42843 Care courses in Cardiff delivered Online

Occupational Therapy Online Course Occupational Therapy Course is the only profession that helps people to participate in the activities of everyday life. The therapy promotes health and well being through occupation. Occupational Therapy Course course covers the techniques you need to know for involving people with various activities and making them independent. Firstly, the course explains the very concept of occupational therapy and shows you the techniques while working with adults and kids. Then, you will know the basic anatomy of physiology such as Blood & the Immune System, Brain injuries, physical disabilities, etc. Next, the course teaches you creative and management skills including the use of creative activities, management and leadership skills, developing confidence and self-esteem and the techniques to deal with depression. Finally, the course covers the career growth and opportunities of occupational therapy and alternative career options. Course Curriculum Introduction to Occupational Therapy (Basic) Anatomy & Physiology Creative & Management Skills Career Growth in Occupational Therapy Individual (Client's) Development (Learn more about this online course)

Want to step into the exciting world of design? This beginner-friendly Adobe XD UI/UX Design course takes you from zero to hero. Learn how to design beautiful websites and apps, understand what users need, and create smart, user-friendly interfaces. With hands-on lessons, clear guidance, and real examples, you’ll build the skills needed to start your career in UX/UI design. Learning Outcomes Understand how the web and design industries work Learn the difference between UX and UI design Master the basics of graphic design Discover how to create wireframes and prototypes Use Adobe XD to design websites and mobile apps Who is this for? This course is perfect for beginners who want to learn UX/UI design from scratch. It's also great for creative people, students, career changers, or anyone looking to explore the world of digital design. No design or coding experience is needed—just a curious mind and a desire to learn something new. Career path After completing this course, you can explore job roles in the growing tech and design world. Many companies need UX/UI designers to improve their websites and apps. You can work in tech, marketing, or even as a freelance designer. Prerequisites There are no formal requirements to join this course. You only need basic computer skills, a passion for creativity, and the willingness to learn. A free version of Adobe XD will also help you follow along with the lessons. Certification Upon completion of the course and passing the final assessment, you can obtain a PDF certificate for £9.99. Hard copy certificates are available for an additional £15.99. Disclaimer: This is an online course with pre-recorded sessions. Course access will be granted within 24 hours of enrollment. (Learn more about this online course)

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

Ignite your passion for the electrifying world with our course on 'Electrical Engineering With Electric Circuits'. Envision the realm where electricity comes alive, weaving intricate patterns that power our world. Journey with us as we illuminate foundational concepts, delve deep into circuit analysis, and unveil the magic behind operational amplifiers. With each unit, you'll unravel the mysteries of capacitors, inductors, and the fundamental laws governing them, forging a path towards mastery in electrical engineering. Learning Outcomes Develop a solid understanding of the foundational concepts in electrical engineering. Discover and apply the basic laws governing electric circuits. Employ various methods to analyse complex electrical circuits. Understand the principles behind circuit theorems and operational amplifiers. Gain proficiency in working with capacitors and inductors. Why choose this Electrical Engineering With Electric Circuits 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 Electrical Engineering With Electric Circuits 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 Electrical Engineering With Electric