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Overview Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career. This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today! 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? This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush. Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst And much more! Course Curriculum 90 sections • 90 lectures • 10:19:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:04:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:06:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

48-Hour Knowledge Knockdown! Prices Reduced Like Never Before! Are you looking to improve your current abilities or make a career move? Our unique Kitchen Supervisor Course might help you get there! Expand your expertise with high-quality training - study the course and get an expertly designed, great value training experience. Learn from industry professionals and quickly equip yourself with the specific knowledge and skills you need to excel in your chosen career through the online training course. This online training course is accredited by CPD with 10 CPD points for professional development. Students can expect to complete this training course in around 8 hours. You'll also get dedicated expert assistance from us to answer any queries you may have while studying our course. The Kitchen Supervisor course is broken down into several in-depth modules to provide you with the most convenient and rich learning experience possible. Upon successful completion of the course, you will receive an instant e-certificate as proof of the comprehensive skill development and competency. Add these amazing new skills to your resume and boost your employability by simply enroling on this course. With this comprehensive course, you can achieve your dreams and train for your ideal career. The Kitchen Supervisor course provides students with an in-depth learning experience that they can work through at their own pace to enhance their professional development. You'll get a Free Student ID card by enroling in this course. This ID card entitles you to discounts on bus tickets, movie tickets, and library cards. Enroling on the course will ensure that you land your dream career faster than you thought possible. Stand out from the crowd and train for the job you want with the program. Learning Outcomes of Kitchen Supervisor: Instant access to verified and reliable information Participation in inventive and interactive training exercises Quick assessment and guidance for all subjects CPD accreditation for proof of acquired skills and knowledge Freedom to study in any location and at a pace that suits you Expert support from dedicated tutors committed to online learning Experts created the course to provide a rich and in-depth training experience for all students who enrol in it. Enrol in the course right now and you'll have immediate access to all of the course materials. Then, from any internet-enabled device, access the course materials and learn when it's convenient for you. Start your learning journey straight away with this course and take a step toward a brighter future! Why Prefer this Kitchen Supervisor Course? Opportunity to earn a certificate accredited by CPDQS after completing this course Student ID card with amazing discounts - completely for FREE! (£10 postal charge will be applicable for international delivery) Standard-aligned lesson planning Innovative and engaging content and activities Assessments that measure higher-level thinking and skills Complete the program in your own time, at your own pace Each of our students gets full 24/7 tutor support ****Course Curriculum of Kitchen Supervisor Essential Skills**** Here is the curriculum breakdown of the Kitchen Supervisor course: Module 01: Understanding the Role of Supervision Defining Supervision Types of Supervision Roles of a Supervisor Challenges Supervisors Face Successful Supervisors Do Things Differently Summary Module 02: Essential Skills in Supervision Problem-Solving and Decision Making Planning Delegation Basics of Internal Communications Meeting Management Summary Module 03: Employee Management Goal Settings Motivation Observation and Feedback Addressing Performance Issues Conducting Performance Reviews Summary Module 04: Conflict Management Why Conflicts Happen Managing Conflict Based on Personality Types Managing Conflict Based on Perception Managing Conflict Based on Goals Handling Common Conflict Situations Summary Module 05: Supervision of Food Safety What is a Supervisor? Essential Supervisor Skills Communication and Motivation Suggested Responsibilities of a Supervisor The Role of the Supervisor What Sort of Supervisory Styles Count? Failures of Management and Supervisors Food Safety Cultures Standards Food Safety Policies Quality Assurance Quality Control Summary Module 06: Food Safety Management System What is a Food Safety Management System (FSMS)? Who needs an FSMS?What should an FSMS include?How often should the FSMS be reviewed?Who needs to see the FSMS? What is