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Discover the potential of ChatGPT and OpenAI API for language tasks such as summarization, translation, chat completion, and more. Explore the nuances of the OpenAI API, unravel its architecture, and master text generation capabilities. Whether you are a beginner or an expert, this course empowers you to apply these tools in the real world.

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

Computer vision (CV), a subfield of computer science, focuses on replicating the complex functionalities of the human visual system. This course provides a comprehensive understanding of Computer Vision from the beginning using Python and helps you in becoming an expert.

This Microsoft Power BI course will help you become a Power BI expert. It'll enhance your skills by offering you comprehensive knowledge on Power BI Desktop and Power BI Online, and unleash the capabilities of Power BI to perform a comprehensive analysis of data from multiple sources and present the data using the perfect visualization.

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The course 'Deep Learning & Neural Networks Python - Keras' provides a comprehensive introduction to deep learning using the Keras library in Python. It covers topics ranging from basic neural networks to more advanced concepts, such as convolutional neural networks, image augmentation, and performance improvement techniques for various datasets. Learning Outcomes: Understand the fundamental concepts of deep learning and how it differs from traditional machine learning. Gain proficiency in using Keras, a powerful deep learning library, for building and training neural network models. Develop practical skills in creating and optimizing neural network models for different datasets, including image recognition tasks and regression problems. Why buy this Deep Learning & Neural Networks Python - Keras? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Deep Learning & Neural Networks Python - Keras there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Deep Learning & Neural Networks Python - Keras course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Deep Learning & Neural Networks Python - Keras does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Deep Learning & Neural Networks Python - Keras was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Deep Learning & Neural Networks Python - Keras is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Introduction and Table of Contents Course Introduction and Table of Contents 00:11:00 Deep Learning Overview Deep Learning Overview - Theory Session - Part 1 00:06:00 Deep Learning Overview - Theory Session - Part 2 00:07:00 Choosing Between ML or DL for the next AI project - Quick Theory Session Choosing Between ML or DL for the next AI project - Quick Theory Session 00:09:00 Preparing Your Computer Preparing Your Computer - Part 1 00:07:00 Preparing Your Computer - Part 2 00:06:00 Python Basics Python Basics - Assignment 00:09:00 Python Basics - Flow Control 00:09:00 Python Basics - Functions 00:04:00 Python Basics - Data Structures 00:12:00 Theano Library Installation and Sample Program to Test Theano Library Installation and Sample Program to Test 00:11:00 TensorFlow library Installation and Sample Program to Test TensorFlow library Installation and Sample Program to Test 00:09:00 Keras Installation and Switching Theano and TensorFlow Backends Keras Installation and Switching Theano and TensorFlow Backends 00:10:00 Explaining Multi-Layer Perceptron Concepts Explaining Multi-Layer Perceptron Concepts 00:03:00 Explaining Neural Networks Steps and Terminology Explaining Neural Networks Steps and Terminology 00:10:00 First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset 00:07:00 Explaining Training and Evaluation Concepts Explaining Training and Evaluation Concepts 00:11:00 Pima Indian Model - Steps Explained Pima Indian Model - Steps Explained - Part 1 00:09:00 Pima Indian Model - Steps Explained - Part 2 00:07:00 Coding the Pima Indian Model Coding the Pima Indian Model - Part 1 00:11:00 Coding the Pima Indian Model - Part 2 00:09:00 Pima Indian Model - Performance Evaluation Pima Indian Model - Performance Evaluation - Automatic Verification 00:06:00 Pima Indian Model - Performance Evaluation - Manual Verification 00:08:00 Pima Indian Model - Performance