5804 Flexibility courses in Cardiff delivered On Demand

Are you ready to be at the helm, steering the ship into a realm where data is the new gold? In the infinite world of data, where information spirals at breakneck speed, lies a universe rich in potential and discovery: the domain of Data Science and Visualisation. This 'Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3' course unravels the wonders of extracting meaningful insights using Python, the worldwide leading language of data experts. Harnessing the strength of Python, you'll delve deep into data analysis, experience the finesse of visualisation tools, and master the art of Machine Learning. The need to understand, interpret, and act on this data has become paramount, with vast amounts of data increasing the digital sphere. Envision a canvas where raw numbers are transformed into visually compelling stories, and machine learning models foretell future trends. This course provides a meticulous pathway for anyone eager to learn the data representation paradigms backed by Python's robust libraries. Dive into a curriculum rich with analytical explorations, visual artistry, and machine learning predictions. Learning Outcomes Understanding the foundations and functionalities of Python, focusing on its application in data science. Applying various Python libraries like NumPy and Pandas for effective data analysis. Demonstrating proficiency in creating detailed visual narratives using tools like matplotlib, Seaborn, and Plotly. Implementing Machine Learning algorithms in Python using scikit-learn, ranging from regression models to clustering techniques. Designing and executing a holistic data analysis and visualisation project, encapsulating all learned techniques. Exploring advanced topics, encompassing recommender systems and natural language processing with Python. Attaining the confidence to independently analyse complex data sets and translate them into actionable insights.   Video Playerhttps://studyhub.org.uk/wp-content/uploads/2021/03/Data-Science-and-Visualisation-with-Machine-Learning.mp400:0000:0000:00Use Up/Down Arrow keys to increase or decrease volume. Why buy this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3? 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 Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 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 Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 course for? Aspiring data scientists aiming to harness the power of Python. Researchers keen to enrich their analytical and visualisation skills. Analysts aiming to add machine learning to their toolkit. Developers striving to integrate data analytics into applications. Business professionals desiring data-driven decision-making capabilities. Career path Data Scientist: £55,000 - £85,000 Per Annum Machine Learning Engineer: £60,000 - £90,000 Per Annum Data Analyst: £30,000 - £50,000 Per Annum Data Visualisation Specialist: £45,000 - £70,000 Per Annum Natural Language Processing Specialist: £65,000 - £95,000 Per Annum Business Intelligence Developer: £40,000 - £65,000 Per Annum Prerequisites This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 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. Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £85 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. Course Curriculum Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Visualisation with Machine Learning 00:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00

Overview This Deep Learning & Neural Networks Python - Keras: For Dummies course will unlock your full potential and will show you how to excel in a career in Deep Learning & Neural Networks Python - Keras: For Dummies. So upskill now and reach your full potential. Everything you need to get started in Deep Learning & Neural Networks Python - Keras: For Dummies is available in this course. Learning and progressing are the hallmarks of personal development. This Deep Learning & Neural Networks Python - Keras: For Dummies will quickly teach you the must-have skills needed to start in the relevant industry. In This Deep Learning & Neural Networks Python - Keras: For Dummies Course, You Will: Learn strategies to boost your workplace efficiency. Hone your Deep Learning & Neural Networks Python - Keras: For Dummies skills to help you advance your career. Acquire a comprehensive understanding of various Deep Learning & Neural Networks Python - Keras: For Dummies topics and tips from industry experts. Learn in-demand Deep Learning & Neural Networks Python - Keras: For Dummies skills that are in high demand among UK employers, which will help you to kickstart your career. This Deep Learning & Neural Networks Python - Keras: For Dummies course covers everything you must know to stand against the tough competition in the Deep Learning & Neural Networks Python - Keras: For Dummies field.  