Name: LangChain Masterclass - Build 15 OpenAI and LLAMA 2 LLM Apps Using Python
Price: 14.99 GBP
Availability: InStock

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			<h2>Overview
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					<p>This comprehensive course on <strong>Complete iOS 11 and Swift 4</strong> will deepen your understanding on this topic.</p><p>After successful completion of this course you can acquire the required skills in this sector. This <strong>Complete iOS 11 and Swift 4 </strong>comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.</p><p>So enrol in this course today to fast track your career ladder.</p><section></section>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.</p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p>There is no experience or previous qualifications required for enrolment on this Complete iOS 11 and Swift 4. It is available to all students, of all academic backgrounds.</p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p>Our Complete iOS 11 and Swift 4 is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</p><p>There is no time limit for completing this course, it can be studied in your own time at your own pace.</p>					</div>
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			<h2>Career Path</h2>		</div>
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					<p>Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to-</p><ul><li>Open doors of opportunities</li><li>Increase your adaptability</li><li>Keep you relevant</li><li>Boost confidence</li></ul><p>And much more!</p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>13 sections • 177 lectures • 19:08:00 total length</p></div><div><div><div><div><div><div><div><p>•iOS 11 Course Overview: 00:11:00</p></div><div><div><p>•Install Xcode 9: 00:07:00</p></div><div><div><p>•Xcode 9 Beta 4 Update: 00:09:00</p></div><div><div><p>•App: Hustle - Your first iOS 11 App: 00:22:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Variables, operators, and how computers work: 00:17:00</p></div><div><div><p>•Strings in Swift: 00:15:00</p></div><div><div><p>•Working with numbers in Swift: 00:20:00</p></div><div><div><p>•Swift Functions: 00:23:00</p></div><div><div><p>•Booleans: 00:21:00</p></div><div><div><p>•Constants: 00:10:00</p></div><div><div><p>•Array Data Structure in Swift: 00:13:00</p></div><div><div><p>•Swift Loops: 00:19:00</p></div><div><div><p>•Dictionary Data Structure in Swift: 00:19:00</p></div><div><div><p>•Object Oriented Programming in Swift: 00:12:00</p></div><div><div><p>•Inheritance: 00:08:00</p></div><div><div><p>•Polymorphism: 00:08:00</p></div><div><div><p>•Optionals: 00:21:00</p></div><div><div><p>•Enumerations: 00:20:00</p></div><div><div><p>•Extensions: Part 1: 00:15:00</p></div><div><div><p>•Extensions: Part 2: 00:21:00</p></div><div><div><p>•Intro to Protocols, Delegates - Numbers Example: 00:13:00</p></div><div><div><p>•Intro to Protocols, Delegates Part 2 - Question Generator: 00:18:00</p></div><div><div><p>•Protocols, Delegates - Building Color Magic App UI: 00:17:00</p></div><div><div><p>•Protocols, Delegates - Using the Delegate Method in Color Magic App: 00:16:00</p></div><div><div><p>•Protocols, Delegates - Using Mutating Functions in Types: 00:18:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Git and Version Control - The Fun Way!: 00:12:00</p></div><div><div><p>•Terminal Basics - Changing Directories: 00:06:00</p></div><div><div><p>•Terminal Basics - Creating Directories and Files: 00:05:00</p></div><div><div><p>•Terminal Basics - Copying and Renaming Files: 00:09:00</p></div><div><div><p>•Terminal Basics - Deleting Files and Directories: 00:06:00</p></div><div><div><p>•Git Basics: 00:17:00</p></div><div><div><p>•Setting up Github: 00:05:00</p></div><div><div><p>•Working with Local and Remote Repositories: 00:11:00</p></div><div><div><p>•Handling Git Merge Conflicts: 00:17:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•App: Swoosh 01 - Creating the Welcome Screen: 00:25:00</p></div><div><div><p>•App: Swoosh 02 - Working with Frames: 00:16:00</p></div><div><div><p>•App: Swoosh 03 - Intro to Auto Layout: 00:27:00</p></div><div><div><p>•App: Swoosh 04 - Working with Stack Views: 00:27:00</p></div><div><div><p>•App: Swoosh 05 - Intro to Segues (Changing Screens): 00:10:00</p></div><div><div><p>•App: Swoosh 06 - Refactoring in Xcode 9: 00:10:00</p></div><div><div><p>•App: Swoosh 07 - Debugging: setValue forUndefinedKey: 00:04:00</p></div><div><div><p>•App: Swoosh 08 - Programmatic Segues: 00:09:00</p></div><div><div><p>•App: Swoosh 09 - IBActions (Handling Events) and Data Models: 00:16:00</p></div><div><div><p>•App: Swoosh 10 - Passing Data Between View Controllers: 00:12:00</p></div><div><div><p>•App: Dev Profile 01 - Auto layout for iPhones: 00:22:00</p></div><div><div><p>•App: Dev Profile 02 - Auto layout for iPads (Size Classes): 00:20:00</p></div><div><div><p>•App: Window Shopper 01 - Custom Text Fields: 00:18:00</p></div><div><div><p>•App: Window Shopper 02 - Input Accessory Views: 00:15:00</p></div><div><div><p>•App: Window Shopper 03 - Unit Testing our Data: 00:17:00</p></div><div><div><p>•App: Window Shopper 04 - Calculation Algorithm: 00:13:00</p></div><div><div><p>•App: Window Shopper 05 - Custom Drawing with drawRect: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•App: Coder Swag 01 - Project creation: 00:23:00</p></div><div><div><p>•App: Coder Swag 02 - Tableviews, Delegate, and Data Source: 00:33:00</p></div><div><div><p>•App: Coder Swag 03 - Collection Views (Grid Layouts): 00:13:00</p></div><div><div><p>•App: Coder Swag 04 - Working with Data Models: 00:14:00</p></div><div><div><p>•App: Coder Swag 05 - Displaying Data in Collection View Cells: 00:20:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Intro to Chat App: 00:04:00</p></div><div><div><p>•App: Smack - Project Setup: 00:26:00</p></div><div><div><p>•App: Smack - SWReveal: 00:20:00</p></div><div><div><p>•App: Smack - ChannelVC UI: 00:25:00</p></div><div><div><p>•App: Smack - LoginVC UI: 00:22:00</p></div><div><div><p>•App: Smack - CreateAccountVC UI: 00:19:00</p></div><div><div><p>•App: Smack - Web request and API: 00:09:00</p></div><div><div><p>•App: Smack - Hosting API: 00:20:00</p></div><div><div><p>•App: Smack - Locally Hosting API: 00:18:00</p></div><div><div><p>•App: Smack - Creating a web request in Xcode: 00:28:00</p></div><div><div><p>•App: Smack - Registering a User: 00:16:00</p></div><div><div><p>•App: Smack - Logging in a user: 00:20:00</p></div><div><div><p>•App: Smack - Creating a user: 00:26:00</p></div><div><div><p>•App: Smack - Avatar Picker Part 1: 00:19:00</p></div><div><div><p>•App: Smack - Avatar Picker Part 2: 00:20:00</p></div><div><div><p>•App: Smack - Generate a Avatar BG Color: 00:26:00</p></div><div><div><p>•App: Smack - LoggedIn Interface: 00:23:00</p></div><div><div><p>•App: Smack - Profile View: 00:25:00</p></div><div><div><p>•App: Smack - Logging in users: 00:23:00</p></div><div><div><p>•App: Smack - Getting channels: 00:19:00</p></div><div><div><p>•App: Smack - Channels TableView: 00:14:00</p></div><div><div><p>•App: Smack - Add Channel VC: 00:19:00</p></div><div><div><p>•App: Smack - Sockets and Channels: 00:26:00</p></div><div><div><p>•App: Smack - Refining Login Flow: 00:19:00</p></div><div><div><p>•App: Smack - Fetching Messages: 00:20:00</p></div><div><div><p>•App: Smack - Sending First Message: 00:17:00</p></div><div><div><p>•App: Smack - Displaying Chat Messages: 00:18:00</p></div><div><div><p>•App: Smack - Sockets and Messages: 00:19:00</p></div><div><div><p>•App: Smack - Typing Users: 00:22:00</p></div><div><div><p>•App: Smack - Unread Channels: 00:18:00</p></div><div><div><p>•Where to go from here: 00:08:00</p></div><div><div><p>•I'm Back: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Intro to App: Pixel City: 00:02:00</p></div><div><div><p>•Creating Xcode Project: Pixel City: 00:04:00</p></div><div><div><p>•Installing Alamofire / AlamofireImage Cocoapods: 00:07:00</p></div><div><div><p>•Building MapVC UI /Conforming to MKMapViewDelegate /Setting Delegate of mapView: 00:15:00</p></div><div><div><p>•Requesting Location Services in iOS 11 / Centering Map On User Location: 00:18:00</p></div><div><div><p>•Adding UITapGestureRecognizer to Drop Custom Pins on MapView: 00:15:00</p></div><div><div><p>•Setting a Custom Map Annotation Color: 00:05:00</p></div><div><div><p>•Animating Photos View / Programmatically adding spinner and label subviews: 00:20:00</p></div><div><div><p>•Adding UILabel for Pull Up View / Adding UICollectionView Programmatically: 00:17:00</p></div><div><div><p>•Getting API Key from Flickr / Using Flickr API URL Format: 00:14:00</p></div><div><div><p>•Using Alamofire to Download URLS: 00:21:00</p></div><div><div><p>•Using Alamofire to Download Images / Cancelling All Sessions: 00:16:00</p></div><div><div><p>•Setting Up UICollectionView / Adding Images / Reloading UICollectionView: 00:10:00</p></div><div><div><p>•Building PopVC / Presentation PopVC When UICollectionViewCell is Tapped: 00:16:00</p></div><div><div><p>•Adding 3D Touch Peek: 00:15:00</p></div><div><div><p>•Challenge 1: 00:02:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Setting up developer: 00:10:00</p></div><div><div><p>•Implementing Google AdMob: 00:19:00</p></div><div><div><p>•Fetching a list of Products: 00:15:00</p></div><div><div><p>•Starting an in-app Purchase: 00:09:00</p></div><div><div><p>•Testing in-app Purchases: 00:18:00</p></div><div><div><p>•Restoring in-app Purchases after App Deletion: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Intro to App: GoalPost: 00:03:00</p></div><div><div><p>•Creating Xcode Project / Project Folders: 00:04:00</p></div><div><div><p>•Building GoalsVC: 00:14:00</p></div><div><div><p>•Building GoalCell: 00:14:00</p></div><div><div><p>•What is Core Data?: 00:06:00</p></div><div><div><p>•Creating Goal Core Data Entity and Attributes: 00:08:00</p></div><div><div><p>•Displaying Static GoalCells in UITableView / Creating GoalType Enum: 00:10:00</p></div><div><div><p>•Building CreateGoalVC: 00:15:00</p></div><div><div><p>•Creating a UIViewController Extension: 00:11:00</p></div><div><div><p>•Creating a UIView / UIButton Extension: 00:19:00</p></div><div><div><p>•Building FinishGoalVC / Passing Data from CreateGoalVC: 00:19:00</p></div><div><div><p>•Saving Goal Data to Persistent Store: 00:13:00</p></div><div><div><p>•Fixing Dismissal of FinishGoalVC: 00:07:00</p></div><div><div><p>•Fetching Data from Persistent Store / Filling UITableView with Fetched Data: 00:16:00</p></div><div><div><p>•Removing Objects from Persistent Store using UITableView Delete Action: 00:11:00</p></div><div><div><p>•Setting Goal Progress for UITableViewCell: 00:15:00</p></div><div><div><p>•Challenge 2: 00:01:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Intro to App: Breakpoint: 00:03:00</p></div><div><div><p>•Creating Xcode Project / Setting Up Project Folders: 00:05:00</p></div><div><div><p>•Creating Firebase Project: 00:11:00</p></div><div><div><p>•Setting Up DataService / Creating Firebase Database Users: 00:11:00</p></div><div><div><p>•Building AuthVC and LoginVC in Interface Builder: 00:18:00</p></div><div><div><p>•Creating InsetTextField and ShadowView Subclasses: 00:18:00</p></div><div><div><p>•Setting up AuthService: 00:13:00</p></div><div><div><p>•Building FeedVC and GroupsVC in Interface Builder: 00:16:00</p></div><div><div><p>•Presenting LoginVC from AppDelegate / Allowing Login with Email: 00:22:00</p></div><div><div><p>•Building MeVC and Adding to UITabBarController: 00:10:00</p></div><div><div><p>•Creating CreatePostVC and Uploading Posts to Firebase: 00:20:00</p></div><div><div><p>•Creating UIView Extension for Binding Views to Keyboard: 00:15:00</p></div><div><div><p>•Building FeedCell: 00:10:00</p></div><div><div><p>•Writing the Message Model and Getting All Feed Messages from Firebase: 00:21:00</p></div><div><div><p>•Converting UIDs into Emails and Reversing the Order of a TableView: 00:20:00</p></div><div><div><p>•Creating CreateGroupVC and Connecting @IBOutlets/Actions: 00:15:00</p></div><div><div><p>•Creating UserCell: 00:16:00</p></div><div><div><p>•Searching for Email Accounts to Add to Group: 00:19:00</p></div><div><div><p>•Adding Users to Group with didSelectRowAt indexPath: 00:21:00</p></div><div><div><p>•Creating Groups and pushing them to Firebase: 00:16:00</p></div><div><div><p>•Creating GroupCell: 00:15:00</p></div><div><div><p>•Creating Group Model and Getting All Groups from Firebase: 00:19:00</p></div><div><div><p>•Building GroupFeedVC: 00:18:00</p></div><div><div><p>•Initializing Group Data for a Group and Presenting on GroupFeedVC: 00:16:00</p></div><div><div><p>•Downloading All Message for a Group and Animating Upon New Message: 00:24:00</p></div><div><div><p>•Creating a UIViewController Extension for Presenting GroupFeedVC: 00:07:00</p></div><div><div><p>•Challenge 3: 00:02:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Intro to app: 00:02:00</p></div><div><div><p>•Intro to CoreML: 00:05:00</p></div><div><div><p>•What is machine learning?: 00:08:00</p></div><div><div><p>•Creating Xcode 9 project: 00:03:00</p></div><div><div><p>•Building UI: 00:18:00</p></div><div><div><p>•AVFoundation: 00:18:00</p></div><div><div><p>•Tap gestures to take snapshot on item: 00:11:00</p></div><div><div><p>•Core ML Xcode 9 Beta 4 Update / Fix Preview Photo Crash: 00:03:00</p></div><div><div><p>•Downloading CoreML models: 00:21:00</p></div><div><div><p>•Adding UI controls for flash control: 00:07:00</p></div><div><div><p>•Training your app to speak what it sees: 00:18:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•App: RampUp - Intro to ARKit App: 00:02:00</p></div><div><div><p>•App: RampUp - Resources: 00:04:00</p></div><div><div><p>•App: RampUp - Project creation: 00:11:00</p></div><div><div><p>•App: RampUp - SceneKit, 3D models: 00:11:00</p></div><div><div><p>•App: RampUp - Ramp picker popover: 00:14:00</p></div><div><div><p>•App: RampUp - 3D models in SceneKit for popover: 00:18:00</p></div><div><div><p>•App: RampUp - 3D models in SceneKit for popover part 2: 00:12:00</p></div><div><div><p>•App: RampUp - Detecting taps on 3D objects: 00:17:00</p></div><div><div><p>•App: RampUp - Placing ramps in ARKit: 00:24:00</p></div><div><div><p>•App: RampUp - Moving objects in 3D space in augmented reality: 00:20:00</p></div><div><div><p>•ARKit - where to go next: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Assignment - Complete iOS 11 and Swift 4: 00:00:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Apex Learning