Evaluation - k-fold Validation - Keras Pima Indian Model - Performance Evaluation - k-fold Validation - Keras 00:10:00 Pima Indian Model - Performance Evaluation - Hyper Parameters Pima Indian Model - Performance Evaluation - Hyper Parameters 00:12:00 Understanding Iris Flower Multi-Class Dataset Understanding Iris Flower Multi-Class Dataset 00:08:00 Developing the Iris Flower Multi-Class Model Developing the Iris Flower Multi-Class Model - Part 1 00:09:00 Developing the Iris Flower Multi-Class Model - Part 2 00:06:00 Developing the Iris Flower Multi-Class Model - Part 3 00:09:00 Understanding the Sonar Returns Dataset Understanding the Sonar Returns Dataset 00:07:00 Developing the Sonar Returns Model Developing the Sonar Returns Model 00:10:00 Sonar Performance Improvement - Data Preparation - Standardization Sonar Performance Improvement - Data Preparation - Standardization 00:15:00 Sonar Performance Improvement - Layer Tuning for Smaller Network Sonar Performance Improvement - Layer Tuning for Smaller Network 00:07:00 Sonar Performance Improvement - Layer Tuning for Larger Network Sonar Performance Improvement - Layer Tuning for Larger Network 00:06:00 Understanding the Boston Housing Regression Dataset Understanding the Boston Housing Regression Dataset 00:07:00 Developing the Boston Housing Baseline Model Developing the Boston Housing Baseline Model 00:08:00 Boston Performance Improvement by Standardization Boston Performance Improvement by Standardization 00:07:00 Boston Performance Improvement by Deeper Network Tuning Boston Performance Improvement by Deeper Network Tuning 00:05:00 Boston Performance Improvement by Wider Network Tuning Boston Performance Improvement by Wider Network Tuning 00:04:00 Save & Load the Trained Model as JSON File (Pima Indian Dataset) Save & Load the Trained Model as JSON File (Pima Indian Dataset) - Part 1 00:09:00 Save & Load the Trained Model as JSON File (Pima Indian Dataset) - Part 2 00:08:00 Save and Load Model as YAML File - Pima Indian Dataset Save and Load Model as YAML File - Pima Indian Dataset 00:05:00 Load and Predict using the Pima Indian Diabetes Model Load and Predict using the Pima Indian Diabetes Model 00:09:00 Load and Predict using the Iris Flower Multi-Class Model Load and Predict using the Iris Flower Multi-Class Model 00:08:00 Load and Predict using the Sonar Returns Model Load and Predict using the Sonar Returns Model 00:10:00 Load and Predict using the Boston Housing Regression Model Load and Predict using the Boston Housing Regression Model 00:08:00 An Introduction to Checkpointing An Introduction to Checkpointing 00:06:00 Checkpoint Neural Network Model Improvements Checkpoint Neural Network Model Improvements 00:10:00 Checkpoint Neural Network Best Model Checkpoint Neural Network Best Model 00:04:00 Loading the Saved Checkpoint Loading the Saved Checkpoint 00:05:00 Plotting Model Behavior History Plotting Model Behavior History - Introduction 00:06:00 Plotting Model Behavior History - Coding 00:08:00 Dropout Regularization - Visible Layer Dropout Regularization - Visible Layer - Part 1 00:11:00 Dropout Regularization - Visible Layer - Part 2 00:06:00 Dropout Regularization - Hidden Layer Dropout Regularization - Hidden Layer 00:06:00 Learning Rate Schedule using Ionosphere Dataset - Intro Learning Rate Schedule using Ionosphere Dataset 00:06:00 Time Based Learning Rate Schedule Time Based Learning Rate Schedule - Part 1 00:07:00 Time Based Learning Rate Schedule - Part 2 00:12:00 Drop Based Learning Rate Schedule Drop Based Learning Rate Schedule - Part 1 00:07:00 Drop Based Learning Rate Schedule - Part 2 00:08:00 Convolutional Neural Networks - Introduction Convolutional Neural Networks - Part 1 00:11:00 Convolutional Neural Networks - Part 2 00:06:00 MNIST Handwritten Digit Recognition Dataset Introduction to MNIST Handwritten Digit Recognition Dataset 00:06:00 Downloading and Testing MNIST Handwritten Digit Recognition Dataset 00:10:00 MNIST Multi-Layer Perceptron Model Development MNIST Multi-Layer Perceptron Model Development - Part 1 00:11:00 MNIST Multi-Layer Perceptron Model Development - Part 2 00:06:00 Convolutional Neural Network Model using MNIST Convolutional Neural Network Model using MNIST - Part 1 00:13:00 Convolutional Neural Network Model using MNIST - Part 2 00:12:00 Large CNN using MNIST Large CNN using MNIST 00:09:00 Load and