The future is truly yours to seize with this Deep Learning & Neural Networks Python - Keras: For Dummies. Enrol today and complete the course to achieve a Deep Learning & Neural Networks Python - Keras: For Dummies certificate that can change your professional career forever. Additional Perks of Buying a Course From Institute of Mental Health Study online - whenever and wherever you want. One-to-one support from a dedicated tutor throughout your course. Certificate immediately upon course completion 100% Money back guarantee Exclusive discounts on your next course purchase from Institute of Mental Health Enrolling in the Deep Learning & Neural Networks Python - Keras: For Dummies course can assist you in getting into your desired career quicker than you ever imagined. So without further ado, start now. Process of Evaluation After studying the Deep Learning & Neural Networks Python - Keras: For Dummies course, your skills and knowledge will be tested with a MCQ exam or assignment. You must get a score of 60% to pass the test and get your certificate.  Certificate of Achievement Upon successfully completing the Deep Learning & Neural Networks Python - Keras: For Dummies course, you will get your CPD accredited digital certificate immediately. And you can also claim the hardcopy certificate completely free of charge. All you have to do is pay a shipping charge of just £3.99. Who Is This Course for? This Deep Learning & Neural Networks Python - Keras: For Dummies is suitable for anyone aspiring to start a career in Deep Learning & Neural Networks Python - Keras: For Dummies; even if you are new to this and have no prior knowledge on Deep Learning & Neural Networks Python - Keras: For Dummies, this course is going to be very easy for you to understand.  And if you are already working in the Deep Learning & Neural Networks Python - Keras: For Dummies field, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  Taking this Deep Learning & Neural Networks Python - Keras: For Dummies course is a win-win for you in all aspects.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements This Deep Learning & Neural Networks Python - Keras: For Dummies course has no prerequisite.  You don't need any educational qualification or experience to enrol in the Deep Learning & Neural Networks Python - Keras: For Dummies course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online Deep Learning & Neural Networks Python - Keras: For Dummies course. Moreover, this course allows you to learn at your own pace while developing transferable and marketable skills. 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

Overview of Google Classroom Essentials Google Classroom Essentials is a helpful course for teachers who want to use digital tools to improve how they teach. After the pandemic, more UK schools have started using online platforms. Over 170 million students and teachers around the world now use Google for Education. This means learning how to use Google Classroom Essentials has become very important for modern-day teaching. The course shows you how to create and manage your online classroom simply and clearly. In this course, you will learn how to set up your first virtual class, share work with students, give feedback, and make grading easier. It also helps with organising lessons, talking to students, and using tools that help with teamwork. Whether you are just starting with online teaching or want to improve the way you already teach digitally, this Google Classroom Essentials course is made to support your progress step by step. The UK education system has seen a 300% rise in digital