Circuits course for? Aspiring electrical engineers seeking foundational knowledge. Technicians aiming for a deeper understanding of electric circuits. University students studying electrical engineering as a major. Hobbyists keen on diving into the world of circuits and electronics. Professionals in related fields aiming to expand their skill set. Career path Electrical Engineer: £25,000 - £55,000 Circuit Designer: £28,000 - £50,000 Operational Amplifier Specialist: £30,000 - £54,000 Electronics Technician: £20,000 - £40,000 System Analyst (Electrical Circuits): £32,000 - £57,000 Researcher in Electrical Engineering: £28,000 - £52,000 Prerequisites This Electrical Engineering With Electric Circuits does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Electrical Engineering With Electric Circuits 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 Unit 1- Basic Concepts Module 1- What Is an Electric Circuit 00:02:00 Module 2-System of Units 00:07:00 Module 3- What Is an Electric Charge 00:05:00 Module 4- What Is an Electric Current 00:08:00 Module 5-Example 1 00:01:00 Module 6- Example 2 00:05:00 Module 7- Example 3 00:02:00 Module 8- What Is Voltage 00:07:00 Module 9- What Is Power 00:06:00 Module 10- What Is Energy 00:04:00 Module 11- Example 4 00:03:00 Module 12-Example 5 00:03:00 Module 13- Dependent and Independent Sources 00:05:00 Module 14- Example 6 Part 1 00:04:00 Module 15- Example 6 Part 2 00:01:00 Module 16- Application 1 Cathode Ray Tube 00:04:00 Module 17-Example 7 00:04:00 Module 18- Application 2 Electricity Bills 00:02:00 Module 19- Example 8 00:03:00 Unit 2- Basic Laws Module 1- Introduction to Basic Laws 00:01:00 Module 2- Definition of Resistance 00:06:00 Module 3- Ohm's Law 00:02:00 Module 4- Types of Resistances 00:06:00 Module 5- Open and Short Circuit 00:05:00 Module 6- Definition of Conductance 00:04:00 Module 7- Example 1 00:02:00 Module 8- Example 2 00:03:00 Module 9-Example 3 00:05:00 Module 10- Branch, Node and Loops 00:07:00 Module 11- Series and Parallel Connection 00:04:00 Module 12- KCL 00:04:00 Module 13- KVL 00:03:00 Module 14- Example 4 00:05:00 Module 15- Example 5 00:02:00 Module 16- Example 6 00:06:00 Module 17- Series Resistors and Voltage Division 00:07:00 Module 18-Parallel Resistors and Current Division 00:12:00 Module 19- Analogy between Resistance and Conductance 00:07:00 Module 20-Example 7 00:03:00 Module 21-Example 8 00:04:00 Module 22- Introduction to Delta-Wye Connection 00:06:00 Module 23-Delta to Wye Transformation 00:05:00 Module 24- Wye to Delta Transformation 00:07:00 Module 25-Example 9 00:03:00 Module 26- Example 10 00:15:00 Module 27- Application Lighting Bulbs 00:03:00 Module 28-Example 11 00:05:00 Unit 3- Methods of Analysis Module 1- Introduction to Methods of Analysis 00:02:00 Module 2- Nodal Analysis with No Voltage Source 00:15:00 Module 3-Example 1 00:04:00 Module 4-Cramer's Method 00:04:00 Module 5-Nodal Analysis with Voltage Source 00:07:00 Module 6- Example 2 00:02:00 Module 7- Example 3 00:13:00 Module 8-Mesh Analysis with No Current Source 00:10:00 Module 9-Example 4 00:04:00 Module 10- Example 5 00:06:00 Module 11-Mesh Analysis with Current Source 00:07:00 Module 12-Example 6 00:08:00 Module 13-Nodal Vs Mesh Analysis 00:04:00 Module 14-Application DC Transistor 00:04:00 Module 15-Example 7 00:04:00 Unit 4- Circuit Theorems Module 1-Introduction to Circuit theorems 00:02:00 Module 2-Linearity of Circuit 00:07:00 Module 3-Example 1 00:04:00 Module 4-Superposition Theorem 00:07:00 Module 5- Example 2 00:04:00 Module 6-Example 3 00:06:00 Module 7-Source Transformation 00:08:00 Module 8-Example 4 00:05:00 Module 9-Example 5 00:03:00 Module 10-Thevenin Theorem 00:10:00 Module 11-Example 6 00:06:00 Module 12-Example 7 00:05:00 Module 13- Norton's Theorem 00:05:00 Module 14-Example 8 00:04:00 Module 15-Example 9 00:05:00 Module 16-Maximum Power Transfer 00:05:00 Module 17-Example 10 00:03:00 Module 18-Resistance Measurement 00:05:00 Module 19-Example 11 00:01:00 Module 20-Example 12 00:04:00 Module 21-Summary 00:05:00 Unit 5- Operational Amplifiers Module 1-Introduction to Operational Amplifiers 00:03:00 Module 2-Construction of Operational Amplifiers 00:07:00 Module 3-Equivalent