3045 Software Development courses

Venture into the world of data interpretation with our illuminating 'Applied Statistics' course. In an era where every decision is data-driven, the art of statistics becomes an indispensable tool. This course masterfully unfolds the complexities of statistical analysis, from the basics to advanced methodologies. Whether it's understanding central tendencies, charting out data, or avoiding common pitfalls, we've got it all covered. Set yourself on a path to make informed decisions and discern patterns like never before. Learning Outcomes Understand the core concepts and fundamentals of statistics. Master methods of measuring central tendencies and dispersion in datasets. Dive deep into correlation, regression, and hypothesis testing techniques. Attain proficiency in data visualisation using charts and graphs. Identify and rectify ten prevalent statistical mistakes. Why choose this Applied Statistics course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Applied Statistics Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Who is this Applied Statistics course for? Students embarking on a journey in data science or related fields. Business analysts seeking to enhance their data interpretation skills. Researchers keen to strengthen their statistical analysis techniques. Professionals in various industries aiming to make data-backed decisions. Educators looking to teach statistical methods more effectively. Career path Data Analyst - £25,000 - £40,000 Statistical Consultant - £30,000 - £50,000 Research Scientist (Statistics) - £35,000 - £55,000 Econometrician - £40,000 - £60,000 Quantitative Analyst - £45,000 - £70,000 Biostatistician - £30,000 - £50,000 Prerequisites This Applied Statistics does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Applied Statistics 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. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Module 01: Introduction to Statistics Introduction to Statistics 00:18:00 Module 02: Measuring Central Tendency Measuring Central Tendency 00:18:00 Module 03: Measures of Dispersion Measures of Dispersion 00:11:00 Module 04: Correlation and Regression Analysis Correlation and Regression Analysis 00:35:00 Module 05: Probability Probability 00:16:00 Module 06: Sampling Sampling 00:11:00 Module 07: Charts and Graphs Charts and Graphs 00:21:00 Module 08: Hypothesis Testing Hypothesis Testing 00:25:00 Module 09: Ten Common Statistical Mistakes Ten Common Statistical Mistakes 00:30:00 Assignment Assignment - Applied Statistics 00:00:00