learning. Because of this, more schools are looking for teachers who can use tools like Google Classroom Essentials. This course helps you build the skills needed for today’s classrooms. With practical training and simple methods, Google Classroom Essentials gives you the support you need to become more confident with digital teaching and meet the needs of students in a changing world. Learning Outcomes By the end of this Google Classroom Essentials course, you will be able to: Set up and manage your Google Classroom account easily Find your way around the classroom dashboard and adjust the settings Add other teachers and invite students to your online class Create and organise assignments, share learning materials, and post updates Use marking rubrics and keep track of student progress Create quizzes and ask questions using built-in classroom tools Who is this course for This course is a good fit for: Primary and Secondary School Teachers who want to bring digital tools into their classrooms and support students aged 5–18 with better learning University Lecturers and Education Staff who want an easier way to share course content, manage assignments, and stay connected with students Corporate Training Managers who need a smart way to deliver training and learning materials to employees Tutoring Centre Coordinators who work with many students and need a clear system to manage classes, track progress, and share resources Support Staff in Education, such as teaching assistants or learning mentors, who help with classroom tasks and support student learning Process of Evaluation After studying the Google Classroom Essentials Course, your skills and knowledge will be tested with an MCQ exam or assignment. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the Google Classroom Essentials Course, you can order your CPD Accredited Digital / PDF Certificate for £5.99. (Each) Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. (Each) Shipping Charges: Inside the UK: £3.99 International: £10.99 Requirements You don't need any educational qualification or experience to enrol in the Google Classroom Essentials course. Career Path: Completing the Google Classroom Essentials course can support your move into roles such as: Teaching Assistant – £18,000 to £25,000 per year Primary School Teacher – £28,000 to £45,000 per year Secondary School Teacher – £28,000 to £50,000 per year Educational Technology Coordinator – £25,000 to £40,000 per year Corporate Training Specialist – £30,000 to £48,000 per year Course Curriculum Unit 01: Setting up your Classroom Getting Google Account and Signing in 00:02:00 Creating a Class 00:04:00 Classroom Navigation 00:09:00 Class Settings 00:04:00 Add Co-Teacher and Students 00:09:00 Creating of Additional Class 00:02:00 Google Drive Structure & Class options 00:07:00 Unit 02: Managing Google Classroom Topics 00:07:00 Assignment 00:14:00 Assignment Dashboard & Student Communication 00:06:00 To Do List and Calendar 00:05:00 Materials 00:06:00 Announcement 00:06:00 Quiz Assignments 00:17:00 Questions 00:03:00 Reusing Posts 00:05:00 Grade 00:05:00 Rubric 00:11:00 Unit 03: Bonus (Update) Join Google Classroom using link 00:02:00 Google Classroom Home Page Changes 00:01:00

Overview of Dog Training - Stop Dog Attacks Are you worried about dog attacks—whether from your own pet or another dog? The Dog Training - Stop Dog Attacks Course is designed to help you stay safe and build a better bond with dogs. Every year, around 250,000 people in the UK are bitten by dogs. In fact, hospital visits due to dog attacks have gone up by 17% in recent years. This course helps you understand dog behaviour, especially signs that show if a dog is feeling aggressive or scared. You’ll learn how to react the right way to stop problems before they happen. This is more than basic obedience training. The Dog Training - Stop Dog Attacks Course goes deeper by showing you the real reasons behind dog aggression. You’ll learn how to spot warning signs and calm things down before a dog reacts. The course also shows how to handle leash-pulling, prevent fights between dogs, and guide aggressive dogs in safe ways. These are skills you can use every day with your dog or any dog you meet. Many people have already seen big changes using this