Hazard Analysis Critical Control Point (HACCP)? Advantages of HACCPSeven Principles of HACCPPrerequisite Programmes for HACCP The Implementation of HACCP (12 logical steps) Description of the each 12 steps Food Safety Management System Based on HACCP Principles Summary Assessment Module 07: Food Safety Management Tools Safer Food, Better Business (SFBB) Opening and Closing Checks Food Safety Management for Retailers Documenting the Food Safety System Why is a written Food Safety Management System required? Summary Assessment Module 08: Contamination Hazards Cross-Contamination Where Can Contamination Occur? The Sources of Contamination Types of Contamination Hazards Microbiological HazardsPhysical HazardsChemical HazardsAllergenic Hazards Vehicles of Contamination Detecting Contaminants Summary Assessment Module 09: Controlling Contamination The Control of Different Contamination Hazards Microbiological HazardsPhysical HazardsChemical HazardsAllergenic Hazards Deliveries Food Storage Stock Control Recording and Labelling Use by Dates The role of the Supervisor in Preventing Contamination Summary Assessment Module 10: Personal Hygiene Food safety Legislation and High Standards of Personal Hygiene Hazards from Food Handlers HandsThe nose, mouth and earsCuts, boils, septic spots and skin infectionsThe hairJewellery and perfumeSmokingEmployee sickness Protective Clothing Exclusion of Food Handlers Visitors The Role of Supervisor in Personal Hygiene Summary Assessment Module 11: The Food Hygiene Rating Scheme What is The Food Hygiene Rating Scheme? Why is Food Hygiene Rating Important? What the Food Hygiene Rating Covers The Rating Scale Understanding the scoring system Is It a Legal Requirement to Display the Food Hygiene Rating? What does an Environmental Health Officer Look for? The FSA Inspection What happens if you are prosecuted? Due Diligence Can you appeal? Finding a Rating Differences Between Online Ratings and the Rating Sticker That is Displayed Frequency of Inspections How Do You Achieve a 5 star food hygiene rating? Summary Assessment Module 12: Food Preparation and Processing for Food Manufacturing Controlling the temperature in order to control the bacteria Cooking Food Hot Holding Food Cooling Hot Food Checking Temperatures When things go wrong Summary Assessment Module 13: Cleaning The Purpose of Cleaning Safe Cleaning Precautions Detergents and Disinfectants Cleaning and disinfection Cleaning schedules Safe cleaning Six stages of cleaning Cleaning food storage areas and chillers Dishwashers Pests and controlling pests Summary Assessment Module 14: Waste Disposal, Cleaning and Disinfection Waste Storage The Removal of Waste The Benefits of Cleaning Energy in Cleaning Thermal energy Detergents Cleaning Equipment Mechanical Equipment Disinfection Procedures and Methods of Cleaning Manual Equipment and Utensil Washing Cleaning a Cooked Meat Slicing Machine Clean in Place (CIP) Cleaning Schedules The Role of Supervisor in Cleaning In-house and Contract Cleaning Summary Assessment Module 15: Pest Management Pests Looking for Evidence of Pests Contamination Caused by Pests Pets Pest Control Good Housekeeping Prevention The Use of Pest Control Contractor A Due-Diligence Defence The Role of Supervisor in Pest Control Summary Assessment Assessment Process Once you have completed all the modules in the course, your skills and knowledge will be tested with an automated multiple-choice assessment. You will then receive instant results to let you know if you have successfully passed the course. CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Is This Kitchen Supervisor Course Right for You? Anyone interested in learning more about this subject should take this Kitchen Supervisor course. This will help you grasp the basic concepts as well as develop a thorough understanding of the subject. All are welcome to take this course. There are no prerequisites for enrolment, and you can access the course materials from any location in the world. Requirements The Kitchen Supervisor program does not require any prior knowledge; everyone may participate! This Kitchen Supervisor course is open to anyone who is interested in learning from anywhere in the world. Every student must be over the age of 16 and have a passion for learning and literacy. This 100% online course can be accessed from any internet-connected device, such as a computer, tablet, or smartphone. This Kitchen Supervisor course allows you to study at your own speed and grow a quality skillset. Career path After completing this Kitchen Supervisor Course, you are to start your career or begin the next phase of your career in this field. Our entire Kitchen Supervisor course will help you to gain a position of respect and dignity over your competitors. The certificate enhances your CV and helps you find work in the field concerned. Certificates CPD Accredited Certificate Digital certificate - £10 CPD Accredited Certificate Hard copy certificate - £29 If you are an international student, then you have to pay an additional 10 GBP as an international delivery charge.

