commands Identify the best tool for command-line interface troubleshooting Identify important log files for troubleshooting vCenter Server and ESXi Describe the benefits and capabilities of VMware SkylineTM Explain how VMware Skyline works Describe VMware SkylineTM Health Describe VMware Skyline AdvisorTM Troubleshooting Virtual Networking Analyze and troubleshoot standard switch problems Analyze and troubleshoot virtual machine connectivity problems Analyze and troubleshoot management network problems Analyze and troubleshoot distributed switch problems Troubleshooting Storage Discuss the vSphere storage architecture Identify the possible causes of problems in the various types of datastores Analyze the common storage connectivity and configuration problems Discuss the possible storage problems causes Solve the storage connectivity problems, correct misconfigurations, and restore LUN visibility Review vSphere storage architecture and functionality necessary to troubleshoot storage problems Use ESXi and Linux commands to troubleshoot storage problems Analyze log file entries to identify the root cause of storage problems Investigate ESXi storage issues Troubleshoot VM snapshots Troubleshoot storage performance problems Review multipathing Identify the common causes of missing paths, including PDL and APD conditions Solve the missing path problems between hosts and storage devices Troubleshooting vSphere Clusters Identify and troubleshoot vSphere HA problems Analyze and solve vSphere vMotion problems Diagnose and troubleshoot common vSphere DRS problems Troubleshooting Virtual Machines Discuss virtual machine files and disk content IDs Identify, analyze, and solve virtual machine snapshot problems Troubleshoot virtual machine power-on problems Identify possible causes and troubleshoot virtual machine connection state problems Diagnose and recover from VMware Tools installation failures Troubleshooting vCenter Server and ESXi Analyze and solve vCenter Server service problems Diagnose and troubleshoot vCenter Server database problems Use vCenter Server Appliance shell and the Bash shell to identify and solve problems Identify and troubleshoot ESXi host problems
Duration 1 Days 6 CPD hours This course is intended for This introductory-level course is great for experienced technical professionals working in a wide range of industries, such as software development, data science, marketing and advertising, finance, healthcare, and more, who are looking to use the latest AI and machine learning techniques in their day to day. The hands-on labs in this course use Python, so you should have some familiarity with Python scripting basics. Overview Working in an interactive learning environment, led by our engaging OpenAI expert you'll: Understand the capabilities and products offered by OpenAI and how to access them through the OpenAI API. set up an OpenAI environment on Azure, including creating an Azure virtual machine and configuring the environment to connect to Azure resources. Gain hands-on experience building a GPT-3 based chatbot on Azure and implement advanced natural language processing capabilities. Use the OpenAI API to access GPT-3 and generate high-quality text Learn how to use Whisper to improve the quality of text generation. Understand the capabilities of DALL-E and use it to generate images for unique and engaging visuals. Geared for technical professionals, Quick Start to Azure AI Basics for Technical Users is a fun, fast paced course designed to quickly get you up to speed with OpenAI?s powerful tools and functionality, and to provide hands-on experience in setting up an OpenAI environment on Azure. Guided by our AI expert, you?ll explore the capabilities of OpenAI's GPT-3, Whisper and DALL-E, and build a chatbot on Azure. It will provide you with the knowledge and resources to continue your journey in AI and machine learning and have a good understanding of the potential of OpenAI and Azure for your projects. First, you?ll dive into the world of OpenAI, learning about its products and the capabilities they offer. You'll also discover how Azure's offerings for AI and machine learning can complement OpenAI's tools and resources, providing you with a powerful combination for your projects. And don't worry if you're new to Azure, we'll walk you through the process of setting up an account and creating a resource group. As you progress through the course, you'll get the chance to work with OpenAI's GPT-3, one of the most advanced large language models available today. You'll learn how to use the OpenAI API to access GPT-3 and discover how to use it to generate high-quality text quickly and easily. And that's not all, you'll also learn how to build a GPT-3 based chatbot on Azure, giving you the opportunity to implement advanced natural language processing capabilities in your chatbot projects. The course will also cover OpenAI Whisper, an OpenAI tool that can improve the quality of text generation, allowing you to create more coherent and natural language content. And you will learn about OpenAI DALL-E, an OpenAI tool that can generate images, giving you the ability to create unique and engaging visuals to enhance your content and projects. Introduction to OpenAI and Azure Explore OpenAI and its products, as well as Azure's offerings for AI and Machine Learning, allowing you to understand the tools and resources available to you for your AI projects. Explore OpenAI and its products Explore Azure and its offerings for AI and Machine Learning Get Hands-On: Setting up an OpenAI environment on Azure Walk through the process of setting up an OpenAI environment on Azure, giving you the hands-on experience needed to start building your own projects using OpenAI and Azure. Create an Azure virtual machine and installing the OpenAI SDK Configure the OpenAI environment and connecting to Azure resources Explore OpenAI GPT-3 Learn about GPT-3, one of OpenAI's most powerful language models, and how to use it to generate high quality text, giving you the ability to create natural language content quickly and easily. Review GPT-3 and its capabilities Use the OpenAI API to access GPT-3 Get Hands-on: Building a GPT-3 based chatbot on Azure Learn how to build a GPT-3 based chatbot on Azure, giving you the opportunity to learn how to implement advanced natural language processing capabilities in your chatbot projects. Setup an Azure Function and creating a chatbot Integrate GPT-3 with the chatbot OpenAI Whisper Explore Whisper, an OpenAI tool that can improve the quality of text generation, allowing you to create more coherent and natural language content. Explore Whisper and its capabilities Use Whisper to improve the quality of text generation OpenAI DALL-E Explore DALL-E, an OpenAI tool that can generate images, giving you the ability to create unique and engaging visuals to enhance your content and projects. Explore DALL-E and its capabilities Use the OpenAI API to access DALL-E What?s Next: Keep Going! Other ways OpenAI can impact your day to day Explore great places to check for expanded tools and add-ons for Azure OpenAI Where to go for help and support Quick Look at Generative AI and its Business Implications Understanding Generative AI Generative AI in Business Ethical considerations of Generative AI
