1673 Computing & IT courses in Warlingham delivered Live Online

Duration 5 Days 30 CPD hours This course is intended for This introductory-level, fast-paced course is for skilled web developers new to React who have prior experienced working HTML5, CSS3 and JavaScript. Overview Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore: A basic and advanced understanding of React components An advanced, in-depth knowledge of how React works A complete understanding of using Redux How to build, validate, and populate interactive forms How to use inline styles for perfect looking components How to test React components How to build and use components How to get control of your build process A deep understanding of data-driven modeling with props and state How to use client-side routing for pages in your apps How to debug a React application Mastering React is a comprehensive hands-on course that aims to be the single most useful resource on getting up to speed quickly with React. Geared for more experienced web developers new to React, this course provides students with the core knowledge and hands-on skills they require to build reliable, powerful React apps. After the first few modules, you?ll have a solid understanding of React?s fundamentals and will be able to build a wide array of rich, interactive web apps with the framework. The first module is an introduction to the new functionality in ECMAScript 6 (JavaScript). Client-side routing between pages, managing complex state, and heavy API interaction at scale are also covered. This course consists of two parts. In the first part of the course students will explore all the fundamentals with a progressive, example-driven approach. You?ll create your first apps, learn how to write components, start handling user interaction, and manage rich forms. We end the first part by exploring the inner workings of Create React App (Facebook?s tool for running React apps), writing automated unit tests, and building a multi-page app that uses client-side routing. The latter part of the course moves into more advanced concepts that you?ll see used in large, production applications. These concepts explore strategies for data architecture, transport, and management: Redux is a state management paradigm based on the Flux architecture. Redux provides a structure for large state trees and allows you to decouple user interaction in your app from state changes. GraphQL is a powerful, typed, REST API alternative where the client describes the data it needs. Hooks is the powerful, new way to maintain state and properties with functional components and the future of React according to Facebook. ES6 Primer (Optional) Prefer const and let over var Arrow functions Modules Object.assign() Template literals The spread operator and Rest parameters Enhanced object literals Default arguments Destructuring assignments Your first React Web Application Setting up your development environment JavaScript ES6 /ES7 Getting started What?s a component? Our first component Building the App Making the App data-driven Your app?s first interaction Updating state and immutability Refactoring with the Babel plugin transform-class-properties JSX and the Virtual DOM React Uses a Virtual DOM Why Not Modify the Actual DOM? What is a Virtual DOM? Virtual DOM Pieces ReactElement JSX JSX Creates Elements JSX Attribute Expressions JSX Conditional Child Expressions JSX Boolean Attributes JSX Comments JSX Spread Syntax JSX Gotchas JSX Summary Components A time-logging app Getting started Breaking the app into components The steps for building React apps from scratch Updating timers Deleting timers Adding timing functionality Add start and stop functionality Methodology review Advanced Component Configuration with props, state, and children ReactComponent props are the parameters PropTypes Default props with getDefaultProps() context state Stateless Components Talking to Children Components with props.children Forms Forms 101 Text Input Remote Data Async Persistence Redux Form Modules Unit Testing & Jest Writing tests without a framework What is Jest? Using Jest Testing strategies for React applications Testing a basic React component with Enzyme Writing tests for the food lookup app Writing FoodSearch.test.js Routing What?s in a URL? React Router?s core components Building the components of react-router Dynamic routing with React Router Supporting authenticated routes Intro to Flux and Redux Why Flux? Flux is a Design Pattern Flux implementations Redux & Redux?s key ideas Building a counter The core of Redux The beginnings of a chat app Building the reducer() Subscribing to the store Connecting Redux to React Intermediate Redux Using createStore() from the redux library Representing messages as objects in state Introducing threads Adding the ThreadTabs component Supporting threads in the reducer Adding the action OPEN_THREAD Breaking up the reducer function Adding messagesReducer() Defining the initial state in the reducers Using combineReducers() from redux React Hooks Motivation behind Hooks How Hooks Map to Component Classes Using Hooks Requires react 'next' useState() Hook Example useEffect() Hook Example useContext() Hook Example Using Custom Hooks Using Webpack with Create React App JavaScript modules Create React App Exploring Create React App Webpack basics Making modifications Hot reloading; Auto-reloading Creating a production build Ejecting Using Create React App with an API server When to use Webpack/Create React App Using GraphQL Your First GraphQL Query GraphQL Benefits GraphQL vs. REST GraphQL vs. SQL Relay and GraphQL Frameworks Chapter Preview Consuming GraphQL Exploring With GraphiQL GraphQL Syntax 101 . Complex Types Exploring a Graph Graph Nodes ; Viewer Graph Connections and Edges Mutations Subscriptions GraphQL With JavaScript GraphQL With React