12 in 1 Career Guided Programme | Python, Data Science, ML, Tableau, SQL Programming, Data Mining, Business Analysis | 130 CPD Points | Tutor Support | Lifetime Access

Duration 5 Days 30 CPD hours This course is intended for This course is recommended for administrators and engineers. Overview What you'll learn: Understand the differences between Citrix Virtual Apps and Desktops 2203 LTSR on-premises and the Citrix DaaS. Install, configure, and manage Citrix Cloud Connectors. Deploy and manage Virtual Delivery Agent machines to on-premises resource locations as well as in Microsoft Azure using MCS. Integrate Citrix Cloud and Citrix Virtual Apps and Desktops 2203 LTSR with Microsoft Azure Active Directory. Provide remote access with Citrix StoreFront and Citrix Gateway on Microsoft Azure. In this course you will learn how to create a new Citrix DaaS deployment on Citrix Cloud, with a resource location on Microsoft Azure. You will also learn how to migrate to Citrix DaaS from an on-premises Citrix Virtual Apps and Desktops Site. Get hands-on as the course guides you through the architecture, communications, management, installation, and configuration of Citrix DaaS on Citrix Cloud and a Microsoft Azure resource location that will host apps and desktops for your users. This course is a necessary step in enabling you with the right training and skills, to not only understand, manage, and deliver successfully, but also to make well-informed planning decisions along the way. Module 1: Introduction to Citrix DaaS New Citrix Workspace Packaging Citrix Virtual Apps and Desktops - On-Premises Site What is Citrix Cloud? Why Citrix DaaS? What is a Migration from Citrix Virtual Apps and Desktops to Citrix DaaS? Citrix Cloud Administration Module 2: Planning - Citrix DaaS Architecture, Security, and Operations Architecture and Deployment Options Citrix DaaS Security Citrix DaaS Operations Module 3: Planning - Citrix Cloud Connectors Cloud Connector Architecture Cloud Connector Services and Communications Overview Cloud Connector Operations in a Resource Location Cloud Connector Resiliency Installing, Updating, and Removing Cloud Connectors Supported Domain Scenarios for Cloud Connectors Securing Cloud Connector Communications Local Host Cache (LHC) Citrix Cloud Connector vs Delivery Controller Operations Module 4: Planning - Citrix DaaS Resource Locations Citrix DaaS Resource Locations Citrix DaaS Hosting Connections Zones Module 5: Active Directory, Authentication, and Authorization Active Directory Design Options Desktops from Non-Domain Joined VDAs Citrix Federated Authentication Service and Identity Provider Services Module 6: Planning - Provisioning VDA Workloads and Delivering Resources Master Images Machine Creation Services (MCS) in Citrix DaaS Citrix Provisioning in Citrix DaaS Machine Catalogs Delivery Groups Citrix Cloud Library Module 7: Planning - Provide Access in Citrix Cloud Selecting Between Citrix digital workspace experience and StoreFront Citrix StoreFront and Citrix digital workspace experience Communications Selecting Between Citrix Gateway Service and On-Premises Citrix Gateway Access Layer Communications User Authentication Module 8: Planning - Citrix DaaS Administration Citrix Cloud Manage and Monitor Delegated Administration Citrix DaaS Remote PowerShell Software Development Kit Manage Multiple Resource Locations Module 9: Planning - Public Cloud Considerations General Public Cloud Considerations Using Autoscale to Power Manage Machines in a Public Cloud Microsoft Azure as a Citrix DaaS Resource Location Amazon Web Services as a Citrix DaaS Resource Location Google Cloud as a Citrix DaaS Resource Location Module 10: Planning - Migrating to Citrix DaaS from Citrix Virtual Apps and Desktops Citrix Cloud Migration Options and Considerations Citrix Automated Configuration Tool Citrix Image Portability Service Module 11: Manage - Operations and Support in Citrix Cloud Citrix Cloud Connector Support Updating and Rolling Back Machine Catalogs VDA Restore Citrix Self-Help Strategy Monitor Your Environment Module 12: Introduction to Citrix DaaS on Microsoft Azure Partnering for Success Module 13: Planning - Citrix DaaS Resource Location on Microsoft Azure Overview of Citrix DaaS Components Creating a Citrix DaaS Deployment Overview Module 14: Planning - Microsoft Azure Overview Azure Virtual Network Structure Azure Virtual Network Connectivity Azure Virtual Resources Azure Active Directory Identity and Access Management Azure Active Directory Options and Considerations Module 15: Planning - Deploying Citrix DaaS on Microsoft Azure Citrix DaaS Resource Locations in Azure Citrix DaaS Components in Azure Creating and Managing Workloads in an Azure Resource Location Module 16: Planning - Provide Access to End Users Providing Access to Resources in Citrix Cloud Citrix Gateway Deployment Options Deploying Citrix Gateway or ADC in Azure GSLB and StoreFront Optimal Gateway in Hybrid Environments Module 17: Rollout - Citrix DaaS Deployment on Microsoft Azure Citrix Workspace App Rollout Preparing Migration of End-Users to Workspace Platform Module 18: Managing - Citrix DaaS Workloads on Microsoft Azure Maintaining Citrix Gateway Backup and Monitoring in Azure Maintaining Master Images in Azure Monitoring VDAs in Manage Console and Azure Module 19: Optimize - Citrix DaaS on Microsoft Azure Managing Azure Costs Using Azure Pricing Calculator - Instructor Demo Additional course details: Nexus Humans CWS-252 Citrix DaaS Deployment and Administration on Microsoft Azure training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the CWS-252 Citrix DaaS Deployment and Administration on Microsoft Azure course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