Duration 3 Days 18 CPD hours This course is intended for This in an intermediate and beyond-level course is geared for experienced Python developers looking to delve into the exciting field of Natural Language Processing. It is ideally suited for roles such as data analysts, data scientists, machine learning engineers, or anyone working with text data and seeking to extract valuable insights from it. If you're in a role where you're tasked with analyzing customer sentiment, building chatbots, or dealing with large volumes of text data, this course will provide you with practical, hands on skills that you can apply right away. Overview This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll: Master the fundamentals of Natural Language Processing (NLP) and understand how it can help in making sense of text data for valuable insights. Develop the ability to transform raw text into a structured format that machines can understand and analyze. Discover how to collect data from the web and navigate through semi-structured data, opening up a wealth of data sources for your projects. Learn how to implement sentiment analysis and topic modeling to extract meaning from text data and identify trends. Gain proficiency in applying machine learning and deep learning techniques to text data for tasks such as classification and prediction. Learn to analyze text sentiment, train emotion detectors, and interpret the results, providing a way to gauge public opinion or understand customer feedback. The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more. Our comprehensive curriculum covers a broad spectrum of NLP topics. Beginning with an introduction to NLP and feature extraction, the course moves to the hands-on development of text classifiers, exploration of web scraping and APIs, before delving into topic modeling, vector representations, text manipulation, and sentiment analysis. Half of your time is dedicated to hands-on labs, where you'll experience the practical application of your knowledge, from creating pipelines and text classifiers to web scraping and analyzing sentiment. These labs serve as a microcosm of real-world scenarios, equipping you with the skills to efficiently process and analyze text data. Time permitting, you?ll also explore modern tools like Python libraries, the OpenAI GPT-3 API, and TensorFlow, using them in a series of engaging exercises. By the end of the course, you'll have a well-rounded understanding of NLP, and will leave equipped with the practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems. You?ll be able to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots. Launch into the Universe of Natural Language Processing The journey begins: Unravel the layers of NLP Navigating through the history of NLP Merging paths: Text Analytics and NLP Decoding language: Word Sense Disambiguation and Sentence Boundary Detection First steps towards an NLP Project Unleashing the Power of Feature Extraction Dive into the vast ocean of Data Types Purification process: Cleaning Text Data Excavating knowledge: Extracting features from Texts Drawing connections: Finding Text Similarity through Feature Extraction Engineer Your Text Classifier The new era of Machine Learning and Supervised Learning Architecting a Text Classifier Constructing efficient workflows: Building Pipelines for NLP Projects Ensuring continuity: Saving and Loading Models Master the Art of Web Scraping and API Usage Stepping into the digital world: Introduction to Web Scraping and APIs The great heist: Collecting Data by Scraping Web Pages Navigating through the maze of Semi-Structured Data Unearth Hidden Themes with Topic Modeling Embark on the path of Topic Discovery Decoding algorithms: Understanding Topic-Modeling Algorithms Dialing the right numbers: Key Input Parameters for LSA Topic Modeling Tackling complexity with Hierarchical Dirichlet Process (HDP) Delving Deep into Vector Representations The Geometry of Language: Introduction to Vectors in NLP Text Manipulation: Generation and Summarization Playing the creator: Generating Text with Markov Chains Distilling knowledge: Understanding Text Summarization and Key Input Parameters for TextRank Peering into the future: Recent Developments in Text Generation and Summarization Solving real-world problems: Addressing Challenges in Extractive Summarization Riding the Wave of Sentiment Analysis Unveiling emotions: Introduction to Sentiment Analysis Tools Demystifying the Textblob library Preparing the canvas: Understanding Data for Sentiment Analysis Training your own emotion detectors: Building Sentiment Models Optional: Capstone Project Apply the skills learned throughout the course. Define the problem and gather the data. Conduct exploratory data analysis for text data. Carry out preprocessing and feature extraction. Select and train a model. ? Evaluate the model and interpret the results. Bonus Chapter: Generative AI and NLP Introduction to Generative AI and its role in NLP. Overview of Generative Pretrained Transformer (GPT) models. Using GPT models for text generation and completion. Applying GPT models for improving autocomplete features. Use cases of GPT in question answering systems and chatbots. Bonus Chapter: Advanced Applications of NLP with GPT Fine-tuning GPT models for specific NLP tasks. Using GPT for sentiment analysis and text classification. Role of GPT in Named Entity Recognition (NER). Application of GPT in developing advanced chatbots. Ethics and limitations of GPT and generative AI technologies.