Online Course Creation

App Development

App Store Optimisation

iOS Development

Xcode

Trainers Course

Complete iOS 11 and Swift 4

General Admission

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>The ideal audience for the RPA and UiPath Boot Camp is beginners in the field of RPA and individuals in roles such as developers, project managers, operation analysts, and tech enthusiasts looking to familiarize themselves with automation technologies. It's also perfectly suited for business professionals keen on understanding and implementing automated solutions within their organizations to optimize processes.</p>Overview<p>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. Working in a hands-on learning environment, led by our Automation Learning expert instructor, students will explore:<br />Gain a thorough understanding of Robotic Process Automation (RPA) and its applications using UiPath, setting a solid foundation for future learning and application.<br />Learn to record and play in UiPath Studio, a key skill that enables automating complex tasks in a user-friendly environment.<br />Master the art of designing and controlling workflows using Sequencing, Flowcharting, and Control Flow, helping to streamline and manage automation processes effectively.<br />Acquire practical skills in data manipulation, from variable management to CSV/Excel and data table conversions, empowering you to handle data-rich tasks with confidence.<br />Develop competence in managing controls and exploring various plugins and extensions, providing a broader toolkit for handling diverse automation projects.<br />Get hands-on experience with exception handling, debugging, logging, code management, and bot deployment, fundamental skills that ensure your automated processes are reliable and efficient.<br />How to deploy and control Bots with UiPath Orchestrator</p><p>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.</p></div><div id="course-pills-2"><h4>What is Robotic Process  Automation?</h4><ul><li>Scope and techniques of automation </li><li>Robotic process automation </li><li>About UiPath </li><li>The future of automation</li></ul><h4>Record and Play</h4><ul><li>UiPath stack </li><li>Downloading and installing UiPath Studio </li><li>Learning UiPath Studio </li><li>Task recorder </li><li>Step-by-step examples using the recorder</li></ul><h4>Sequence, Flowchart, and Control Flow</h4><ul><li>Sequencing the workflow Activities </li><li>Control flow, various types of loops, and decision making </li><li>Step-by-step example using Sequence and Flowchart </li><li>Step-by-step example using Sequence and Control flow</li></ul><h4>Data Manipulation</h4><ul><li>Variables and scope </li><li>Collections </li><li>Arguments ? Purpose and use </li><li>Data table usage with examples </li><li>Clipboard management </li><li>File operation with step-by-step example </li><li>CSV/Excel to data table and vice versa (with a step-by-step example) </li></ul><h4>Taking Control of the Controls</h4><ul><li>Finding and attaching windows </li><li>Finding the control </li><li>Techniques for waiting for a control Act on controls ? mouse and keyboard activities </li><li>Working with UiExplorer </li><li>Handling events </li><li>Revisit recorder </li><li>Screen Scraping </li><li>When to use OCR </li><li>Types of OCR available </li><li>How to use OCR </li><li>Avoiding typical failure points</li></ul><h4>Tame that Application with Plugins and Extensions</h4><ul><li>Terminal plugin </li><li>SAP automation </li><li>Java plugin </li><li>Citrix automation </li><li>Mail plugin </li><li>PDF plugin </li><li>Web integration </li><li>Excel and Word plugins </li><li>Credential management Extensions ? Java, Chrome, Firefox, and Silverlight</li></ul><h4>Handling User Events and Assistant Bots</h4><ul><li>What are assistant bots? </li><li>Monitoring system event triggers </li><li>Monitoring image and element triggers </li><li>Launching an assistant bot on a keyboard event</li></ul><h4>Exception Handling, Debugging, and Logging</h4><ul><li>Exception handling </li><li>Common exceptions and ways to handle them </li><li>Logging and taking screenshots </li><li>Debugging techniques </li><li>Collecting crash dumps </li><li>Error reporting </li></ul><h4>Managing and Maintaining the Code</h4><ul><li>Project organization </li><li>Nesting workflows </li><li>Reusability of workflows </li><li>Commenting techniques </li><li>State Machine </li><li>When to use Flowcharts, State Machines, or Sequences Using config files and examples of a config file </li><li>Integrating a TFS server</li></ul><h4>Deploying and Maintaining the Bot</h4><ul><li>Publishing using publish utility </li><li>Overview of Orchestration Server </li><li>Using Orchestration Server to control bots </li><li>Using Orchestration Server to deploy bots </li><li>License management </li><li>Publishing and managing updates</li></ul></div>

Nexus Human

Robotic Surgery

Robotics

UiPath

NLP Practitioner

UiPath Certified Advanced RPA Developer (UiARD)

RPA Boot Camp / Hands-On Robotic Process Automation (RPA) (TTAI4000)

Studyhub UK

Learning & Development

Online Course Marketing

Learning To Learn

Deep Learning

Deep Learning & Neural Networks Python - Keras

NextGen Learning

Business Analytics & Intelligence

Big Data Analytics

Marketing Analytics

Data Analysis

Data Skills

Data Analyst

Data Analyst (Data Analytics)

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Understand the mechanics behind AI prompts.</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Initiate your journey in prompt engineering.</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Apply basic and advanced prompting techniques.</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Create effective prompts for Dall-E&#39;s image generation.</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Utilize Dall.E Outpainting and Stable Diffusion for creative prompts.</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Leverage Midjourney for image prompts and styles.</span></li></ul><div data-id="4afcd14" data-element_type="widget" data-widget_type="text-editor.default" style="color: rgb(35, 35, 35); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(35, 35, 35);"><div data-id="4afcd14" data-element_type="widget" data-widget_type="text-editor.default" style="color: rgb(35, 35, 35); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(35, 35, 35);"><div data-id="4afcd14" data-element_type="widget" data-widget_type="text-editor.default" style="color: rgb(35, 35, 35); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(35, 35, 35);"><div data-id="4afcd14" data-element_type="widget" data-widget_type="text-editor.default" style="color: rgb(35, 35, 35); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(35, 35, 35);"><div data-id="4afcd14" data-element_type="widget" data-widget_type="text-editor.default" style="color: rgb(35, 35, 35); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(35, 35, 35);"><div data-id="4afcd14" data-element_type="widget" data-widget_type="text-editor.default" style="color: rgb(35, 35, 35); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(35, 35, 35);"><div data-id="4afcd14" data-element_type="widget" data-widget_type="text-editor.default" style="color: rgb(35, 35, 35); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(35, 35, 35);"><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">The realm of AI and machine learning is evolving, and prompt engineering stands at the forefront of this revolution. This comprehensive course on Prompt Engineering is designed to equip you with the skills to effectively interact with and guide AI models.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">&ldquo;Understanding How Prompts Work&rdquo; lays the foundation, elucidating the principles that make AI respond to prompts. &ldquo;Getting Started with Prompt Engineering&rdquo; then transitions you from theory to practice, preparing you for the hands-on aspects of prompt crafting.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Modules like &ldquo;Basic Prompting Methods&rdquo; and &ldquo;Advanced Prompting&rdquo; provide a gradient learning curve, ensuring you are well-equipped to handle a variety of prompting scenarios. With &ldquo;Dall E and Image Prompting,&rdquo; dive into the world of AI-generated imagery, learning to create prompts that yield vivid, accurate visuals.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">In &ldquo;Writing Prompts for Dall-E&rdquo; and &ldquo;Dall.E Outpainting,&rdquo; your skills are further refined, enabling you to explore and create with these powerful AI tools. &ldquo;Stable Diffusion Prompt Writing Guide&rdquo; adds another layer to your expertise, specifically tailored for this innovative image synthesis model.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">The course then guides you through &ldquo;Midjourney Image Prompts and Styles,&rdquo; expanding your creative possibilities. In &ldquo;ChatGPT Effective Prompting Tips,&rdquo; you&rsquo;ll learn to interact with one of the most sophisticated conversational AI models effectively.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Join this course to become adept at steering AI through prompts, opening doors to new possibilities in creative, professional, and technological domains. Enroll now and be part of the AI revolution!</p></div></div></div></div></div></div></div><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">What is a Prompt</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Elements of a Prompt</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">What is Prompt Engineering</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">How to Design a Prompt</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Setting up your environment</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Installing Necessary Libraries</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Choosing a Machie Learning Framework</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Developing Programming Skills</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Basic Prompting</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">A Little Advanced Prompting</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Making AI Models Factual</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Examples of Various Prompts</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Zero-Shot Prompting</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Few-Shot Prompting</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Chain-of-Thought (CoT) and Zero-Shot CoT Prompting</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Self-Consistency</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Generated Knowledge</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Image Prompting</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Style Modifiers</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Quality Boosters</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Attention to Detail</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Generating Different Image Styles</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Trying Different Art Medium Styles</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Getting Digital Looks</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Make it Retro!</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Generating Good Photographs</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Making Objects</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">What to Avoid</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Generating Better Prompts with ChatGPT</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Tweaking Images</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">What is Outpainting</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">How to Use Outpainting</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Basic Guideline for Prompt Engineering for Image Generation</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Repetition</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Weighing Terms</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Resource</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Midjourney Image Prompts and Styles</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Parameters in Version 4</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Crafting Effective ChatGPT Prompts A Guide</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">A Few General Guidelines for OpenAI Prompt Engineering</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Parameters</strong></li></ul>

LearnDrive UK

Computer Engineering

Engineering Management

Engineering

Step into the future of AI with our Prompt Engineering course! Master the art of prompt crafting for Dall-E, Stable Diffusion, ChatGPT, and more. Learn advanced techniques and best practices to optimize interactions with cutting-edge AI models.