course. It helps build trust, respect, and understanding between dogs and humans. With the Dog Training - Stop Dog Attacks Course, you’ll be ready to create a safer and more peaceful life with your dog. This Dog Training - Stop Dog Attacks course will help you to learn: Spot the difference between confident and scared behaviour in dogs. Use calm and strong leadership to guide your dog’s behaviour. Stop your dog from pulling on the leash during walks. Notice early signs that a dog may be ready to attack. Help aggressive dogs mix safely with other dogs. Use simple steps to stop aggressive behaviour from happening again. Who is this Course for? Dog owners who have trouble with their pet’s aggression and want real help to stop attacks. Dog walkers who look after more than one dog and want to feel safer when handling aggressive dogs. Rescue centre workers who care for dogs with past trauma or social problems. Pet owners who’ve gone through a dog attack and want to understand why it happened and how to prevent it. Puppy owners who want to teach their dog the right behaviour from the start. Process of Evaluation After studying the Dog Training - Stop Dog Attacks Course, your skills and knowledge will be tested with an MCQ exam or assignment. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the Dog Training - Stop Dog Attacks Course, you can order your CPD Accredited Digital / PDF Certificate for £5.99. (Each) Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. (Each) Shipping Charges: Inside the UK: £3.99 International: £10.99 Requirements There is no prerequisite to enrol in this course. Career path Dog Behaviour Expert – Earn around £25,000 to £45,000 a year Dog Trainer – Earn around £20,000 to £35,000 a year Animal Welfare Worker – Earn around £18,000 to £28,000 a year Dog Walker or Handler – Earn around £15,000 to £25,000 a year Dog Rehabilitation Therapist – Earn around £22,000 to £38,000 a year Course Curriculum The Proof Is In The Pudding - This Is What People Say Why Getting 2, 8 Week Old Parson Terrier Puppies Changed My Life And My Career! 00:03:00 This Is What People Say Who've Learnt My Methods 00:01:00 This Could Be You! 00:15:00 This is What You'll Get from This Course Able To Do All This After the Course... 00:04:00 Important - Please Read This Before Starting The Course Important - Please Read This First 00:05:00 Challenging And Submissive Signals In Dogs How To Recognize Challenging And Submissive Signs In Order to Stop an Attack 00:11:00 Body Language And How To Get Leadership qualities 00:07:00 Pulling on the Leash and Dog Aggression The Missing Part of Dog Aggression - You Must Get This Right First 00:10:00 How To Get Your Dog To Walk Calmly On The Leash And Integrate Aggressive Dogs 00:08:00 Who's Walking Who E-Book 01:00:00 This Video Shows You How To Get An Aggressive Dog To Relax Around Other Dogs 00:05:00 A Dog Attack What A Dog Attack Means And How To Stop It Happening Again 00:15:00 The Transcript To Lectures 6,7 And 10 00:10:00 Mock Exam Mock Exam - Dog Training - Stop Dog Attacks 00:20:00 Final Exam Final Exam - Dog Training - Stop Dog Attacks 00:20:00

Embarking on the CompTIA Security+ course is akin to unlocking a treasure trove of cybersecurity knowledge. Imagine standing at the forefront of digital security, equipped with the latest tools and techniques to safeguard information systems. This course, meticulously designed with fourteen comprehensive sections, offers an immersive journey from the basics of risk management to the complexities of securing dedicated systems. The curriculum is tailored to blend theoretical knowledge with real-world applications, ensuring learners grasp the essence of cybersecurity in today's technology-driven world. Whether it's understanding the nuances of cryptography or mastering the art of incident response, CompTIA Security+ is your gateway to becoming a cybersecurity connoisseur. Delving into this course, you'll navigate through a landscape where every section is a stepping stone to mastering security competencies.  