Circuit of non Ideal Op Amp 00:10:00 Module 4-Vo Vs Vd Relation Curve 00:04:00 Module 5-Example 1 00:09:00 Module 6-Ideal Op Amp 00:07:00 Module 7- Example 2 00:04:00 Module 8-Inverting Amplifier 00:05:00 Module 9-Example 3 00:02:00 Module 10-Example 4 00:02:00 Module 11-Non Inverting Amplifier 00:08:00 Module 12-Example 5 00:03:00 Module 13-Summing Amplifier 00:05:00 Module 14-Example 6 00:02:00 Module 15-Difference amplifier 00:06:00 Module 16-Example 7 00:08:00 Module 17-Cascaded Op Amp Circuits 00:06:00 Module 18-Example 8 00:04:00 Module 19-Application Digital to Analog Converter 00:06:00 Module 20-Example 9 00:04:00 Module 21-Instrumentation Amplifiers 00:05:00 Module 22-Example 10 00:01:00 Module 23-Summary 00:04:00 Unit 6- Capacitors and Inductors Module 1-Introduction to Capacitors and Inductors 00:02:00 Module 2-Capacitor 00:06:00 Module 3-Capacitance 00:02:00 Module 4-Voltage-Current Relation in Capacitor 00:03:00 Module 5-Energy Stored in Capacitor 00:06:00 Module 6-DC Voltage and Practical Capacitor 00:02:00 Module 7-Example 1 00:01:00 Module 8-Example 2 00:01:00 Module 9-Example 3 00:05:00 Module 10-Equivalent Capacitance of Parallel Capacitors 00:02:00 Module 11-Equivalent Capacitance of Series Capacitors 00:03:00 Module 12-Example 4 00:02:00 Module 13-Definition of Inductors 00:06:00 Module 14-Definition of Inductance 00:03:00 Module 15-Voltage-Current Relation in Inductor 00:03:00 Module 16-Power and Energy Stored in Inductor 00:02:00 Module 17-DC Source and Inductor 00:04:00 Module 18-Example 5 00:02:00 Module 19-Series Inductors 00:03:00 Module 20-Parallel Inductors 00:04:00 Module 21-Example 6 00:01:00 Module 22-Small Summary to 3 Basic Elements 00:02:00 Module 23-Example 7 00:05:00 Module 24-Application Integrator 00:05:00 Module 25-Example 8 00:03:00 Module 26-Application Differentiator 00:02:00 Module 27-Example 9 00:06:00 Module 28-Summary 00:05:00 Assignment Assignment - Electrical Engineering With Electric Circuits 00:00:00

This course aims to prepare individuals for the AWS Certified Solutions Architect Associate exam. It covers essential AWS services, cloud architecture design, deployment strategies, and best practices for managing various AWS components. Learning Outcomes: Understand the fundamental concepts of AWS Cloud Services and their application in real-world scenarios. Design and implement AWS Storage and Virtual Private Cloud (VPC) solutions. Learn how to design, implement, and manage Compute Services effectively. Master Identity and Access Management (IAM) and its best practices for secure access control. Explore Auto Scaling Solutions and Virtual Network Services to optimize AWS infrastructure. Gain proficiency in deploying applications and databases on AWS. Discover additional AWS services and their integration for comprehensive cloud solutions. Develop insights into achieving operational excellence with AWS services. Why buy this AWS Certified Solutions Architect Associate Preparation? 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 AWS Certified Solutions Architect Associate Preparation 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 AWS Certified Solutions Architect Associate Preparation 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 AWS Certified Solutions Architect Associate Preparation does not require you to have any prior qualifications or experience. You can just enrol and start learning.This AWS Certified Solutions Architect Associate Preparation 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 AWS Certified Solutions Architect Associate Preparation is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Section 01: Introduction Introduction 00:03:00 Section 02: Exam Tips and Tricks What is AWS? 00:02:00 Why use AWS? 00:03:00 How to Get Started with AWS 00:04:00 AWS Certifications 00:04:00 Preparation Resources 00:02:00 Benefits of Certification 00:02:00 AWS CSA-A Overview 00:04:00 What's New on the 2020 Updated Exam? 00:03:00 AWS CSA-A Exam Objectives 00:06:00 The Four Key Areas (Compute, Networking, Storage, and Databases) 00:04:00 Master the Knowledge Areas 00:02:00 Use the System 00:05:00 Take Notes 00:03:00 Be Mentally and Physically Prepared 00:04:00 Take