Duration 1 Days 6 CPD hours This course is intended for Students taking this course are database administrators or prospective database administrators who have experience working with Access for Microsoft 365 and need to learn advanced skills. Overview In this course, you will manage an Access database and add complex database features to improve its usability, efficiency, performance, and security. You will: Share data across applications. Use action, unmatched, and duplicate queries to manage data. Create complex reports and forms. Use macros to improve user interface design. Use VBA to extend database capabilities. Perform database management tasks such as backup, compacting, repairing, performance analysis, checking object dependencies, and documenting. Implement security strategies and distribute a database to multiple users. You've covered many of the basic functions of Microsoft© Access©, and now you're ready to learn advanced Access features such as database management, advanced form design, packaging a database, encrypting a database, preparing a database for multiple-user access, and more. Knowledge of these features separates database professionals from the casual database users or occasional designers.This course is the third part of a three-course series that covers the skills needed to perform basic database design and development in Access.Microsoft© Access© for Office 365?: Part 1 : Focuses on the design and construction of an Access database?viewing, navigating, searching, and entering data in a database, as well as basic relational database design and creating simple tables, queries, forms, and reports.Microsoft© Access© for Office 365?: Part 2 : Focuses on optimization of an Access database, including optimizing performance and normalizing data; data validation; usability; and advanced queries, forms, and reports.Microsoft© Access© for Office 365?: Part 3 (this course): Focuses on managing the database and supporting complex database designs, including import and export of data; using action queries to manage data; creating complex forms and reports; macros and Visual Basic for Applications (VBA); and tools and strategies to manage, distribute, and secure a database.This course may be a useful component in your preparation for the Microsoft Access Expert (Microsoft 365 Apps and Office 2019): Exam MO-500 certification exam. Lesson 1: Importing and Exporting Table Data Topic A: Import and Link Data Topic B: Export Data Topic C: Create a Mail Merge Lesson 2: Using Queries to Manage Data Topic A: Create Action Queries Topic B: Create Unmatched and Duplicate Queries Lesson 3: Creating Complex Reports and Forms Topic A: Create Subreports Topic B: Create a Navigation Form Topic C: Show Details in Subforms and Popup Forms Lesson 4: Creating Access Macros Topic A: Create a Standalone Macro to Automate Repetitive Tasks Topic B: Create a Macro to Program a User Interface Component Topic C: Filter Records by Using a Condition Topic D: Create a Data Macro Lesson 5: Using VBA to Extend Database Capabilities Topic A: Introduction to VBA Topic B: Use VBA with Form Controls Lesson 6: Managing a Database Topic A: Back Up a Database Topic B: Manage Performance Issues Topic C: Document a Database Lesson 7: Distributing and Securing a Database Topic A: Split a Database for Multiple-User Access Topic B: Implement Security Topic C: Convert an Access Database to an ACCDE File Topic D: Package a Database with a Digital Signature
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 5 Days 30 CPD hours This course is intended for System administrators System integrators Overview By the end of the course, you should be able to meet the following objectives: Introduce troubleshooting principles and procedures Practice Linux commands that aid in the troubleshooting process Use command-line interfaces, log files, and the vSphere Client to diagnose and resolve problems in the vSphere environment Explain the purpose of key vSphere log files Identify networking problems based on reported symptoms, validate and troubleshoot the reported problem, identify the root cause and implement the appropriate resolution Analyze storage failure scenarios using a logical troubleshooting methodology, identify the root cause, and apply the appropriate resolution to resolve the problem Troubleshoot vSphere cluster failure scenarios and analyze possible causes Diagnose common VMware vSphere High Availability problems and provide solutions Identify and validate VMware ESXi⢠host and VMware vCenter Server problems, analyze failure scenarios, and select the correct resolution Troubleshoot virtual machine problems, including migration problems, snapshot problems, and connection problems Troubleshoot performance problems