Predict using the MNIST CNN Model Load and Predict using the MNIST CNN Model 00:14:00 Introduction to Image Augmentation using Keras Introduction to Image Augmentation using Keras 00:11:00 Augmentation using Sample Wise Standardization Augmentation using Sample Wise Standardization 00:10:00 Augmentation using Feature Wise Standardization & ZCA Whitening Augmentation using Feature Wise Standardization & ZCA Whitening 00:04:00 Augmentation using Rotation and Flipping Augmentation using Rotation and Flipping 00:04:00 Saving Augmentation Saving Augmentation 00:05:00 CIFAR-10 Object Recognition Dataset - Understanding and Loading CIFAR-10 Object Recognition Dataset - Understanding and Loading 00:12:00 Simple CNN using CIFAR-10 Dataset Simple CNN using CIFAR-10 Dataset - Part 1 00:09:00 Simple CNN using CIFAR-10 Dataset - Part 2 00:06:00 Simple CNN using CIFAR-10 Dataset - Part 3 00:08:00 Train and Save CIFAR-10 Model Train and Save CIFAR-10 Model 00:08:00 Load and Predict using CIFAR-10 CNN Model Load and Predict using CIFAR-10 CNN Model 00:16:00 RECOMENDED READINGS Recomended Readings 00:00:00

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Overview Uplift Your Career & Skill Up to Your Dream Job - Learning Simplified From Home! Kickstart your career & boost your employability by helping you discover your skills, talents and interests with our special Microsoft Power BI Masterclass 2021 Course. You'll create a pathway to your ideal job as this course is designed to uplift your career in the relevant industry. It provides professional training that employers are looking for in today's workplaces. The Microsoft Power BI Masterclass 2021 Course is one of the most prestigious training offered at StudyHub and is highly valued by employers for good reason. This Microsoft Power BI Masterclass 2021 Course has been designed by industry experts to provide our learners with the best learning experience possible to increase their understanding of their chosen field. This Microsoft Power BI Masterclass 2021 Course, like every one of Study Hub's courses, is meticulously developed and well researched. Every one of the topics is divided into elementary modules, allowing our students to grasp each lesson quickly. At StudyHub, we don't just offer courses; we also provide a valuable teaching process. When you buy a course from StudyHub, you get unlimited Lifetime access with 24/7 dedicated tutor support. Why buy this Microsoft Power BI Masterclass 2021? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Microsoft Power BI Masterclass 2021 there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Microsoft Power BI Masterclass 2021 course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill.   Prerequisites This Microsoft Power BI Masterclass 2021 does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Microsoft Power BI Masterclass 2021 was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Microsoft Power BI Masterclass 2021 is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Section 01: Introduction Welcome! 00:01:00 What is Power BI? 00:03:00 Download & Installing Power BI Desktop 00:04:00 Getting to know the interface 00:03:00 Mini Project: Transform Data 00:07:00 Mini Project: Visualize Data 00:05:00 Mini Project: Creating a Data Model 00:07:00 Course Outline: What will you learn in this course? 00:05:00 How to learn best with this course? 00:03:00 Section 02: Preparing our Project Creating our initial project file 00:04:00 Working with the attached project files 00:04:00 Section 03: Data Transformation - The Query Editor Exploring the Query Editor 00:06:00 Connecting to our data source 00:07:00 Editing rows 00:08:00 Changing data types 00:08:00 Replacing values 00:03:00 Close & Apply 00:03:00 Connecting to a csv file 00:03:00 Connecting to a web page 00:05:00 Extracting characters 00:06:00 Splitting & merging columns 00:09:00 Creating conditional columns 00:06:00 Creating columns from examples 00:09:00 Merging Queries 00:17:00 Pivoting & Unpivoting 00:06:00 Appending Queries 00:08:00 Practice & Solution: Population table 00:15:00 The Fact-Dimension-Model 00:09:00 Practice: Load the dimension table 00:04:00 Organizing our queries in groups 00:03:00 Entering data manually 00:05:00 Creating an index column 00:03:00 Workflow & more transformations 00:05:00 Module summary 00:05:00 Exercise 1 - Instruction 00:02:00 Exercise