Prompt Engineering

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>Business Analysts, Technical Managers, and Programmers</p>Overview<p>This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice.</p><p>Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning.</p></div><div id="course-pills-2"><h4>What is R</h4><ul><li>? What is R?</li><li>? Positioning of R in the Data Science Space</li><li>? The Legal Aspects</li><li>? Microsoft R Open</li><li>? R Integrated Development Environments</li><li>? Running R</li><li>? Running RStudio</li><li>? Getting Help</li><li>? General Notes on R Commands and Statements</li><li>? Assignment Operators</li><li>? R Core Data Structures</li><li>? Assignment Example</li><li>? R Objects and Workspace</li><li>? Printing Objects</li><li>? Arithmetic Operators</li><li>? Logical Operators</li><li>? System Date and Time</li><li>? Operations</li><li>? User-defined Functions</li><li>? Control Statements</li><li>? Conditional Execution</li><li>? Repetitive Execution</li><li>? Repetitive execution</li><li>? Built-in Functions</li><li>? Summary</li></ul><h4>Introduction to Functional Programming with R</h4><ul><li>? What is Functional Programming (FP)?</li><li>? Terminology: Higher-Order Functions</li><li>? A Short List of Languages that Support FP</li><li>? Functional Programming in R</li><li>? Vector and Matrix Arithmetic</li><li>? Vector Arithmetic Example</li><li>? More Examples of FP in R</li><li>? Summary</li></ul><h4>Managing Your Environment</h4><ul><li>? Getting and Setting the Working Directory</li><li>? Getting the List of Files in a Directory</li><li>? The R Home Directory</li><li>? Executing External R commands</li><li>? Loading External Scripts in RStudio</li><li>? Listing Objects in Workspace</li><li>? Removing Objects in Workspace</li><li>? Saving Your Workspace in R</li><li>? Saving Your Workspace in RStudio</li><li>? Saving Your Workspace in R GUI</li><li>? Loading Your Workspace</li><li>? Diverting Output to a File</li><li>? Batch (Unattended) Processing</li><li>? Controlling Global Options</li><li>? Summary</li></ul><h4>R Type System and Structures</h4><ul><li>? The R Data Types</li><li>? System Date and Time</li><li>? Formatting Date and Time</li><li>? Using the mode() Function</li><li>? R Data Structures</li><li>? What is the Type of My Data Structure?</li><li>? Creating Vectors</li><li>? Logical Vectors</li><li>? Character Vectors</li><li>? Factorization</li><li>? Multi-Mode Vectors</li><li>? The Length of the Vector</li><li>? Getting Vector Elements</li><li>? Lists</li><li>? A List with Element Names</li><li>? Extracting List Elements</li><li>? Adding to a List</li><li>? Matrix Data Structure</li><li>? Creating Matrices</li><li>? Creating Matrices with cbind() and rbind()</li><li>? Working with Data Frames</li><li>? Matrices vs Data Frames</li><li>? A Data Frame Sample</li><li>? Creating a Data Frame</li><li>? Accessing Data Cells</li><li>? Getting Info About a Data Frame</li><li>? Selecting Columns in Data Frames</li><li>? Selecting Rows in Data Frames</li><li>? Getting a Subset of a Data Frame</li><li>? Sorting (ordering) Data in Data Frames by Attribute(s)</li><li>? Editing Data Frames</li><li>? The str() Function</li><li>? Type Conversion (Coercion)</li><li>? The summary() Function</li><li>? Checking an Object's Type</li><li>? Summary</li></ul><h4>Extending R</h4><ul><li>? The Base R Packages</li><li>? Loading Packages</li><li>? What is the Difference between Package and Library?</li><li>? Extending R</li><li>? The CRAN Web Site</li><li>? Extending R in R GUI</li><li>? Extending R in RStudio</li><li>? Installing and Removing Packages from Command-Line</li><li>? Summary</li></ul><h4>Read-Write and Import-Export Operations in R</h4><ul><li>? Reading Data from a File into a Vector</li><li>? Example of Reading Data from a File into A Vector</li><li>? Writing Data to a File</li><li>? Example of Writing Data to a File</li><li>? Reading Data into A Data Frame</li><li>? Writing CSV Files</li><li>? Importing Data into R</li><li>? Exporting Data from R</li><li>? Summary</li></ul><h4>Statistical Computing Features in R</h4><ul><li>? Statistical Computing Features</li><li>? Descriptive Statistics</li><li>? Basic Statistical Functions</li><li>? Examples of Using Basic Statistical Functions</li><li>? Non-uniformity of a Probability Distribution</li><li>? Writing Your Own skew and kurtosis Functions</li><li>? Generating Normally Distributed Random Numbers</li><li>? Generating Uniformly Distributed Random Numbers</li><li>? Using the summary() Function</li><li>? Math Functions Used in Data Analysis</li><li>? Examples of Using Math Functions</li><li>? Correlations</li><li>? Correlation Example</li><li>? Testing Correlation Coefficient for Significance</li><li>? The cor.test() Function</li><li>? The cor.test() Example</li><li>? Regression Analysis</li><li>? Types of Regression</li><li>? Simple Linear Regression Model</li><li>? Least-Squares Method (LSM)</li><li>? LSM Assumptions</li><li>? Fitting Linear Regression Models in R</li><li>? Example of Using lm()</li><li>? Confidence Intervals for Model Parameters</li><li>? Example of Using lm() with a Data Frame</li><li>? Regression Models in Excel</li><li>? Multiple Regression Analysis</li><li>? Summary</li></ul><h4>Data Manipulation and Transformation in R</h4><ul><li>? Applying Functions to Matrices and Data Frames</li><li>? The apply() Function</li><li>? Using apply()</li><li>? Using apply() with a User-Defined Function</li><li>? apply() Variants</li><li>? Using tapply()</li><li>? Adding a Column to a Data Frame</li><li>? Dropping A Column in a Data Frame</li><li>? The attach() and detach() Functions</li><li>? Sampling</li><li>? Using sample() for Generating Labels</li><li>? Set Operations</li><li>? Example of Using Set Operations</li><li>? The dplyr Package</li><li>? Object Masking (Shadowing) Considerations</li><li>? Getting More Information on dplyr in RStudio</li><li>? The search() or searchpaths() Functions</li><li>? Handling Large Data Sets in R with the data.table Package</li><li>? The fread() and fwrite() functions from the data.table Package</li><li>? Using the Data Table Structure</li><li>? Summary</li></ul><h4>Data Visualization in R</h4><ul><li>? Data Visualization</li><li>? Data Visualization in R</li><li>? The ggplot2 Data Visualization Package</li><li>? Creating Bar Plots in R</li><li>? Creating Horizontal Bar Plots</li><li>? Using barplot() with Matrices</li><li>? Using barplot() with Matrices Example</li><li>? Customizing Plots</li><li>? Histograms in R</li><li>? Building Histograms with hist()</li><li>? Example of using hist()</li><li>? Pie Charts in R</li><li>? Examples of using pie()</li><li>? Generic X-Y Plotting</li><li>? Examples of the plot() function</li><li>? Dot Plots in R</li><li>? Saving Your Work</li><li>? Supported Export Options</li><li>? Plots in RStudio</li><li>? Saving a Plot as an Image</li><li>? Summary</li></ul><h4>Using R Efficiently</h4><ul><li>? Object Memory Allocation Considerations</li><li>? Garbage Collection</li><li>? Finding Out About Loaded Packages</li><li>? Using the conflicts() Function</li><li>? Getting Information About the Object Source Package with the pryr Package</li><li>? Using the where() Function from the pryr Package</li><li>? Timing Your Code</li><li>? Timing Your Code with system.time()</li><li>? Timing Your Code with System.time()</li><li>? Sleeping a Program</li><li>? Handling Large Data Sets in R with the data.table Package</li><li>? Passing System-Level Parameters to R</li><li>? Summary</li></ul><h4>Lab Exercises</h4><ul><li>Lab 1 -  Getting Started with R      </li><li>Lab 2 -  Learning the R Type System and Structures             </li><li>Lab 3 -  Read and Write Operations in R    </li><li>Lab 4 -  Data Import and Export in R           </li><li>Lab 5 -  k-Nearest Neighbors Algorithm    </li><li>Lab 6 - Creating Your Own Statistical Functions     </li><li>Lab 7 - Simple Linear Regression   </li><li>Lab 8 - Monte-Carlo Simulation (Method)               </li><li>Lab 9 -  Data Processing with R     </li><li>Lab 10 - Using R Graphics Package              </li><li>Lab 11 -  Using R Efficiently</li></ul></div>