with vSphere components This five-day, hands-on training course provides you with the advanced knowledge, skills, and abilities to achieve competence in troubleshooting the VMware vSphere© 7.x environment. This workshop increases your skill and competence in using the command-line interface, VMware vSphere© Client?, log files, and other tools to analyze and solve problems Course Introduction Introductions and course logistics Course objectives Introduction to Troubleshooting Define the scope of troubleshooting Use a structured approach to solve configuration and operational problems Apply a troubleshooting methodology to logically diagnose faults and improve troubleshooting efficiency Troubleshooting Tools Use command-line tools (such as Linux commands, vSphere CLI, ESXCLI) to identify and troubleshoot vSphere problems Identify important vSphere log files and interpret the log file contents Troubleshooting Virtual Networking Analyze and resolve standard switch and distributed switch problems Analyze virtual machine connectivity problems and fix them Examine common management network connectivity problems and restore configurations Troubleshooting Storage Troubleshoot and resolve storage (iSCSI, NFS, and VMware vSphere© VMFS) connectivity and configuration problems Analyze and resolve common VM snapshot problems Identify multipathing-related problems, including common causes of permanent device loss (PDL) and all paths down (APD) events and resolve these problems Troubleshooting vSphere Clusters Identify and recover from problems related to vSphere HA Analyze and resolve VMware vSphere© vMotion© configuration and operational problems Analyze and resolve common VMware vSphere© Distributed Resource Scheduler? problems Troubleshooting Virtual Machines Identify possible causes and resolve virtual machine power-on problems Troubleshoot virtual machine connection state problems Resolve problems seen during VMware Tools? installations Troubleshooting vCenter Server and ESXi Analyze and fix problems with vCenter Server services Analyze and fix vCenter Server database problems Examine ESXi host and vCenter Server failure scenarios and resolve the problems
Duration 2 Days 12 CPD hours This course is intended for The audience for this course is an AWS Sysops Administrator Associate or equivalent. This person has one to two years of experience in AWS deployment, management, and operations. Students taking this course are interested in learning how Azure is different from AWS, and how Azure is administered. Students may also be interested in taking the AZ-103 Microsoft Azure Administrator certification exam, or the AZ-900 Azure Fundamentals exam. This two-day course is designed for AWS Sysops administrators interested in learning how Azure is administered. In this workshop which combines lecture with hands-on practical exercises and discussion/review, you will be introduced to Azure Administration, Azure Networking, Azure Compute, Azure Storage, and Azure Governance. During the workshop, you will apply this knowledge - building end-to-end architecture that demonstrates the main features discussed. Azure Administration In this module, you?ll learn about the tools and principle concepts needed to administer Azure. Topics include: Resource Manager, Resource Groups, Azure Portal, Azure CLI, Azure Templates, Cloud Shell, Azure Marketplace, and Azure PowerShell. Azure Networking In this module, you?ll learn about Azure networking features. Topics include: Azure Regions, Virtual Networks and Subnets, IP Addressing, Network Security Groups, Virtual Network Peering, VNet-to-VNet Connections, ExpressRoute, Load Balancers, and Network Watcher. Azure Compute In this module, you?ll learn about configuring and monitoring Azure virtual machines. Topics include: Azure Virtual Machines, Creating Virtual Machines, Virtual Machine Sizes, Virtual Machine Disks, Availability Zones, Availability Sets, Windows VM Connections, Linux VM Connections, Azure Monitor, and Azure Alerts. Azure Storage In this module, you?ll learn about Azure storage features and implementation. Topics include: Storage Accounts, Blob Storage, Blob Performance Tiers, File Shares, File Sync, Data Box, Content Delivery Network, Shared Access Signatures, and Service Endpoints. Azure Identity In this module, you?ll learn about Azure identity solutions. Topics include: Azure Domains, Role-based Access Control, Azure Active Directory, Multi-Factor Authentication, Azure AD Identity Protection, and Azure Policy. Additional course details: Nexus Humans AZ-010T00 Azure Administration for AWS SysOps 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 AZ-010T00 Azure Administration for AWS SysOps 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.