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

Description  Whether you want to become a highly paid Android Developer or a Freelancer Android Developer and build top-notch Apps in no time flat, then this Android Developer's Portfolio Masterclass - Build real Apps course is what you need. This comprehensive course is designed to help you in building Android Developer's Portfolio, developer resume, and attract employers and amp. This course covers everything you need to know about building world-class apps you can add to your portfolio including the tools and techniques that required to become a Pro-Java Developer. You'll also learn how to grow your Android Development knowledge while building up your portfolio and the crucial Android Framework in a little time. What you'll learn Build Android Apps to include on your portfolio Master intermediate to advanced Android & Java Development Skills Build your Android Developer Resume and Reputation so you get that job! Master the tools that will make you and EXPERT Android & Java Developer Get all the tools and knowledge to become a true Android Developer Champion Requirements Have some Android Development Experience Have some Java Programming Experience (Java Refresher section provided) Not a complete Java and Android Beginner Who is the target audience? If you have a working Java and Android Development knowledge, this is for you. If you are a seasoned Java programmer and have done some Android Development then take this for you. If you are switching from C++ to Java then this is a fast-track way of doing it. You can get started straight away with the Intermediate Java Language section. If you are a pro developer and want to quickly get up to date with Android Development, then this course is for you. If you need some Java refresher, then you have come to the right place as I have included a bonus section on Java too. Who this course is for: Java and Android Student's who want to build their Android Developer's Portfolio Intermediate Android Developers who want to build their Android Developer Resume and get Jobs or start their freelancer careers Advanced Java and Android Developers who want to Build Android Apps that will wow potential employers and clients Android Developers who want to build their developer online presence Students who want to take their existent Android/Java Skills to the next level by building Amazing Android Apps Getting Started What you'll get from this Course? 00:03:00 How To Get Your Free Gifts? 00:05:00 About Bonus Sections 00:02:00 Build Your Portfolio App - Brand Yourself as Android Development What You'll Make by the End of This Section 1 00:01:00 Let's Build our Portfolio App - Setup User Interface - Part 1 00:14:00 Let's Build our Portfolio App - Setup User Interface - Part 2 00:10:00 Portfolio App - User Interface and Coordinator Widget 00:10:00 Let's Build our Portfolio App - TabLayout and Fragment 00:13:00 Let's Build our Portfolio App - Setup User Interface - Viewpager Adapter 00:15:00 Let's Build our Portfolio App - Final App 00:08:00 Build Your Portfolio - Motivational App What You'll Make by the End of This Section 2 00:01:00 Motivational App - Intro and UI Setup - Part 1 00:11:00 Motivational App - AppController Class 00:10:00 Motivational App - Pager Adapter 00:14:00 Motivational App - Quotes Fragment Setup 00:14:00 Motivational App - Show Quotes on Slide 00:20:00 Motivational App - Final - Cardview Colors 00:07:00 Build Your Portfolio - Build and Android Game What You'll Make by the End of This Section 3 00:01:00 Let's Build a Fun Game - Reflex Game - UI Setup 00:10:00 Let's Build a Fun Game - Reflex View - Setup - Part 1 00:11:00 Let's Build a Fun Game - Reflex View - Setup - Part 2 00:13:00 Let's Build a Fun Game - Reflex Game - Add Spot on Screen 00:13:00 Let's Build a Fun Game - Reflex Game - Show Spots and Tapping 00:15:00 Let's Build a Fun Game - Reflex Game - Adding Sound Efects 00:13:00 Let's Build a Fun Game - Reflex Game - More Setup 00:11:00 Let's Build a Fun Game - Reflex Game - Game Setup 