Dive into the intricate world of website design with our course, 'HTML and CSS Coding: Beginner to Advanced.' Unravel the mysteries behind the foundational elements of the internet, as you journey from understanding the basics to mastering the complexities. Whether you're a newbie dipping your toes or someone eager to delve deeper, our comprehensive curriculum ensures that by the end, you'll be adept at crafting engaging, responsive, and visually appealing web experiences. Learning Outcomes Comprehend the fundamentals and complexities of HTML, progressing from a beginner to an expert level. Understand the diverse aspects of CSS, from its basic structures to intricate details. Develop the capability to set up an optimal development environment. Acquire the skillset to design and develop a complete website project. Grasp the process to effectively publish a live website. Why buy this HTML and CSS Coding: Beginner to Advanced course? 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 HTML and CSS Coding: Beginner to Advanced 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 HTML and CSS Coding: Beginner to Advanced course for? Novices aiming to grasp the basics of web development. Intermediate coders seeking to bolster their HTML and CSS knowledge. Designers aspiring to incorporate coding into their skillset. Web enthusiasts eager to understand the backbone of online platforms. Individuals looking to transition into a web design or development role. Prerequisites This HTML and CSS Coding: Beginner to Advanced does not require you to have any prior qualifications or experience. You can just enrol and start learning.This HTML and CSS Coding: Beginner to Advanced 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 Web Developer: £30,000 - £50,000 Front-end Developer: £35,000 - £55,000 Web Designer: £25,000 - £45,000 UI/UX Designer: £40,000 - £60,000 Content Manager: £28,000 - £42,000 Website Tester: £27,000 - £40,000 Course Curriculum Getting Started Introduction 00:03:00 Course Curriculum 00:07:00 How to Get Course requirements 00:02:00 Getting Started on Windows, Linux or Mac 00:02:00 How to ask a Great Questions 00:01:00 FAQ's 00:01:00 Setting Up Development Environment Introduction 00:05:00 Choosing Code Editor 00:06:00 Installing Code Editor (Sublime Text) 00:04:00 Overview of a Webpage 00:05:00 Full HTML Code Page 00:07:00 First Hello World! Webpage 00:09:00 HTML Fundamentals Introduction 00:03:00 Heading 00:09:00 Paragraph 00:08:00 Formatting Text 00:12:00 List Items Unordered 00:05:00 List Items Ordered 00:04:00 Classes 00:09:00 ID's in CSS 00:06:00 Comments in HTML 00:04:00 Summary 00:04:00 HTML Intermediate Introduction 00:02:00 Images 00:12:00 Forms 00:05:00 Form Actions 00:04:00 Br tag 00:03:00 Marquee 00:06:00 Text area 00:06:00 Tables 00:06:00 Links 00:07:00 Navbar - Menu 00:04:00 HTML Entities 00:05:00 Div tag 00:06:00 Google Maps 00:07:00 Summary 00:02:00 HTML Advanced Introduction 00:02:00 HTML5 Audio 00:07:00 HTML5 Video 00:05:00 Progress Bar 00:04:00 Drag and Drop 00:18:00 Canvas 00:06:00 I frames 00:05:00 Input Types 00:04:00 Input Attributes 00:06:00 YouTube Video Linking 00:04:00 Creating Responsive Page 00:05:00 Summary 00:02:00 HTML Expert Introduction 00:02:00 Registration Form 00:04:00 Login Form 00:04:00 About Us Form 00:02:00 Contact Us Form 00:10:00 Footer Form 00:03:00 Integrate All Together 00:07:00 Coding Exercise 00:01:00 Solution for Coding Exercise 00:02:00 Summary 00:02:00 HTML Website Project Introduction 00:02:00 Challenge - HTML 5 Clock Face with Numbers 00:05:00 Project overview 00:03:00 Conclusion on Project 00:01:00 Summary 00:02:00 CSS Fundamentals Introduction 00:03:00 CSS Syntax 00:05:00 Creating a first page with CSS Style 00:13:00 Inline CSS 00:06:00 Internal CSS 00:05:00 CSS External 00:10:00 CSS Classes 00:09:00 CSS IDs 00:06:00 Colors 00:08:00 Backgrounds 00:04:00 Floating 00:09:00 Positioning 00:06:00 Margins 00:07:00 Padding 00:04:00 Borders 00:03:00 Summary 00:02:00 CSS Intermediate Introduction 00:02:00 Styling Text 00:07:00 Aligning Text 00:04:00 Styling Links 00:10:00 Font Family 00:07:00 Font Styles 00:03:00 Applying Google Fonts 00:07:00 Box Model 00:09:00 Icons 00:09:00 Tables 00:16:00 Navigation-Menu 00:11:00 Dropdowns 00:15:00 Summary 00:02:00 CSS Advanced Introduction 00:02:00 Advanced Selectors 00:06:00 Forms 00:17:00 Website Layout 00:21:00 Rounded Corners 00:06:00 Color Keywords 00:06:00 Animations 00:08:00 Pseudo Classes 00:03:00 Gradients 00:03:00 Shadows 00:03:00 Calculations 00:05:00 Creating Responsive Page 00:06:00 Summary 00:02:00 CSS Expert Introduction 00:01:00 Button Styles 00:06:00 Flexbox 00:14:00 CSS Grid 00:15:00 Pagination 00:07:00 Multiple Columns 00:06:00 Image Reflection 00:03:00 UI - UX Design 00:09:00 Social Media Icons 00:08:00 External CSS Style adding 00:06:00 Coding Exercise 00:01:00 Solution for Coding Exercise 00:03:00 Summary 00:02:00 CSS Website Project Introduction 00:01:00 CSS Project Getting 00:05:00 CSS Project Overview 00:08:00 Summary 00:01:00 Publish Your Website For Live Introduction 00:02:00 Installing Free FTP Solution (FileZilla) 00:04:00 Setting Up FTP (File Transfer Protocol) 00:03:00 Publish Website to Hosting Server 00:04:00 Summary 00:01:00