Duration 4 Days 24 CPD hours This course is intended for The primary audience for this course is as follows: Security administrators Security consultants Network administrators System engineers Technical support personnel Channel partners and resellers Overview Upon completing this course, the learner will be able to meet these overall objectives: Define the use and placement IDS/IPS components. Identify Snort features and requirements. Compile and install Snort. Define and use different modes of Snort. Install and utilize Snort supporting software. Securing Cisco Networks with Open Source Snort (SSFSNORT) v3.0 is a 4-day course that shows you how to deploy Snort© in small to enterprise-scale implementations. You will learn how to install, configure, and operate Snort in Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) modes. You?ll practice installing and configuring Snort, utilize additional software tools and define rules to configure and improve the Snort environment, and more. The course qualifies for 32 Cisco Continuing Education credits (CE) towards recertification.This course will help you:Learning how to implement Snort, an open-source, rule-based, intrusion detection and prevention system. Gain leading-edge skills for high-demand responsibilities focused on security. Module 1: Detecting Intrusions with Snort 3.0 History of Snort IDS IPS IDS vs. IPS Examining Attack Vectors Application vs. Service Recognition Module 2: Sniffing the Network Protocol Analyzers Configuring Global Preferences Capture and Display Filters Capturing Packets Decrypting Secure Sockets Layer (SSL) Encrypted Packets Module 3: Architecting Nextgen Detection Snort 3.0 Design Modular Design Support Plug Holes with Plugins Process Packets Detect Interesting Traffic with Rules Output Data Module 4: Choosing a Snort Platform Provisioning and Placing Snort Installing Snort on Linux Module 5: Operating Snort 3.0 Start Snort Monitor the System for Intrusion Attempts Define Traffic to Monitor Log Intrusion Attempts Actions to Take When Snort Detects an Intrusion Attempt License Snort and Subscriptions Module 6: Examining Snort 3.0 Configuration Introducing Key Features Configure Sensors Lua Configuration Wizard Module 7: Managing Snort Pulled Pork Barnyard2 Elasticsearch, Logstash, and Kibana (ELK) Module 8: Analyzing Rule Syntax and Usage Anatomy of Snort Rules Understand Rule Headers Apply Rule Options Shared Object Rules Optimize Rules Analyze Statistics Module 9: Use Distributed Snort 3.0 Design a Distributed Snort System Sensor Placement Sensor Hardware Requirements Necessary Software Snort Configuration Monitor with Snort Module 10: Examining Lua Introduction to Lua Get Started with Lua