Computing Skills

Programming Fundamentals

R (programming language)

Data Analysis Expressions

Statistical Computing

Introduction to R Programming

Powerbi

Microsoft Power BI

Microsoft Power Platform

Microsoft BI

Microsoft Power BI Masterclass 2021

Diploma in Management Studies (DMS)

Diploma in Professional Studies (DPS)

ICDL (International Certification of Digital Literacy)

Advanced Diploma in Microsoft Power BI 2021 at QLS Level 7

<h2 style="color: rgb(51, 51, 51); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction</strong></h2><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Ever felt the sting of realizing you&#39;ve sent an email with a glaring grammatical error? Or maybe you&#39;ve submitted a report only to find out later that it&#39;s peppered with mistakes? We&#39;ve all been there. Good grammar is more than just a nitpicky concern&mdash;it&#39;s essential for clear communication. This is where&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><a href="https://www.nativeassignmenthelp.co.uk/grammar-checker" data-mce-href="https://www.nativeassignmenthelp.co.uk/grammar-checker" style="color: rgb(51, 122, 183); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 122, 183);">free grammar checker online</a></strong> come to the rescue, ensuring our writing is polished and professional.</p><h3 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">What is a Grammar Checker?</strong></h3><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Definition and Purpose</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">A grammar checker is a digital tool designed to identify and correct grammatical errors in a text. It scans your writing for issues like misspellings, punctuation errors, and syntax mistakes, offering suggestions for improvement.</p><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">How Grammar Checkers Work</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">These tools use algorithms and natural language processing to analyze text. They compare your writing against a vast database of grammatical rules and common usage patterns. Some advanced checkers even use artificial intelligence to understand context and provide more nuanced suggestions.</p><h3 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Why Use Free Grammar Checkers?</strong></h3><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Cost-Effectiveness</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Not everyone has the budget for premium software. Free grammar checkers provide a cost-effective solution for students, professionals, and casual writers alike.</p><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Accessibility and Convenience</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Most free grammar checkers are available online and can be used without the need for downloads or installations. This makes them incredibly convenient for quick checks on the go.</p><h3 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Features to Look for in a Grammar Checker</strong></h3><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Accuracy</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">The primary function of a grammar checker is to accurately identify and correct errors. Look for tools with a high accuracy rate.</p><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Ease of Use</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">A good grammar checker should be user-friendly and intuitive, requiring minimal effort to use.</p><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Integration with Other Tools</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Check if the grammar checker integrates well with your writing platforms, such as word processors, email clients, or browsers.</p><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Customization Options</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Customization options allow you to tailor the tool to your specific needs, such as choosing between British and American English or setting formal and informal writing styles.</p><h3 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">How to Use Online Grammar Checkers Effectively</strong></h3><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Input Quality</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Start with the best possible version of your text. The cleaner your initial draft, the more effective the grammar checker will be.</p><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Reviewing Suggestions</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Don&#39;t accept every suggestion blindly. Review each recommendation to ensure it fits the context and improves your writing.</p><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Making Final Edits</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">After using the grammar checker, make a final pass through your document to catch any remaining issues and refine your text.</p><h3 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Benefits of Using Grammar Checkers</strong></h3><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Improved Writing Skills</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Regular use of grammar checkers can help you learn from your mistakes and improve your writing skills over time.</p><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Time Efficiency</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Grammar checkers save you time by quickly identifying and correcting errors, allowing you to focus on content and creativity.</p><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Increased Confidence</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Knowing that your writing is error-free boosts your confidence, whether you&#39;re sending an email, submitting a report, or publishing a blog post.</p><h3 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Potential Drawbacks of Free Grammar Checkers</strong></h3><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Limitations in Accuracy</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">While free grammar checkers are useful, they are not infallible. They may miss subtle errors or provide incorrect suggestions.</p><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Over-Reliance on Tools</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Relying too heavily on grammar checkers can prevent you from developing your proofreading skills. It&#39;s essential to strike a balance.</p><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Privacy Concerns</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Using online grammar checkers involves uploading your text to their servers, which may raise privacy concerns, especially for sensitive documents.</p><h3 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Grammar Checker Tools for Different Needs</strong></h3><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Academic Writing</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">For academic writing, tools like Grammarly and ProWritingAid offer advanced features to ensure your essays and papers are error-free and well-structured.</p><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Business Writing</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Professionals can benefit from grammar checkers that provide style and tone suggestions, such as Grammarly and Ginger Software, to maintain a polished and professional image.</p><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Casual Writing</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">For casual writing, simpler tools like Hemingway Editor and LanguageTool can help improve readability and catch basic errors.</p><h3 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">The Future of Grammar Checking Technology</strong></h3><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">AI and Machine Learning</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Advances in AI and machine learning are continually improving the accuracy and capabilities of&nbsp;<a href="https://www.newassignmenthelp.co.uk/grammar-checker" data-mce-href="https://www.newassignmenthelp.co.uk/grammar-checker" style="color: rgb(51, 122, 183); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: rgb(51, 122, 183);">best grammar checker</a>, making them more reliable and user-friendly.</p><h4 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Enhancements in Usability and Accuracy</strong></h4><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">Future developments may include better contextual understanding, real-time collaboration features, and enhanced integration with various digital platforms.</p><h3 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Conclusion</strong></h3><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);">In today&#39;s fast-paced digital world, maintaining impeccable grammar is more important than ever. Free online grammar checkers provide a valuable service, helping writers at all levels produce clear, error-free content. While they have their limitations, their benefits far outweigh the drawbacks, making them an essential tool in any writer&#39;s toolkit.</p><h3 style="color: rgb(51, 51, 51); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">FAQs</strong></h3><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Are free grammar checkers reliable?</strong>&nbsp;Free grammar checkers are generally reliable for basic grammar and spelling checks. However, they may not catch all errors or provide as detailed feedback as premium versions.</p><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Can grammar checkers replace human proofreading?</strong>&nbsp;Grammar checkers are a helpful aid, but they cannot entirely replace human proofreading. They may miss contextual nuances and subtle errors that a human eye would catch.</p><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">How secure are online grammar checkers?</strong>&nbsp;Most reputable grammar checkers have privacy policies in place to protect user data. However, it&#39;s always a good idea to avoid uploading sensitive information to online tools.</p><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Do grammar checkers support multiple languages?</strong>&nbsp;Yes, many grammar checkers, like LanguageTool, support multiple languages, making them useful for multilingual writers.</p><p style="color: rgb(51, 51, 51); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(51, 51, 51);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Are there grammar checkers specifically for academic writing?</strong>&nbsp;Yes, tools like Grammarly and ProWritingAid offer features tailored to academic writing, such as advanced grammar checks, plagiarism detection, and citation support.</p>

Adam Thomas

Free Grammar Checkers Online in the UK

Packt

Memory

Python

This comprehensive course unlocks the boundless potential of LangChain, Pinecone, OpenAI, and LLAMA 2 LLM, guiding you from AI novice to expert. Dive into 15 different practical projects, from dynamic chatbots to data analysis tools, and cultivate a profound understanding of AI, empowering your journey into the future of language-based applications.