Unlock the creative potential of your Android Studio with our Create Android Studio Gallery App course. Are you passionate about mobile app development and eager to create a stunning gallery application? This course provides a step-by-step guide to designing a gallery app right from the start. Whether you're a beginner or a developer looking to expand your skills, you'll dive into the essentials of Android Studio. Learn to list and display images with a user-friendly interface, add features to delete, rename, and share images, and display image information. Additionally, you'll gain valuable insights into image resolution and even implement a feature to rotate images. Let your imagination run wild as you master the art of creating a captivating Android gallery app. Learning Outcomes Set up an Android Studio Gallery App. List and display images with a user-friendly interface. Add features to delete, rename, and share images. Display image information, including resolution. Implement a feature to rotate images. Why choose this Create Android Studio Gallery App course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Create Android Studio Gallery App course for? Aspiring app developers interested in Android. Students and beginners looking to enhance their app development skills. Mobile app enthusiasts keen to create their gallery app. Developers who want to expand their Android Studio knowledge. Anyone excited to dive into mobile app development. Career path Android App Developer: £20,000 - £60,000 Mobile Application Developer: £25,000 - £70,000 Software Engineer: £25,000 - £60,000 Java Developer: £25,000 - £60,000 UI/UX Designer: £22,000 - £50,000 Quality Assurance (QA) Tester: £18,000 - £40,000 Prerequisites This Create Android Studio Gallery App does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Create Android Studio Gallery App 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. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Build an Android Studio Gallery App Module 01: Android Studio Gallery App Setup and General Explanation 00:23:00 Module 02: Listing and Displaying Images to the Ui 00:23:00 Module 03: Displaying Image Name and Adding New Ui Elements 00:24:00 Module 04: Adding Feature to Delete and Rename Images 00:28:00 Module 05: Adding Feature to Share Image and Display Image Information 00:29:00 Module 06: Getting Image Resolution and Setting up Feature to Rotate Image 00:36:00 Module 07: Fixing Rotation Feature, Bug Fixes and General Improvements 00:22:00 Assignment Assignment - Create Android Studio Gallery App 00:00:00

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

Embark on a journey to uncover the labyrinthine world of digital financial security with the 'Hacked Credit and Debit Card Recovery Course'. Navigate through the depths of the web, from understanding the diverse range of websites to delving deep into the dark corridors of the internet, equipping yourself with invaluable cyber intelligence. Through this course, you'll decode various threat perceptions, familiarise yourself with card fraud intricacies, and master the art of information recovery - all tailored to ensure your digital financial transactions remain impervious to threats. Learning Outcomes Understand the fundamentals of cyber threats and their impact on digital financial transactions. Differentiate between various website types and their susceptibility to cyber-attacks. Analyse threat actors and their modus operandi in the cyber realm. Gain insights into the deep and dark web and the tools necessary for information recovery. Acquire proficiency in information handling procedures to maintain digital financial security. Why buy this Hacked Credit and Debit Card Recovery Course? 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 Hacked Credit and Debit Card Recovery Course 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 Hacked Credit and Debit Card Recovery Course for? Individuals keen on enhancing their understanding of digital financial security. Banking and finance professionals looking to fortify their defence mechanisms. Cybersecurity enthusiasts aiming to delve into card fraud detection and prevention. Internet users wanting to safeguard their online financial transactions. Tech-savvy individuals eager to explore deep and dark web intelligence. Prerequisites This Hacked Credit and Debit Card Recovery Course does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Hacked Credit and Debit Card Recovery Course 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 Cyber Security Analyst: £35,000 - £55,000 Fraud Detection Analyst: £30,000 - £50,000 Dark Web Researcher: £40,000 - £65,000 Information Security Officer: £45,000 - £70,000 Threat Intelligence Specialist: £50,000 - £75,000 Financial Security Consultant: £55,000 - £80,000 Course Curriculum Unit 01: Introduction Introduction & Objective 00:01:00 Unit 02: Types of Website Types of Website 00:01:00 Surface Web 00:01:00 Deep Web 00:01:00 Dark Web 00:03:00 2016 - 2017 Profit Comparison from 5000 00:01:00 Intelligence Agency Web 00:01:00 Quantum Computers 00:01:00 Polymeric Falcighol Derivation 00:01:00 Graphical representation 00:01:00 Unit 03: Threat Perception Threat Perception 00:01:00 Threat Actor 00:01:00 Threat Actor-Compared to a Hacker Or Attacker 00:01:00 Is the Dark Net Market gone? 00:03:00 Unit 04: Card Fraud Card Fraud 00:04:00 Card-Not-Present Fraud (CNP) 00:02:00 Unit 05: Threat Ninja Threat Ninja 00:01:00 Threat Ninja Architecture 00:03:00 Adaptive Assessment 00:01:00 Secure Coat Approach 00:01:00 Secure Coat's Value Proposition 00:02:00 Challenge 00:01:00 Unit 06: Threat Actor Analysis Threat Actor Analysis 00:00:00 Kuchinoni - ATM Theft 00:01:00 Insider Threats 00:01:00 Unit 07: Cyber Security Monitoring Cyber Security Monitoring 00:01:00 Protect Your Company via DDWM 00:01:00 Unit 08: Threat Life Cycle Threat Life Cycle 00:06:00 Unit 09: Information Leakage Points Information Leakage Points 00:04:00 Unit 10: Valuable Information Valuable Information 00:09:00 Unit 11: Area of Search Area of Search 00:01:00 Sell Cards at Social Media, Messenger, etc. Groups 00:01:00 Unit 12: Deep & Dark Web Intelligence and Information Recovery Deep & Dark Web Intelligence and Information Recovery 00:01:00 Unit 13: Banking Fraud Types Banking Fraud Types 00:01:00 Card Fraud- Nilson Report 00:01:00 U.S. Card Fraud Losses 00:01:00 Card Fraud Statistics 00:05:00 Unit 14: Threat Ninja Tool Secure Coat Threat Ninja Portal 00:01:00 Threat Ninja Demo 00:05:00 Unit 15: Information Handing Procedures Information Handling Procedures 00:01:00 Card Discard Life Cycle 00:02:00 Unit 16: Course Wrap up Congratulations and Course Summary 00:03:00 Thank you! 00:01:00 Unit 17: Bonus Rise in the price of the Crypto Coin 00:06:00 Assignment Assignment - Hacked Credit and Debit Card Recovery Course 00:00:00