Duration 2 Days 12 CPD hours This course is intended for This in an Introductory and beyond level course is geared for experienced Java developers seeking to be proficient in Apache Kafka. Attendees should be experienced developers who are comfortable with Java, and have reasonable experience working with databases. Overview Working in a hands-on learning environment, students will explore Overview of Streaming technologies Kafka concepts and architecture Programming using Kafka API Kafka Streams Monitoring Kafka Tuning / Troubleshooting Kafka Apache Kafka is a real-time data pipeline processor. It high-scalability, fault tolerance, execution speed, and fluid integrations are some of the key hallmarks that make it an integral part of many Enterprise Data architectures. In this lab intensive two day course, students will learn how to use Kafka to build streaming solutions. Introduction to Streaming Systems Fast data Streaming architecture Lambda architecture Message queues Streaming processors Introduction to Kafka Architecture Comparing Kafka with other queue systems (JMS / MQ) Kaka concepts : Messages, Topics, Partitions, Brokers, Producers, commit logs Kafka & Zookeeper Producing messages Consuming messages (Consumers, Consumer Groups) Message retention Scaling Kafka Programming With Kafka Configuration parameters Producer API (Sending messages to Kafka) Consumer API (consuming messages from Kafka) Commits , Offsets, Seeking Schema with Avro Kafka Streams Streams overview and architecture Streams use cases and comparison with other platforms Learning Kafka Streaming concepts (KStream, KTable, KStore) KStreaming operations (transformations, filters, joins, aggregations) Administering Kafka Hardware / Software requirements Deploying Kafka Configuration of brokers / topics / partitions / producers / consumers Security: How secure Kafka cluster, and secure client communications (SASL, Kerberos) Monitoring : monitoring tools Capacity Planning : estimating usage and demand Trouble shooting : failure scenarios and recovery Monitoring and Instrumenting Kafka Monitoring Kafka Instrumenting with Metrics library Instrument Kafka applications and monitor their performance

Duration 3 Days 18 CPD hours This course is intended for This course is for information technology professionals, security professionals, network, system managers and administrators tasked with installing, configuring and maintaining Symantec Data Center Security: Server Advanced. Overview At the completion of the course, you will be able to: Describe the major components of Symantec Data Center Security: Server Advanced and how they communicate. Install the management server, console and agent. Define, manage and create assets, policies, events and configurations. Understand policy creation and editing in depth. course is an introduction to implementing and managing a Symantec Data Center Security: Server Advanced 6.0 deployment. Introduction Course Overview The Classroom Lab Environment Introduction to Security Risks and Risk Security Risks Security Risk Management Managing and Protecting Systems Corporate Security Policies and Security Assessments Host-Based Computer Security Issues SDCS:Server Advanced Overview SDCS: Server Advanced Component Overview Policy Types and Platforms Management Console Overview Agent User Interface Overview DEMO of Management Console Installation and Deployment Planning the Installation Deploying SDCS:SA for High Availability Scalability Installing the Management Server Installing the Management Console Installing a Windows Agent Installing a UNIX Agent LAB: Install Manager and Agents Configuring Assets Asset and Agent Overview Viewing Agents and Assets Managing Agents Managing Agents on Assets LAB: Create Asset Groups LAB: Examine Agent Interface Policy Overview Policies Defined Prevention Policy Overview Process Sets Resource Access Policy Options Detection Policy Overview IDS Capabilities Rules Collectors Policy Management Workspace User Interface on Agent Example Use Cases LAB: Paper Based Scenarios LAB: What type of security strategy should be used? Detailed Prevention Policies Policy Editor Policy Structure Global Policy Options Service Options Program Options Policy Processing Order Network Rules File Rules Registry Rules Process Sets Predefined Policies LAB: Deploy Strict policy LAB: Examine Functionality Advanced Prevention Profiling Applications Customizing Predefined Policies LAB: Modify Policy Previously Deployed LAB: Re-examine Functionality LAB: Preparing for Policy deployment LAB: Best Practice - Covering Basics LAB: Further Enhance Strict Policy LAB: Create Custom Process Set LAB :Secure an FTP Server LAB: Troubleshoot Policy/pset Assignment Using CLI Detection Policies Detection Policies Structure Collectors Rules Predefined Detection Policies Creating a Detection Policy Using the Template Policy LAB: Deploy Baseline Policy LAB: Create Custom Policy Event Management Events Defined Viewing Events Reports and Queries Overview Creating Queries and Reports Creating Alerts LAB: View Monitor Types and Search Events LAB: Create Real Time Monitor Agent Management and Troubleshooting Configurations Defined Creating and Editing Configurations Common Parameters Prevention Settings Detection Settings Analyzing Agent Log Files Diagnostic Policies Local Agent Tool ? sisipsconfig LAB: Create Custom Configurations LAB: Implement Bulk Logging LAB: Disable Prevention on Agent Using CLI LAB: Use Diagnostic Policy to Gather Logs LAB: Troubleshoot a Policy System Management Managing Users and Roles Server Security Viewing and Managing Server Settings Viewing and Managing Database Settings Viewing and Managing Tomcat Settings LAB: Create a New User LAB: View System Settings