LangChain Masterclass - Build 15 OpenAI and LLAMA 2 LLM Apps Using Python

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.</p><p></p>Overview<p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.</p><p></p><p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.</p><p></p>Prerequisites<p></p><li>Creating cloud resources in Microsoft Azure.</li>  <li>Using Python to explore and visualize data.</li>  <li>Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.</li>  <li>Working with containers</li>  <li>AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.</li><p></p></div><div id="course-pills-2"><h3>1 - Design a data ingestion strategy for machine learning projects</h3> <ul> <li>Identify your data source and format</li> <li>Choose how to serve data to machine learning workflows</li> <li>Design a data ingestion solution</li> </ul> <h3>2 - Design a machine learning model training solution</h3> <ul> <li>Identify machine learning tasks</li> <li>Choose a service to train a machine learning model</li> <li>Decide between compute options</li> </ul> <h3>3 - Design a model deployment solution</h3> <ul> <li>Understand how model will be consumed</li> <li>Decide on real-time or batch deployment</li> </ul> <h3>4 - Design a machine learning operations solution</h3> <ul> <li>Explore an MLOps architecture</li> <li>Design for monitoring</li> <li>Design for retraining</li> </ul> <h3>5 - Explore Azure Machine Learning workspace resources and assets</h3> <ul> <li>Create an Azure Machine Learning workspace</li> <li>Identify Azure Machine Learning resources</li> <li>Identify Azure Machine Learning assets</li> <li>Train models in the workspace</li> </ul> <h3>6 - Explore developer tools for workspace interaction</h3> <ul> <li>Explore the studio</li> <li>Explore the Python SDK</li> <li>Explore the CLI</li> </ul> <h3>7 - Make data available in Azure Machine Learning</h3> <ul> <li>Understand URIs</li> <li>Create a datastore</li> <li>Create a data asset</li> </ul> <h3>8 - Work with compute targets in Azure Machine Learning</h3> <ul> <li>Choose the appropriate compute target</li> <li>Create and use a compute instance</li> <li>Create and use a compute cluster</li> </ul> <h3>9 - Work with environments in Azure Machine Learning</h3> <ul> <li>Understand environments</li> <li>Explore and use curated environments</li> <li>Create and use custom environments</li> </ul> <h3>10 - Find the best classification model with Automated Machine Learning</h3> <ul> <li>Preprocess data and configure featurization</li> <li>Run an Automated Machine Learning experiment</li> <li>Evaluate and compare models</li> </ul> <h3>11 - Track model training in Jupyter notebooks with MLflow</h3> <ul> <li>Configure MLflow for model tracking in notebooks</li> <li>Train and track models in notebooks</li> </ul> <h3>12 - Run a training script as a command job in Azure Machine Learning</h3> <ul> <li>Convert a notebook to a script</li> <li>Run a script as a command job</li> <li>Use parameters in a command job</li> </ul> <h3>13 - Track model training with MLflow in jobs</h3> <ul> <li>Track metrics with MLflow</li> <li>View metrics and evaluate models</li> </ul> <h3>14 - Perform hyperparameter tuning with Azure Machine Learning</h3> <ul> <li>Define a search space</li> <li>Configure a sampling method</li> <li>Configure early termination</li> <li>Use a sweep job for hyperparameter tuning</li> </ul> <h3>15 - Run pipelines in Azure Machine Learning</h3> <ul> <li>Create components</li> <li>Create a pipeline</li> <li>Run a pipeline job</li> </ul> <h3>16 - Register an MLflow model in Azure Machine Learning</h3> <ul> <li>Log models with MLflow</li> <li>Understand the MLflow model format</li> <li>Register an MLflow model</li> </ul> <h3>17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning</h3> <ul> <li>Understand Responsible AI</li> <li>Create the Responsible AI dashboard</li> <li>Evaluate the Responsible AI dashboard</li> </ul> <h3>18 - Deploy a model to a managed online endpoint</h3> <ul> <li>Explore managed online endpoints</li> <li>Deploy your MLflow model to a managed online endpoint</li> <li>Deploy a model to a managed online endpoint</li> <li>Test managed online endpoints</li> </ul> <h3>19 - Deploy a model to a batch endpoint</h3> <ul> <li>Understand and create batch endpoints</li> <li>Deploy your MLflow model to a batch endpoint</li> <li>Deploy a custom model to a batch endpoint</li> <li>Invoke and troubleshoot batch endpoints</li> </ul></div>

Data Engineering

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

DP-100T01 Designing and Implementing a Data Science Solution on Azure

Data Science

Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.

Python Machine Learning Bootcamp

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

Machine Learning with Real World Projects

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			<h2><b>Course Overview</b></h2>		</div>
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					<div><p><span>Machine learning as a programming technique has shaped the future of technology. In this course, you will learn how to build intelligent handwriting recognition apps from scratch using Python and Core ML.</span></p><p><span>The </span><b>Machine Learning for Apps Level 4 </b><span>course will teach you how to take advantage of machine learning to code like a pro and build incredible apps that can make predictions. Designed by industry experts, it covers best practices for managing projects, core concepts for creating your own ML model, building a convolutional neural network, and much more.</span></p><p><span>On successful completion, you will be able to build an amazing handwriting recognition app and convolutional neural network from scratch, and have an in-depth understanding of the core ML basics. This course is ideal for those with a basic understanding of iOS development.</span></p></div>
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					<div><p>This best selling <strong>Machine Learning for Apps Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Machine Learning for Apps Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The <strong>Machine Learning for Apps Level 4 </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Machine Learning for Apps Level 4 </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the<strong> Machine Learning for Apps Level 4</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Machine Learning for Apps Level 4 </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the <strong>Machine Learning for Apps Level 4 </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Course Cloud

Machine Learning (ML)

Machine Learning for Apps Level 4

<strong>The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.</strong>
        <p>Machine learning is a field of computer science through which you can create complex models that perform multiple functions using mathematical input. Python is a popular choice to create machine learning models due to a plethora of libraries easily accessible. This course takes you through this impressive combination of Python and machine learning, teaching you the basics of machine learning to create your own projects.

You'll begin learning about different types of machine learning models and how to choose the relevant ones for your project. You'll learn to optimize this model and apply performance metrics to track its performance. You'll also learn topics like regression, classification, and clustering to improve the performance of your model. You'll learn the basics of neural networks and use scikit-learn to perform calculations in your project.

By the end of this course, you'll have created a face recognition application using everything you've learned in this course.

The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Train different types of machine learning models for your project<br>Prepare and clean data for your project<br>Optimize your machine learning model to best suit your project needs<br>Build your own machine learning model from scratch<br>Apply performance metrics to track the performance of your model<br>Use scikit-learn to perform calculations in your project</p>
        <h2>Audience</h2><p>This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.</p>
        <h2>Approach</h2><p>This is a hands-on course that takes you through all the machine learning concepts step-by-step while showing you real-world applications for each step.</p>
        <h2>Key Features</h2><p>Build a face recognition application from scratch * Prepare and train your data for your projects * Learn real-world applications of your algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><br><h4>2. Why Machine Learning</h4><p></p><br><h4>3. Process of Learning from Data</h4><p></p><br><h4>4. Machine Learning Methods</h4><p></p><br><h4>5. Data Preparation and Preprocessing</h4><p></p><br><h4>6. Machine Learning Models and Optimization</h4><p></p><br><h4>7. Building Machine Learning Model from Scratch</h4><p></p><br><h4>8. Overfitting, Underfitting and Generalization</h4><p></p><br><h4>9. Machine Learning Model Performance Metrics</h4><p></p><br><h4>10. Dimensionality Reduction</h4><p></p><br><h4>11. Deep Learning Overview</h4><p></p><br><h4>12. Hands-on Machine Learning Project Using Scikit-Learn</h4><p></p><br><h4>13. OPTIONAL Section- Mathematics Wrap-up</h4><p></p>

The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.

Python Machine Learning Crash Course for Beginners

<strong>Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis</strong>
        <p>Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. 

In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. 

We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist! 

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels</p>
        <h2>What You Will Learn</h2><p>Confidently work with two of the leading ML packages: statsmodels and sklearn <br>Understand how to perform a linear regression <br>Become familiar with the ins and outs of logistic regression <br>Get to grips with carrying out cluster analysis (both flat and hierarchical) <br>Apply your skills to real-life business cases <br>Get insights into the underlying ideas behind ML models</p>
        <h2>Audience</h2><p>If you want to get acquainted with fundamental machine learning methods, become a successful data scientist, or just get started with building valuable skills in machine learning and data science, this course is for you.</p>
        <h2>Approach</h2><p>The course has a perfect blend of theory and practical knowledge to help you gain a solid understand of the concepts covered.</p>
        <h2>Key Features</h2><p>Learn machine learning with StatsModels and sklearn  * Apply machine learning skills to solve real-world business cases  * Get started with linear regression, logistic regression, and cluster analysis</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/machine-learning-101-with-scikit-learn-and-statsmodels</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Setting Up the Working Environment</h4><p></p><br><h4>3. Linear Regression with StatsModels</h4><p></p><br><h4>4. Linear Regression with Sklearn</h4><p></p><br><h4>5. Linear Regression - Practical Example</h4><p></p><br><h4>6. Logistic Regression</h4><p></p><br><h4>7. Cluster Analysis</h4><p></p><br><h4>8. Cluster Analysis: Additional Topics</h4><p></p>

Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis

Machine Learning 101 with Scikit-learn and StatsModels

<strong>In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.</strong>
        <p>Machine learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and machine language (ML) experts to develop products, features, and projects that will help them unleash the power of machine learning. This course will show you how to prepare data for machine learning algorithms using Python and pandas library.