The course begins with an introduction to the fundamental principles of cybersecurity, setting the stage for more advanced topics like identity and account management, and securing wireless LANs. Each section is a meticulously crafted module, aimed at building a robust understanding of both the threats and the defenses in the cyber world. By the time you reach the final sections on secure protocols, applications, and testing infrastructure, you will have developed a holistic view of network security, ready to apply your knowledge in a variety of real-world scenarios. CompTIA Security+ is not just a course; it's a journey through the dynamic landscape of network security. As you progress from one section to another, you'll not only learn about securing individual systems and public servers but also about the importance of physical security in protecting digital assets. This course is a blend of theory and practicality, providing an in-depth understanding of the latest security tools and techniques. It's an opportunity to transform yourself into a sought-after professional in the cybersecurity domain, equipped with the knowledge to protect and defend against the ever-evolving cyber threats. Learning Outcomes Develop a comprehensive understanding of risk management strategies and their application in cybersecurity. Acquire proficiency in various cryptographic methods and their importance in securing data. Gain insights into effective identity and account management to enhance system security. Learn to utilize essential security tools for safeguarding individual and network systems. Master the skills to implement and manage security measures for both wired and wireless networks. Why buy this CompTIA Security+? 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. Certification After studying the course materials of the CompTIA Security+ 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? Individuals aiming to start a career in cybersecurity. IT professionals seeking to deepen their knowledge in network security. System administrators wanting to enhance their security skills. Students interested in acquiring a foundational understanding of cybersecurity principles. Professionals aiming to achieve the CompTIA Security+ certification for career advancement. Prerequisites This CompTIA Security+ does not require you to have any prior qualifications or experience. You can just enrol and start learning.This CompTIA Security+ 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 Cybersecurity Analyst: £30,000 - £60,000 annually Information Security Manager: £40,000 - £75,000 annually Network Security Engineer: £35,000 - £65,000 annually Security Consultant: £35,000 - £70,000 annually Systems Administrator (with security specialization): £25,000 - £50,000 annually IT Security Coordinator: £28,000 - £55,000 annually Course Curriculum Section 01: Introduction Introduction to CompTIA Security+ SY0-601 00:03:00 About the CompTIA Security+ SY0-601 Exam 00:03:00 Section 02: Risk Management Defining Risk 00:08:00 Threats and Vulnerabilities 00:07:00 Threat Intelligence 00:11:00 Risk Management Concepts 00:07:00 Security Controls 00:09:00 Risk Assessments and Treatments 00:06:00 Quantitative Risk Assessments 00:07:00 Qualitative Risk Assessments 00:04:00 Business Impact Analysis 00:05:00 Data Types and Roles 00:11:00 Security and the Information Life Cycle 00:09:00 Data Destruction 00:06:00 Personnel Risk and Policies 00:10:00 Third-Party Risk Management 00:09:00 Agreement Types 00:07:00 Exam Question Review 00:02:00 Wiping Disks with the dd Command Lab 00:06:00 Ask Me Anything (AMA) 00:02:00 Section 03: Cryptography Cryptography Basics 00:16:00 Data Protection 00:09:00 Cryptographic Methods 00:07:00 Symmetric Cryptosystems 00:13:00 Symmetric Block Modes 00:08:00 Asymmetric Cryptosystems 00:13:00 Diffie-Hellman 00:07:00 Hashing 00:09:00 Understanding Digital Certificates 00:08:00 Trust Models 00:05:00 Public Key Infrastructure 00:04:00 Certificate Types 00:14:00 Touring Certificates 00:09:00 Cryptographic Attacks 00:05:00 Password Cracking 00:10:00 Password Cracking Demo 00:06:00 Exam Question Review 00:02:00 SSH Public Key Authentication Lab 00:09:00 Ask Me Anything (AMA) 00:02:00 Section 04: Identity and Account Management Identification, Authentication, and Authorization 00:08:00 Enabling Multifactor Authentication 00:05:00 Authorization 00:05:00 Accounting 00:05:00 Authentication Methods 00:14:00 Access Control Schemes 00:07:00 Account Management 00:13:00 