Solution 00:11:00 Section 04: Data Transformation - Advanced Advanced Editor - Best practices 00:09:00 Performance: References vs. Duplicating 00:10:00 Performance: Enable / Disable Load & Report Refresh 00:05:00 Group by 00:05:00 Mathematical Operations 00:05:00 Run R Script 00:15:00 Using Parameters to dynamically transform data 00:06:00 M formula language: Basics 00:07:00 M formula language: Values, Lists & Tables 00:14:00 M formula language: Functions 00:13:00 M formula language: More functions & steps 00:05:00 Exercise 2 - Instructions 00:01:00 Exercise 2 - solution 00:05:00 Section 05: Creating a Data Model Understanding the relationship 00:05:00 Create & edit relationships 00:06:00 One-to-many & one-to-one relationship 00:06:00 Many-to-many (m:n) relationship 00:08:00 Cross filter direction 00:06:00 Activate & deactivate relationships 00:06:00 Model summary 00:03:00 Exercise 3 Create Model 00:02:00 Exercise 3 Solution 00:02:00 Section 06: Data Visualization Our first visual 00:08:00 The format tab 00:12:00 Understanding tables 00:10:00 Conditional formatting 00:09:00 The Pie Chart 00:06:00 All about the filter visual 00:13:00 The filter pane for developers 00:09:00 Cross filtering & edit interactions 00:04:00 Syncing slicers across pages 00:07:00 Creating drill downs 00:08:00 Creating drill throughs 00:07:00 The tree map visual 00:07:00 The decomposition tree 00:05:00 Understanding the matrix visual 00:05:00 Editing pages 00:07:00 Buttons & Actions 00:09:00 Bookmarks to customize your report 00:10:00 Analytics and Forecasts with line charts 00:10:00 Working with custom visuals 00:07:00 Get data using R Script & R Script visual 00:08:00 Asking questions - Q&A visual 00:04:00 Wrap up - data visualization 00:08:00 Section 07: Power BI & Python Python in Power BI - Plan of attack 00:03:00 Setting up Python for Power BI 00:03:00 Transforming data using Python 00:11:00 Creating visualizations using Python 00:08:00 Violin plots, pair plots & ridge plots using Python 00:15:00 Machine learning (BayesTextAnalyzer) using Python 00:00:00 Performance & Troubleshooting 00:03:00 Section 08: Storytelling with Data Introduction 00:01:00 Show Empathy & Identify the Requirement 00:03:00 Finding the Most Suitable KPI's 00:02:00 Choose an Effective Visualization 00:04:00 Make Use of Natural Reading Pattern 00:03:00 Tell a Story Using Visual Cues 00:05:00 Avoid Chaos & Group Information 00:02:00 Warp Up - Storytelling with Data 00:02:00 Section 09: DAX - The Essentials Introduction 00:03:00 The project data 00:04:00 Measures vs. Calculated Columns 00:15:00 Automatically creating a date table in DAX 00:08:00 CALENDAR 00:05:00 Creating a complete date table with features 00:04:00 Creating key measure table 00:03:00 Aggregation functions 00:06:00 The different versions of COUNT 00:14:00 SUMX - Row based calculations 00:09:00 Section 10: DAX - The CALCULATE function CALCULATE - The basics 00:11:00 Changing the context with FILTER 00:07:00 ALL 00:08:00 ALL SELECTED 00:03:00 ALL EXCEPT 00:07:00 Section 11: Power BI Service - Power BI Cloud How to go on now? 00:03:00 Power BI Pro vs Premium & Signing up 00:04:00 Exploring the interface 00:04:00 Discovering your workspace 00:03:00 Connecting Power BI Desktop & Cloud 00:04:00 Understanding datasets & reports 00:03:00 Working on reports 00:04:00 Updating reports from Power BI Desktop 00:04:00 Creating and working with workspaces 00:07:00 Installing & using a data gateway 00:13:00 Get Quick Insights 00:03:00 Creating dashboards 00:04:00 Sharing our results through Apps 00:10:00 Power BI Mobile App 00:05:00 Creating the layout for the Mobile App 00:04:00 Wrap up - Power BI Cloud 00:07:00 Section 12: Row-Level Security Introduction 00:03:00 Creating a Row-Level Security 00:05:00 Row-Level Security in the Cloud 00:04:00 Row-Level Security & Data Model 00:05:00 Dynamic Row-Level Security 00:07:00 Dynamic Many-to-Many RLS 00:04:00 Hierarchical Row-Level Security 00:13:00 Section 13: More data sources JSON & REST API 00:10:00 Setting up a local MySQL database 00:14:00 Connecting to a MySQL database in Power BI 00:05:00 Connecting to a SQL database (PostgreSQL) 00:05:00 Section 14: Next steps to improve & stay up to date Congratulations & next steps 00:06:00 The End 00:01:00 Resources Resources - Microsoft Power BI Masterclass 2021 00:00:00 Assignment Assignment - Microsoft Power BI 00:00:00