The course starts by explaining the installation process of Anaconda and Jupyter. Once you are ready with the setup, you will understand Python fundamentals, such as variables, data types, conditional statements, loops, and modules. Next, you will go through the pandas library and learn how to use it for loading real-world large datasets. Towards the end, you will learn the steps and techniques to clean data and make it ready to move into machine learning algorithms.

By the end of this course, you will be well-versed with Python fundamentals and pandas library and will be ready to take on data science projects.

The code bundle for this course is available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2</p>
        <h2>What You Will Learn</h2><p>Understand how to use Jupyter Lab for Jupyter notebooks<br>Learn Python fundamentals<br>Load large datasets from files using pandas<br>Uncover techniques to perform data analysis and exploration<br>Group, sort, export, select, filter, and clean data<br>Find out how to preview the data frame</p>
        <h2>Audience</h2><p>This course is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. You will understand the fundamentals of Python and learn how to pre-process data before using it in machine learning algorithms. There are no prerequisites for taking up this course; however, it is recommended to finish with the Machine Learning for Absolute Beginners - Level 1 course.</p>
        <h2>Approach</h2><p>With the help of exercises and activities, this course will show you how to use Python and pandas library for cleaning data before moving it into machine learning algorithms.</p>
        <h2>Key Features</h2><p>Learn how to use Python for data science and machine learning projects * Understand the interface of Jupyter lab tool * Discover how to get data ready for machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2 </p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 2!</h4><p></p><br><h4>2. Python Fundamentals for Data Science</h4><p></p><br><h4>3. Introduction to the Pandas Library</h4><p></p><br><h4>4. Loading Data into a DataFrame</h4><p></p><br><h4>5. Data Cleaning and Transformation</h4><p></p><br><h4>6. Course Summary</h4><p></p>

In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.