This video course will help you learn all the basic and advanced concepts of Microsoft Excel 2019. You'll start with Excel basics and slowly move to the advanced concepts in Excel. Each section is accompanied by an exercise at the end to practice what you have learned.

Duration 5 Days 30 CPD hours This course is intended for Professionals who need to maintain or set up a Kubernetes cluster Container Orchestration Engineers DevOps Professionals Overview Cluster architecture, installation, and configuration Rolling out and rolling back applications in production Scaling clusters and applications to best use How to create robust, self-healing deployments Networking configuration on cluster nodes, services, and CoreDNS Persistent and intelligent storage for applications Troubleshooting cluster, application, and user errors Vendor-agnostic cloud provider-based Kubernetes Kubernetes is a Cloud Orchestration Platform providing reliability, replication, and stability while maximizing resource utilization for applications and services. By the conclusion of this hands-on, vendor agnostic training you will go back to work with the knowledge, skills, and abilities to design, implement, and maintain a production-grade Kubernetes cluster. We prioritize covering all objectives and concepts necessary for passing the Certified Kubernetes Administrator (CKA) exam. You will be provided the components necessary to assemble your own high availability Kubernetes environment and configure, expand, and control it to meet the demands made of cluster administrators. Your week of intensive, hands-on training will conclude with a mock CKA exam that simulates the real exam. Cluster Architecture, Installation & Configuration Each student will be given an environment that allows them to build a Kubernetes cluster from scratch. After a detailed discussion on key architectural components and primitives, students will install and compare two production grade Kubernetes clusters. Review: Kubernetes Fundamentals After successfully instantiating their own Kubernetes Cluster, students will be guided through foundational concepts of deploying and managing applications in a production environment. Workloads & Scheduling After establishing a solid Kubernetes command line foundation, students will be led through discussion and hands-on labs which focus on effectively creating applications that are easy to configure, simple to manage, quick to scale, and able to heal themselves. Services & Networking Thoroughly understanding the underlying physical and network infrastructure of a Kubernetes cluster is an essential skill for a Certified Kubernetes Administrator. After an in-depth discussion of the Kubernetes Networking Model, students explore the networking of their cluster?s Control Plane, Workers, Pods, and Services. Storage Certified Kubernetes Administrators are often in charge of designing and implementing the storage architecture for their clusters. After discussing many common cluster storage solutions and how to best use each, students practice incorporating stateful storage into their applications. Troubleshooting A Certified Kubernetes Administrator is expected to be an effective troubleshooter for their cluster. The lecture covers a variety of ways to evaluate and optimize available log information for efficient troubleshooting, and the labs have students practice diagnosing and resolving several typical issues within their Kubernetes Cluster. Certified Kubernetes Administrator Practice Exam Just like the Cloud Native Computing Foundation CKA Exam, the students will be given two hours to complete hands-on tasks in their own Kubernetes environment. Unlike the certification exam, students taking the Alta3 CKA Practice Exam will have scoring and documented answers available immediately after the exam is complete, and will have built-in class time to re-examine topics that they wish to discuss in greater depth.