00:11:00 Let's Build a Fun Game - Reflex Game -Final Game 00:17:00 Advanced Drawing on Screen Draw and Views in Android 00:05:00 Drawing Primitive Shapes on Screen - Circles and Lines 00:16:00 How to Read the Android Documentations 00:07:00 Gradients 00:07:00 Drawing Bitmaps on Screen 00:08:00 Custom TextViews 00:08:00 Build Your Portfolio - Build Pikasso - Doodlz App What You'll Make by the End of This Section 4 00:01:00 Pikasso App - Overview 00:14:00 Pikasso App - Setup - Part 1 00:06:00 Pikasso App - Setup - Motion Event Methods 00:15:00 Pikasso App - Drawing on Screen 00:15:00 Pikasso App - Setup - Creating Menus - Part 1 00:16:00 Pikasso App - Creating Menu - Part 2 00:06:00 Pikasso App - Setup Dialog for Width 00:11:00 Pikasso App - Setup - Color Seekbar - Part 1 00:12:00 Pikasso App - Setup SeekBar Dialog 00:16:00 Pikasso App - Setup - Finalize Color Seekbar Dialog 00:21:00 Pikasso App - Saving Images 00:18:00 Pikasso App - Final Product 00:05:00 Build Your Android Portfolio - Weather App What You'll Make by the End of This Section 5 00:01:00 Weather App - Setup User Interface 00:18:00 Weather App - adding a Background Image 00:08:00 Weather App - Setup Volley and JSON API 00:12:00 Weather App - Setup Model Class and ViewPager Fragment 00:17:00 Weather App - Creating the ForecastAdapter and Forecast Fragment 00:14:00 Weather App - Setup Forecast Fragment 00:13:00 Weather App - Setup Forecast Data Class and Download JSON Data 00:09:00 Weather App - Probing in JSON API Object 00:10:00 Weather App - Pulling Data and Async Callback Interface 00:18:00 Weather App - Showing data in ViewPager 00:10:00 Weather App - ViewPager Design and Rearranging Views 00:12:00 Weather App - Putting Together the Top CardView and Current Weather data 00:11:00 Weather App - Top Cardview Final Look 00:11:00 Weather App - Getting Location Input and Populate Screen 00:14:00 Weather App - Saving Locations - Shared Preferences 00:14:00 Weather App - Final Weather Forecast App 00:21:00 Build your Portfolio - Android Sensors What You'll Make by the End of This Section 6 00:01:00 Introduction to Sensor in Android Devices 00:04:00 Different types of Sensors 00:13:00 Getting Light Sensors 00:17:00 Ambient Temperature Sensor 00:11:00 Compass App - Part 1 00:12:00 Compass App - Final 00:12:00 Build your Portfolio - Breathe App What You'll Make by the End of This Section 6 00:01:00 Introduction to Breathe App - UI Setup 00:13:00 Introduction to Breathe App - Animation Library 00:11:00 Introduction to Breathe App - Animate the View 00:12:00 Introduction to Breathe App - Saving App Data 00:13:00 Introduction to Breathe App - Final App 00:21:00 Bonus Section - Java Refresher Intro to Variables - Java 00:07:00 Variables - Integers 00:08:00 Variables - Double, Chars, Floats 00:13:00 Variables - Booleans 00:03:00 Java Basic Operations 00:13:00 Java - Relational Operators and If Statements 00:10:00 Java For and While Loops 00:12:00 Java - Methods and Parameters 00:12:00 Java - Methods and Return Types 00:13:00 Java - Introduction to Classes 00:15:00 Java - Member Variables 00:09:00 Java Access Modifiers 00:13:00 Java - Overloading Constructors 00:05:00 Java - Introduction to Inheritance 00:04:00 Java Inheritance - Part 2 00:11:00 Java - Arrays 00:11:00 Java - Arrays - Part 2 00:06:00 Java - HashMaps 00:10:00 Java - HashMaps - Part 2 00:04:00 Installing Android Studio - Setup Development Environment Installing Java, JDK and JRE (Windows PC) 00:09:00 Install Android Studio on Windows PC 00:12:00 Install Android Studio - Mac OSX 00:09:00

Learn about automated software testing with Python, BDD, Selenium WebDriver, and Postman, focusing on web applications

Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies

The course helps you learn Snowflake from scratch and explore a few of its important features. You will build automated pipelines with Snowflake and use the AWS cloud with Snowflake as a data warehouse. You will also explore Snowpark to be worked on the data pipelines.

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