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Learn core Apache Kafka features along with creating Java, Node.js and Python producers and consumers

Duration 2 Days 12 CPD hours This course is intended for Cybersecurity analysts and engineers Security operations specialists Overview Successful completion of this instructor-led course with hands-on lab activities should enable participants to: Investigate and manage incidents Describe the Cortex XDR causality and analytics concepts Analyze alerts using the Causality and Timeline Views Work with Cortex XDR Pro actions such as remote script execution Create and manage on-demand and scheduled search queries in the Query Center Create and manage the Cortex XDR rules BIOC and IOC Working with Cortex XDR assets and inventories Write XQL queries to search datasets and visualize the result sets Work with Cortex XDR's external-data collection This instructor-led course teaches you how to use the Incidents pages of the Cortex XDR management console to investigate attacks. It explains causality chains, detectors in the Analytics Engine, alerts versus logs, log stitching, and the concepts of causality and analytics. You will learn how to analyze alerts using the Causality and Timeline Views and how to use advanced response actions, such as remediation suggestions, the EDL service, and remote script execution. Multiple modules focus on how to leverage the collected data. You will create simple search queries in one module and XDR rules in another. The course demonstrate how to use specialized investigation views to visualize artifact-related data, such as IP and Hash Views. Additionally, it provides an introduction to XDR Query Language (XQL). The course concludes with Cortex XDR external-data collection capabilities, including the use of Cortex XDR API to receive external alerts. This class is powered by Cloud Harmonics. Course Outline Module 1 - Cortex XDR Incidents Module 2 - Causality and Analytics Concepts Module 3 - Causality Analysis of Alerts Module 4 - Advanced Response Actions Module 5 - Building Search Queries Module 6 - Building XDR Rules Module 7 - Cortex XDR Assets Module 8 - Introduction to XQL Module 9 - External Data Collection

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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.

Want to learn about Kubernetes security or need to pass the Certified Kubernetes Security Specialist (CKS) exam? You are at the right place. A hands-on course to help you develop your Kubernetes security skills. You need to have a basic understanding of Kubernetes Administrator to get the best out of this course.

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This course will help you learn about the basic and advanced topics for automating tests with Selenium and Python. Without any prior knowledge of Python or automation testing, you will be able to perform automation testing with this practical course.

Duration 2 Days 12 CPD hours This course is intended for This course is intended for system and network administrators or operators responsible for the installation, setup, configuration, and administration of the BIG-IQ system or management of BIG-IP devices and F5 products running on those devices. This course uses lectures and hands-on exercises to give participants real-time experience in configuring and using the BIG-IQ© product. Students are introduced to BIG-IQ, its interface, and its various functionality. We first look at administering and operating the BIG-IQ system itself, then detail how it is used to remotely manage BIG-IP devices running BIG-IP Local Traffic Manager (LTM). We then look configuring a BIG-IQ Data Collection Device (DCD) system and see how it is used for the remote storage and examination of BIG-IP log events and statistics. Module 1: BIG-IQ Overview BIG-IQ Centralized Management BIG-IQ Components BIG-IQ Core Functionality REST API BIG-IQ Data Collection Device (DCD) BIG-IP Cloud Edition (CE) Setting up the BIG-IQ System Module 2: Administering the BIG-IQ System Controlling Access to the BIG-IQ Creating, Authenticating, Configuring Users Backups Local Host Settings Configuring DNS, NTP, and SMTP Monitoring BIG-IQ, DCD, and BIG-IP Events with Alerts Monitoring BIG-IQ with iHealth Post Installation Issues; Licensing, Changing Management IP, Master Key, Restoring Backups Module 3: Managing BIG-IP LTM Devices BIG-IP LTM Device Discovery BIG-IP Device Backup Deploying to BIG-IP Devices Deployment and Deployment Logs Configuration Snapshots Managing BIG-IP Certificates Managing BIG-IP Licenses Monitoring BIG-IP Devices with iHealth Management of QKView Reports from Managed BIG-IP Devices Module 4: Setting Up the BIG-IQ Data Collection Device Custom Roles Types and Groups Setting up User Accounts with custom roles and privileges Managing BIG-IP DSC Discovery and management of BIG-IP Device Clusters (DSC) with BIG-IQ Administering BIG-IQ High Availability Configuration and management of BIG-IQ systems in a High Availability pair