the Exam 00:04:00 Section 03: AWS Cloud Services Overview Cloud Computing Defined 00:08:00 Benefits of Cloud Computing 00:10:00 Cloud Computing Models 00:07:00 History 00:07:00 Platform 00:06:00 Services, Part 1 00:10:00 Services, Part 2 00:08:00 Security and Compliance 00:07:00 Regions and Availability 00:06:00 Section 04: AWS Storage Design Storage Services 00:07:00 S3 Storage Class 00:07:00 S3 Terminology 00:09:00 S3 Advanced Features 00:08:00 Creating S3 Buckets Lab 00:08:00 S3 Bucket Properties 00:08:00 S3 Managing Objects Lab 00:11:00 Glacier 00:07:00 Setting up a Glacier Vault Lab 00:08:00 S3 and Tape Gateway 00:06:00 S3 Enhanced Features 00:08:00 Elastic Block Store (EBS) 00:08:00 Creating EBS Volumes Lab 00:07:00 Elastic File System (EFS) 00:07:00 Creating an EFS File System Lab 00:07:00 EFS and PrivateLink 00:03:00 Intro to Amazon FSx 00:06:00 Hands-on with FSx 00:06:00 Integrating on-Premises Storage 00:07:00 Storage Access Security Lab 00:10:00 Storage Performance 00:08:00 Section 05: Virtual Private Cloud (VPC) Virtual Private Cloud (VPC) Overview 00:10:00 Creating a VPC Lab 00:11:00 Configuring DHCP Options Lab 00:04:00 Elastic IP Addresses 00:07:00 Elastic Network Interfaces (ENIs) 00:05:00 Endpoints 00:07:00 VPC Peering 00:08:00 Creating a VPC Peering Connection Lab 00:10:00 Security Groups Overview 00:07:00 Network Address Translation (NAT) 00:11:00 Gateways (VPGs and CGWs) 00:08:00 VPN Configuration Option 00:04:00 Section 06: Compute Services Design EC2 Overview 00:11:00 EC2 Instance Types 00:11:00 EC2 Pricing 00:13:00 EBS and EC2 00:05:00 Section 07: Compute Services Implementation Launching an EC2 Linux Instance Lab 00:13:00 Configuring an EC2 Linux Instance Lab 00:08:00 Setting up an EC2 Windows Instance Lab 00:12:00 Shared Tenancy 00:05:00 Dedicated Hosts 00:08:00 Dedicated Instances 00:06:00 AMI Virtualization 00:12:00 Section 08: Compute Services Management Instance Management 00:09:00 Connecting to Instances Lab 00:09:00 Working with Security Groups 00:10:00 Working with Security Groups Lab 00:10:00 Advanced EC2 Management 00:06:00 AWS Batch 00:06:00 Elastic Container Service (ECS) 00:08:00 Elastic Beanstalk Environment 00:11:00 Section 09: Identity and Access Management (IAM) Identity and Access Management (IAM) Overview 00:07:00 Principals 00:10:00 Root User 00:06:00 Authentication 00:06:00 Authorization Policies 00:13:00 Multi-Factor Authentication 00:08:00 Key Rotation 00:10:00 Multiple Permissions 00:06:00 AWS Compliance Program 00:07:00 AWS Security Hub 00:06:00 Shared Responsibility Models 00:06:00 Section 10: IAM Best Practices User Accounts 00:11:00 Password Policies 00:09:00 Credential Rotation 00:06:00 Principle of Least Privilege 00:05:00 IAM Roles 00:08:00 Policy Conditions 00:08:00 CloudTrail 00:12:00 Section 11: Auto Scaling Solutions Auto Scaling Overview 00:06:00 Auto Scaling Groups 00:04:00 Termination Policies 00:07:00 Auto Scaling Configuration Lab 00:13:00 Launch Methods 00:04:00 Load Balancer Concepts 00:08:00 Elastic Load Balancing (ELB) 00:10:00 Section 12: Virtual Network Services DNS 00:14:00 Configuring DNS Lab 00:07:00 Configuring Route 53 Lab 00:13:00 Configuring ACLs and NACLs Lab 00:09:00 Flow Logs 00:07:00 Section 13: AWS Application Deployment Application and Deployment Services 00:04:00 Lambda 00:06:00 API Gateway 00:09:00 Kinesis 00:06:00 Kinesis Data Streams and Firehose 00:06:00 Kinesis Data Analytics 00:04:00 Reference Architectures 00:06:00 CloudFront 00:10:00 Web Application Firewall (WAF) 00:09:00 Simple Queue Service (SQS) 00:10:00 Simple Notification Service (SNS) 00:08:00 Simple Workflow (SWF) 00:07:00 Step Functions 00:05:00 OpsWorks 00:08:00 Cognito 00:04:00 Elastic MapReduce (EMR) 00:05:00 CloudFormation 00:10:00 CloudFormation Properties 00:03:00 CloudWatch 00:06:00 Trusted Advisor 00:07:00 Organizations 00:09:00 Section 14: AWS Database Design Database Types 00:08:00 Relational Databases 00:08:00 Database Hosting Methods 00:05:00 High Availability Solutions 00:06:00 Scalability Solutions 00:06:00 Database Security 00:08:00 Aurora 00:06:00 Redshift 00:11:00 DynamoDB 00:10:00 Section 15: Database Deployment DynamoDB Tables Lab 00:08:00 MySQL Lab 00:13:00 Configuration