Network Authentication 00:09:00 Identity Management Systems 00:06:00 Exam Question Review 00:02:00 Creating LInux Users and Groups Lab 00:06:00 Ask Me Anything (AMA) 00:01:00 Section 05: Tools of the Trade Touring the CLI 00:16:00 Shells 00:06:00 The Windows Command Line 00:05:00 Microsoft PowerShell 00:13:00 Linux Shells 00:12:00 Python Scripts 00:06:00 Windows Command-Line Tools 00:16:00 Linux Command-Line Tools 00:10:00 Network Scanners 00:05:00 Network Scanning with Nmap 00:09:00 Network Protocol Analyzers 00:08:00 Using Wireshark to Analyze Network Traffic 00:09:00 Using tcpdump to Analyze Network Traffic 00:08:00 Log Files 00:09:00 Centralized Logging 00:09:00 Configuring Linux Log Forwarding 00:08:00 Exam Question Review 00:03:00 Lunux Shell Script Lab 00:07:00 Nmap Lab 00:05:00 Ask Me Anything (AMA) 00:02:00 Section 06: Securing Individual Systems Malware 00:14:00 Weak Configurations 00:12:00 Common Attacks 00:09:00 Driver and Overflow Attacks 00:08:00 Password Attacks 00:08:00 Bots and Botnets 00:06:00 Disk RAID Levels 00:10:00 Securing Hardware 00:11:00 Securing Endpoints 00:09:00 Exam Question Review 00:02:00 Linux Software RAID Lab 00:08:00 Ask Me Anything (AMA) 00:02:00 Section 07: The Basic LAN The OSI Model 00:13:00 ARP Cache Poisoning 00:09:00 Other Layer 2 Attacks 00:05:00 Network Planning 00:07:00 Load Balancing 00:06:00 Securing Network Access 00:06:00 Honeypots 00:06:00 Firewalls 00:08:00 Proxy Servers 00:06:00 Network and Port Address Translation 00:07:00 IP Security (IPsec) 00:09:00 Virtual Private Networks (VPNs) 00:10:00 Intrusion Detection and Prevention Systems (IDS/IPS) 00:13:00 Exam Question Review 00:03:00 Linux Snort IDS Lab 00:07:00 Ask Me Anything (AMA) 00:01:00 Section 08: Securing Wireless LANs Wi-Fi Encryption Standards 00:10:00 RFID, NFC, and Bluetooth 00:07:00 Wi-Fi Coverage and Performance 00:08:00 Wi-Fi Discovery and Attacks 00:12:00 Cracking WPA2 00:10:00 Wi-Fi Hardening 00:11:00 Exam Question Review 00:02:00 WPA2 Cracking Lab 00:06:00 Ask Me Anything (AMA) 00:01:00 Section 09: Securing Public Servers Defining a Public Server 00:01:00 Common Attacks and Mitigations 00:10:00 Containers and Software-Defined Networking 00:11:00 Hypervisors and Virtual Machines 00:08:00 Cloud Deployment Models 00:09:00 Cloud Service Models 00:08:00 Securing the Cloud 00:10:00 Exam Question Review 00:02:00 Docker Container Lab 00:04:00 Ask Me Anything (AMA) 00:02:00 Section 10: Securing Dedicated Systems Embedded Systems 00:13:00 Industrial Control System (ICS) 00:07:00 Internet of Things (IoT) Devices 00:10:00 Connecting to Dedicated and Mobile Systems 00:11:00 Security Constraints for Dedicated Systems 00:05:00 Mobile Device Deployment and Hardening 00:11:00 Exam Question Review 00:03:00 Smartphone Hardening Lab 00:03:00 Ask Me Anything (AMA) 00:02:00 Section 11: Physical Security Physical Security Overview 00:01:00 Physical Security 00:10:00 Keylogger Demo 00:05:00 Environmental Controls 00:05:00 Exam Question Review 00:02:00 Physical Security Lab 00:03:00 Ask Me Anything (AMA) 00:03:00 Section 12: Secure Protocols and Applications DNS Security 00:05:00 FTP Packet Capture 00:03:00 Secure Web and E-mail 00:12:00 Request Forgery Attacks 00:05:00 Cross-Site Scripting Attacks 00:07:00 Web Application Security 00:08:00 Web App Vulnerability Scanning 00:06:00 Exam Question Review 00:03:00 OWASP ZAP Web App Scan Lab 00:04:00 Ask Me Anything (AMA) 00:02:00 Section 13: Testing Infrastructure Testing Infrastructure Overview 00:05:00 Social Engineering 00:06:00 Social Engineering Attacks 00:11:00 Vulnerability Assessments 00:09:00 Penetration Testing 00:10:00 Security Assessment Tools 00:12:00 The Metasploit Framework 00:08:00 Exam Question Review 00:02:00 Hping3 Forged Packet Lab 00:06:00 Ask Me Anything (AMA) 00:02:00 Section 14: Dealing with Incidents Incident Response Overview 00:03:00 Incident Response Plans (IRPs) 00:06:00 Threat Analysis and Mitigating Actions 00:08:00 Digital Forensics 00:10:00 Gathering Digital Evidence 00:10:00 Business Continuity and Alternate Sites 00:06:00 Data Backup 00:10:00 Exam Question Review 00:01:00 Autopsy Forensic Browser Lab 00:05:00 Ask Me Anything (AMA) 00:02:00 Assignment Assignment - CompTIA Security+ 00:00:00

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