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

Duration 5 Days 30 CPD hours This course is intended for Anyone who plans to work with Kubernetes at any level or tier of involvement Any company or individual who wants to advance their knowledge of the cloud environment Application Developers Operations Developers IT Directors/Managers Overview All topics required by the CKAD exam, including: Deploy applications to a Kubernetes cluster Pods, ReplicaSets, Deployments, DaemonSets Self-healing and observable applications Multi-container Pod Design Application configuration via Configmaps, Secrets Administrate cluster use for your team A systematic understanding of Kubernetes architecture Troubleshooting and debugging tools Kubernetes networking and services Kubernetes is a Cloud Orchestration Platform providing reliability, replication, and stability while maximizing resource utilization for applications and services. By the conclusion of this hands-on training, you will go back to work with all necessary commands and practical skills to empower your team to succeed, as well as gain knowledge of important concepts like Kubernetes architecture and container orchestration. We prioritize covering all objectives and concepts necessary for passing the Certified Kubernetes Application Developer (CKAD) exam. You will command and configure a high availability Kubernetes environment (and later, build your own!) capable of demonstrating all ?K8s'' features discussed and demonstrated in this course. Your week of intensive, hands-on training will conclude with a mock CKAD exam that matches the real thing. Kubernetes Architecture Components Understand API deprecations Containers Define, build and modify container images Pods Master Services Node Services K8s Services YAML Essentials Creating a K8s Cluster kubectl Commands Kubernetes Resources Kubernetes Namespace Kubernetes Contexts Pods What is a Pod? Create, List, Delete Pods How to Access Running Pods Kubernetes Resources Managing Cloud Resource Consumption Multi-Container Pod Design Security Contexts Init Containers Understand multi-container Pod design patterns (e.g. sidecar, init and others) Pod Wellness Tracking Networking Packet Forwarding ClusterIP and NodePort Services Provide and troubleshoot access to applications via services Ingress Controllers Use Ingress rules to expose applications NetworkPolicy resource Demonstrate basic understanding of NetworkPolicies Network Plugins Defining the Service Mesh Service mesh configuration examples ReplicaSets Services ReplicaSet Function Deploying ReplicaSets Deployments Deployment Object Updating/Rolling Back Deployments Understand Deployments and how to perform rolling updates Deployment Strategies Use Kubernetes primitives to implement common deployment strategies (e.g. blue/green or canary) Scaling ReplicaSets Autoscaling Labels and Annotations Labels Annotations Node Taints and Tolerations Jobs The K8s Job and CronJob Understand Jobs and CronJobs Immediate vs. scheduled internal use Application Configuration Understanding and defining resource requirements, limits and quotas Config Maps Create & consume Secrets Patching Custom Resource Definition Discover and use resources that extend Kubernetes (CRD) Managing ConfigMaps and Secrets as Volumes Storage Static and dynamic persistent volumes via StorageClass K8s volume configuration Utilize persistent and ephemeral volumes Adding persistent storage to containers via persistent volume claims Introduction to Helm Helm Introduction Charts Use the Helm package manager to deploy existing packages Application Security Understand authentication, authorization and admission control Understand ServiceAccounts Understand SecurityContexts Application Observability and Maintenance Use provided tools to monitor Kubernetes applications How to Troubleshoot Kubernetes Basic and Advanced Logging Techniques Utilize container logs Accessing containers with Port-Forward Debugging in Kubernetes Hands on Labs: Define, build and modify container images Deploy Kubernetes using Ansible Isolating Resources with Kubernetes Namespaces Cluster Access with Kubernetes Context Listing Resources with kubectl get Examining Resources with kubectl describe Create and Configure Basic Pods Debugging via kubectl port-forward Imperative vs. Declarative Resource Creation Performing Commands inside a Pod Understanding Labels and Selectors Insert an Annotation Create and Configure a ReplicaSet Writing a Deployment Manifest Perform rolling updates and rollbacks with Deployments Horizontal Scaling with kubectl scale Implement probes and health checks Understanding and defining resource requirements, limits and quotas Understand Jobs and CronJobs Best Practices for Container Customization Persistent Configuration with ConfigMaps Create and Consume Secrets Understand the Init container multi-container Pod design pattern Using PersistentVolumeClaims for Storage Dynamically Provision PersistentVolumes with NFS Deploy a NetworkPolicy Provide and troubleshoot access to applications via services Use Ingress rules to expose applications Understand the Sidecar multi-container Pod design pattern Setting up a single tier service mesh Tainted Nodes and Tolerations Use the Helm package manager to deploy existing packages A Completed Project Install Jenkins Using Helm and Run a Demo Job Custom Resource Definitions (CRDs) Patching Understanding Security Contexts for Cluster Access Control Utilize container logs Advanced Logging Techniques Troubleshooting Calicoctl Deploy a Kubernetes Cluster using Kubeadm Monitoring Applications in Kubernetes Resource-Based Autoscaling Create ServiceAccounts for use with the Kubernetes Dashboard Saving Your Progress With GitHub CKAD Practice Drill Alta Kubernetes Course Specific Updates Sourcing Secrets from HashiCorp Vault Example CKAD Test Questions