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 3 Days 18 CPD hours This course is intended for Data Wrangling with Python takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context. Overview By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. In this course you will start with the absolute basics of Python, focusing mainly on data structures. Then you will delve into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python.This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets. Introduction to Data Structure using Python Python for Data Wrangling Lists, Sets, Strings, Tuples, and Dictionaries Advanced Operations on Built-In Data Structure Advanced Data Structures Basic File Operations in Python Introduction to NumPy, Pandas, and Matplotlib NumPy Arrays Pandas DataFrames Statistics and Visualization with NumPy and Pandas Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame Deep Dive into Data Wrangling with Python Subsetting, Filtering, and Grouping Detecting Outliers and Handling Missing Values Concatenating, Merging, and Joining Useful Methods of Pandas Get Comfortable with a Different Kind of Data Sources Reading Data from Different Text-Based (and Non-Text-Based) Sources Introduction to BeautifulSoup4 and Web Page Parsing Learning the Hidden Secrets of Data Wrangling Advanced List Comprehension and the zip Function Data Formatting Advanced Web Scraping and Data Gathering Basics of Web Scraping and BeautifulSoup libraries Reading Data from XML RDBMS and SQL Refresher of RDBMS and SQL Using an RDBMS (MySQL/PostgreSQL/SQLite) Application in real life and Conclusion of course Applying Your Knowledge to a Real-life Data Wrangling Task An Extension to Data Wrangling

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 4.5 Days 27 CPD hours This course is intended for This intermediate course is for people who are new to the RACF component of the z/OS Security Server, and responsible for security administration. This includes people who are planning to implement RACF for the first time, and people who are security administrators in installations where RACF is already implemented. Those inexperienced z/OS users may find the course Basics of z/OS RACF Administration (ES19) more appropriate Overview Identify the security requirements of a system Evaluate the facilities and options of RACF Define users to RACF Set up a RACF group structure Use RACF to protect resources Select options to tailor RACF Evaluate and implement RACF database and performance options Identify tools available for auditing Administer the system so that it is consistent with the installation's security goals Be a more effective security administrator using the RACF component of the z/OS Security Server to define users, set up group structures, define general resources, protect z/OS data sets, & use several RACF utilities. Security and RACF overview . Administering groups and users . Protecting z/OS data sets . Introduction to user administration and delegation and general resources . RACF database, tables, and performance options . RACF utilities and exits . RACF options . Auditing the RACF environment . Storage management and RACF . Security for JES facilities . Security classification .

Duration 5 Days 30 CPD hours This course is intended for This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. Overview In this course, you will learn to: Use data science principles to address business issues. Apply the extract, transform, and load (ETL) process to prepare datasets. Use multiple techniques to analyze data and extract valuable insights. Design a machine learning approach to address business issues. Train, tune, and evaluate classification models. Train, tune, and evaluate regression and forecasting models. Train, tune, and evaluate clustering models. Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance. For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, it can also inform?by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice Addressing Business Issues with Data Science Topic A: Initiate a Data Science Project Topic B: Formulate a Data Science Problem Extracting, Transforming, and Loading Data Topic A: Extract Data Topic B: Transform Data Topic C: Load Data Analyzing Data Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Use Visualizations to Analyze Data Topic D: Preprocess Data Designing a Machine Learning Approach Topic A: Identify Machine Learning Concepts Topic B: Test a Hypothesis Developing Classification Models Topic A: Train and Tune Classification Models Topic B: Evaluate Classification Models Developing Regression Models Topic A: Train and Tune Regression Models Topic B: Evaluate Regression Models Developing Clustering Models Topic A: Train and Tune Clustering Models Topic B: Evaluate Clustering Models Finalizing a Data Science Project Topic A: Communicate Results to Stakeholders Topic B: Demonstrate Models in a Web App Topic C: Implement and Test Production Pipelines