Machine Learning for Absolute Beginners - Level 2

<strong>Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!</strong>
        <p>Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
&bull;	Set up a Python development environment correctly
&bull;	Gain complete machine learning toolsets to tackle most real-world problems
&bull;	Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
&bull;	Combine multiple models with by bagging, boosting or stacking
&bull;	Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
&bull;	Develop in Jupyter (IPython) notebook, Spyder and various IDE
&bull;	Communicate visually and effectively with Matplotlib and Seaborn
&bull;	Engineer new features to improve algorithm predictions
&bull;	Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
&bull;	Use SVM for handwriting recognition, and classification problems in general
&bull;	Use decision trees to predict staff attrition
&bull;	Apply the association rule to retail shopping datasets
&bull;	And much more!
By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.</p>
        <h2>What You Will Learn</h2><p>&bull;&nbsp;&nbsp;Learn to Build Powerful Machine Learning Models to Solve Any Problem<br>&bull;&nbsp;&nbsp;Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells &amp; more</p>
        <h2>Audience</h2><p>A newbie who wants to learn machine learning algorithm with Python. Anyone who has a deep interest in the practical application of machine learning to real world problems. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms. Any intermediate to advanced EXCEL users who is unable to work with large datasets. Anyone interested to present their findings in a professional and convincing manner. Anyone who wishes to start or transit into a career as a data scientist. Anyone who wants to apply machine learning to their domain.</p>
        <h2>Approach</h2><p>You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.</p>
        <h2>Key Features</h2><p>&bull;&nbsp;&nbsp;Solve any problem in your business or job with powerful Machine Learning models * &bull;&nbsp;&nbsp;Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/the-complete-machine-learning-course-with-python</p>
        <h2>About the Author</h2><h3>Anthony Ng</h3><p>Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Does the Course Cover?</p><p style='margin: 0'>Introduction: What Does the Course Cover?</p></td></tr></tbody></table><br><h4>2. Getting Started with Anaconda</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Windows OS] Downloading & Installing Anaconda</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Downloading &amp; Installing Anaconda</p></td></tr><tr><td style='padding: 15px;'><p>2. [Windows OS] Managing Environment</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Managing Environment</p></td></tr><tr><td style='padding: 15px;'><p>3. Navigating the Spyder & Jupyter Notebook Interface</p><p style='margin: 0'>Getting Started with Anaconda: Navigating the Spyder &amp; Jupyter Notebook Interface</p></td></tr><tr><td style='padding: 15px;'><p>4. Downloading the IRIS Datasets</p><p style='margin: 0'>Getting Started with Anaconda: Downloading the IRIS Datasets</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Exploration and Analysis</p><p style='margin: 0'>Getting Started with Anaconda: Data Exploration and Analysis</p></td></tr><tr><td style='padding: 15px;'><p>6. Presenting Your Data</p><p style='margin: 0'>Getting Started with Anaconda: Presenting Your Data</p></td></tr></tbody></table><br><h4>3. Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Regression: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Categories of Machine Learning</p><p style='margin: 0'>Regression: Categories of Machine Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Scikit-Learn</p><p style='margin: 0'>Regression: Working with Scikit-Learn</p></td></tr><tr><td style='padding: 15px;'><p>4. Boston Housing Data - EDA</p><p style='margin: 0'>Regression: Boston Housing Data - EDA</p></td></tr><tr><td style='padding: 15px;'><p>5. Correlation Analysis and Feature Selection</p><p style='margin: 0'>Regression: Correlation Analysis and Feature Selection</p></td></tr><tr><td style='padding: 15px;'><p>6. Simple Linear Regression Modelling with Boston Housing Data</p><p style='margin: 0'>Regression: Simple Linear Regression Modelling with Boston Housing Data</p></td></tr><tr><td style='padding: 15px;'><p>7. Robust Regression</p><p style='margin: 0'>Regression: Robust Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Evaluate Model Performance</p><p style='margin: 0'>Regression: Evaluate Model Performance</p></td></tr><tr><td style='padding: 15px;'><p>9. Multiple Regression with statsmodel</p><p style='margin: 0'>Regression: Multiple Regression with statsmodel</p></td></tr><tr><td style='padding: 15px;'><p>10. Multiple Regression and Feature Importance</p><p style='margin: 0'>Regression: Multiple Regression and Feature Importance</p></td></tr><tr><td style='padding: 15px;'><p>11. Ordinary Least Square Regression and Gradient Descent</p><p style='margin: 0'>Regression: Ordinary Least Square Regression and Gradient Descent</p></td></tr><tr><td style='padding: 15px;'><p>12. Regularised Method for Regression</p><p style='margin: 0'>Regression: Regularised Method for Regression</p></td></tr><tr><td style='padding: 15px;'><p>13. Polynomial Regression</p><p style='margin: 0'>Regression: Polynomial Regression</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Non-linear relationships</p><p style='margin: 0'>Regression: Dealing with Non-linear relationships</p></td></tr><tr><td style='padding: 15px;'><p>15. Feature Importance Revisited</p><p style='margin: 0'>Regression: Feature Importance Revisited</p></td></tr><tr><td style='padding: 15px;'><p>16. Data Pre-Processing 1</p><p style='margin: 0'>Regression: Data Pre-Processing 1</p></td></tr><tr><td style='padding: 15px;'><p>17. Data Pre-Processing 2</p><p style='margin: 0'>Regression: Data Pre-Processing 2</p></td></tr><tr><td style='padding: 15px;'><p>18. Variance Bias Trade Off - Validation Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Validation Curve</p></td></tr><tr><td style='padding: 15px;'><p>19. Variance Bias Trade Off - Learning Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Learning Curve</p></td></tr><tr><td style='padding: 15px;'><p>20. Cross Validation</p><p style='margin: 0'>Regression: Cross Validation</p></td></tr></tbody></table><br><h4>4. Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Classification: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression 1</p><p style='margin: 0'>Classification: Logistic Regression 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Logistic Regression 2</p><p style='margin: 0'>Classification: Logistic Regression 2</p></td></tr><tr><td style='padding: 15px;'><p>4. MNIST Project 1 - Introduction</p><p style='margin: 0'>Classification: MNIST Project 1 - Introduction</p></td></tr><tr><td style='padding: 15px;'><p>5. MNIST Project 2 - SGDClassifiers</p><p style='margin: 0'>Classification: MNIST Project 2 - SGDClassifier</p></td></tr><tr><td style='padding: 15px;'><p>6. MNIST Project 3 - Performance Measures</p><p style='margin: 0'>Classification: MNIST Project 3 - Performance Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p><p style='margin: 0'>Classification: MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p></td></tr><tr><td style='padding: 15px;'><p>8. MNIST Project 5 - Precision and Recall Tradeoff</p><p style='margin: 0'>Classification: MNIST Project 5 - Precision and Recall Tradeoff</p></td></tr><tr><td style='padding: 15px;'><p>9. MNIST Project 6 - The ROC Curve</p><p style='margin: 0'>Classification: MNIST Project 6 - The ROC Curve</p></td></tr></tbody></table><br><h4>5. Support Vector Machine (SVM)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Support Vector Machine (SVM): Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Support Vector Machine (SVM) Concepts</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Machine (SVM) Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear SVM Classification</p><p style='margin: 0'>Support Vector Machine (SVM): Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>4. Polynomial Kernel</p><p style='margin: 0'>Support Vector Machine (SVM): Polynomial Kernel</p></td></tr><tr><td style='padding: 15px;'><p>5. Gaussian Radial Basis Function</p><p style='margin: 0'>Support Vector Machine (SVM): Gaussian Radial Basis Function</p></td></tr><tr><td style='padding: 15px;'><p>6. Support Vector Regression</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Advantages and Disadvantages of SVM</p><p style='margin: 0'>Support Vector Machine (SVM): Advantages and Disadvantages of SVM</p></td></tr></tbody></table><br><h4>6. Tree</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Tree: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Decision Tree</p><p style='margin: 0'>Tree: What is Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>3. Training a Decision Tree</p><p style='margin: 0'>Tree: Training a Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>4. Visualising a Decision Trees</p><p style='margin: 0'>Tree: Visualising a Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>5. Decision Tree Learning Algorithm</p><p style='margin: 0'>Tree: Decision Tree Learning Algorithm</p></td></tr><tr><td style='padding: 15px;'><p>6. Decision Tree Regression</p><p style='margin: 0'>Tree: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Overfitting and Grid Search</p><p style='margin: 0'>Tree: Overfitting and Grid Search</p></td></tr><tr><td style='padding: 15px;'><p>8. Where to From Here</p><p style='margin: 0'>Tree: Where to From Here</p></td></tr><tr><td style='padding: 15px;'><p>9. Project HR - Loading and preprocesing data</p><p style='margin: 0'>Tree: Project HR - Loading and preprocesing data</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Modelling</p><p style='margin: 0'>Tree: Project HR - Modelling</p></td></tr></tbody></table><br><h4>7. Ensemble Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Ensemble Learning Methods Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble Learning Methods Introduction</p></td></tr><tr><td style='padding: 15px;'><p>3. Bagging Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Bagging Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Random Forests</p><p style='margin: 0'>Ensemble Machine Learning: Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>6. Extra-Trees</p><p style='margin: 0'>Ensemble Machine Learning: Extra-Trees</p></td></tr><tr><td style='padding: 15px;'><p>7. AdaBoost</p><p style='margin: 0'>Ensemble Machine Learning: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Boosting Machine</p><p style='margin: 0'>Ensemble Machine Learning: Gradient Boosting Machine</p></td></tr><tr><td style='padding: 15px;'><p>9. XGBoost</p><p style='margin: 0'>Ensemble Machine Learning: XGBoost</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Human Resources Analytics</p><p style='margin: 0'>Ensemble Machine Learning: Project HR - Human Resources Analytics</p></td></tr><tr><td style='padding: 15px;'><p>11. Ensemble of ensembles Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 1</p></td></tr><tr><td style='padding: 15px;'><p>12. Ensemble of ensembles Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 2</p></td></tr></tbody></table><br><h4>8. k-Nearest Neighbours (kNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. kNN Introduction</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. kNN Concepts</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. kNN and Iris Dataset Demo</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN and Iris Dataset Demo</p></td></tr><tr><td style='padding: 15px;'><p>4. Distance Metric</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Distance Metric</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Cancer Detection Part 1</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Cancer Detection Part 2</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 2</p></td></tr></tbody></table><br><h4>9. Dimensionality Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Dimensionality Reduction: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Dimensionality Reduction Concept</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Concept</p></td></tr><tr><td style='padding: 15px;'><p>3. PCA Introduction</p><p style='margin: 0'>Dimensionality Reduction: PCA Introduction</p></td></tr><tr><td style='padding: 15px;'><p>4. Dimensionality Reduction Demo</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Demo</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Wine 1: Dimensionality Reduction with PCA</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 1: Dimensionality Reduction with PCA</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Wine 2: Choosing the Number of Components</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 2: Choosing the Number of Components</p></td></tr><tr><td style='padding: 15px;'><p>7. Kernel PCA</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA</p></td></tr><tr><td style='padding: 15px;'><p>8. Kernel PCA Demo</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA Demo</p></td></tr><tr><td style='padding: 15px;'><p>9. LDA & Comparison between LDA and PCA</p><p style='margin: 0'>Dimensionality Reduction: LDA &amp; Comparison between LDA and PCA</p></td></tr></tbody></table><br><h4>10. Unsupervised Learning: Clustering</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Unsupervised Learning: Clustering: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Clustering Concepts</p><p style='margin: 0'>Unsupervised Learning: Clustering: Clustering Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. MLextend</p><p style='margin: 0'>Unsupervised Learning: Clustering: MLextend</p></td></tr><tr><td style='padding: 15px;'><p>4. Ward's Agglomerative Hierarchical Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: Ward's Agglomerative Hierarchical Clustering</p></td></tr><tr><td style='padding: 15px;'><p>5. Truncating Dendrogram</p><p style='margin: 0'>Unsupervised Learning: Clustering: Truncating Dendrogram</p></td></tr><tr><td style='padding: 15px;'><p>6. k-Means Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: k-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>7. Elbow Method</p><p style='margin: 0'>Unsupervised Learning: Clustering: Elbow Method</p></td></tr><tr><td style='padding: 15px;'><p>8. Silhouette Analysis</p><p style='margin: 0'>Unsupervised Learning: Clustering: Silhouette Analysis</p></td></tr><tr><td style='padding: 15px;'><p>9. Mean Shift</p><p style='margin: 0'>Unsupervised Learning: Clustering: Mean Shift</p></td></tr></tbody></table>

TensorFlow

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!

The Complete Machine Learning Course with Python

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					<div><p>This best selling Machine Learning - Use in Flutter has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning - Use in Flutter is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Machine Learning - Use in Flutter is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. The Machine Learning - Use in Flutter is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the Machine Learning - Use in Flutter, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Machine Learning - Use in Flutter will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the Machine Learning - Use in Flutter to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Machine Learning - Use in Flutter

<strong>This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.</strong>
        <p>Instead of using hard-coded rules for performing something, artificial intelligence (AI) and machine learning (ML) prepare the machines to learn things from data to decipher the complex patterns automatically and then use it for building multiple use cases. If you too want to make applications and solve complex problems using AI and ML, then this course will help you to get started with the basics.

The course starts by highlighting the learning objectives and explains the rise of AI in today's world. Next, you will be introduced to the concepts of ML. You will learn to understand ML terminologies and learn how to train a model. Towards the end, you will become familiar with the classification of ML systems, including the topics of supervised and unsupervised learning, regression, clustering, and reinforcement.

By the end of this course, you will have a fundamental knowledge of AI and ML.

All the resource for this course are available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>What You Will Learn</h2><p>Find out why machine learning (ML) is called a black box<br>Get to grips with the features and labels of ML<br>Uncover techniques for training ML models<br>Find out the differences between supervised and unsupervised learning<br>Understand regression, clustering, and dimension reduction</p>
        <h2>Audience</h2><p>If you are a developer, product or project manager, engineer, or a data science enthusiast, who is looking to learn the fundamentals of artificial intelligence (AI) and machine learning (ML), this course is for you. There is no prerequisite to start this course as it is specially designed for absolute beginners.</p>
        <h2>Approach</h2><p>With the help of simple narration, interesting examples, and practical tips, this course helps you to get started with artificial intelligence (AI) and machine learning (ML).</p>
        <h2>Key Features</h2><p>Learn the basics of artificial intelligence (AI), machine learning (ML), and deep learning * Find out how ML helps in training a model * Discover how ML systems are classified</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

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        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.

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