Lab 00:13:00 Backups Lab 00:04:00 Restore Lab 00:04:00 Snapshot Lab 00:08:00 Monitoring Lab 00:06:00 Section 16: Additional AWS Services Media Content Delivery 00:13:00 Desktop and Appstreaming 00:06:00 ElastiCache 00:05:00 Security Services Lab 00:12:00 Analytics Engines 00:11:00 Development Operations (DevOps) 00:12:00 AWS Solutions 00:05:00 AWS Transit Gateway 00:03:00 AWS Backup 00:04:00 AWS Cost Explorer 00:04:00 Section 17: Operational Excellence with AWS The Operational Excellence Process 00:08:00 Widget Makers Scenario 00:06:00 Resilient Design 00:08:00 Resilient Design Scenario 00:05:00 Performant Design 00:09:00 Performant Design Scenario 00:06:00 Secure Design 00:08:00 Secure Design Scenario 00:05:00 Cost Optimization 00:07:00 Cost Optimization Scenario 00:05:00 General Best Practices 00:07:00

Dive into the realm of effective management with our course, 'Essentials of Becoming a Manager and Managing Teams'. Navigate the challenges of stepping into a managerial role in 'New Manager Mastery'. Discover the art of selecting the perfect candidate for your team in 'A Masterclass in Hiring For Your Teams' and foster an environment that promotes continuous growth with 'Creating a Culture of Learning in Your Organisation'. Equip yourself with top-tier interview techniques, manage stress, lead with conviction, conduct impactful meetings, and delve into the intricacies of advanced team management. Learning Outcomes Understand the transition intricacies and best practices of becoming a new manager. Master the strategies to effectively hire and onboard team members. Establish and nurture a growth-oriented learning culture within the organisation. Acquire techniques to proficiently manage stress, ensuring personal and team well-being. Lead meetings effectively and gain insights into advanced aspects of team leadership. Why buy this Essentials of Becoming a Manager and Managing Teams 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 Essentials of Becoming a Manager and Managing Teams 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 Essentials of Becoming a Manager and Managing Teams course for? Aspiring managers looking to transition into leadership roles seamlessly. Team leaders seeking to refine their hiring and team-building strategies. Organisational heads aiming to instil a robust learning culture. Professionals wanting to enhance their interview conduction skills. Individuals eager to develop leadership attributes and manage workplace stress. Prerequisites This Essentials of Becoming a Manager and Managing Teams does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Essentials of Becoming a Manager and Managing Teams 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 Manager: £30,000 - £45,000 Team Leader: £25,000 - £35,000 HR Specialist: £28,000 - £40,000 Training and Development Officer: £27,000 - £37,000 Stress Management Consultant: £35,000 - £50,000 Meetings Coordinator: £22,000 - £30,000 Course Curriculum Section 01: New Manager Mastery What to Expect & About Me 00:02:00 Well Done! 00:02:00 It's a Different Job 00:03:00 You're Scared? Good! 00:02:00 The Manager Mindset 00:03:00 Set Expectations 00:04:00 Get Learning Leadership! 00:03:00 What is Culture? 00:02:00 Culture of Fairness 00:08:00 People Are Crazy 00:02:00 Mastering One To One Meetings 00:05:00 Manager to Coach 00:07:00 Me? A Psychologist? 00:02:00 Emotional Intelligence 00:05:00 Mastering Performance Evaluations 00:07:00 Welcoming New Starters 00:07:00 How to Hire a Diverse Workforce 00:06:00 Get the Basics Right Every Time 00:02:00 Conclusion 00:02:00 Section 02: A Masterclass In Hiring For Your Teams Introduction 00:02:00 The Importance of the Interview Process 00:03:00 What's Wrong With The Interview Process 00:07:00 Human Vs. Algorithm 1 00:02:00 Unconscious Interviewer Biases 00:03:00 Be Data Driven 00:03:00 Let's See You In Action 00:02:00 Keep it Relevant 00:02:00 Seriously Avoid These Topics 00:03:00 Benefits of a Diverse Workforce 00:03:00 How to Hire a Diverse Workforce 00:06:00 Hunger and Desire Over Skills 00:03:00 Culture Addition, Not Culture Fit 00:03:00 Compliment, Backup or Add To The Team 00:05:00 Hiring The Right Rank 00:04:00 What Are Your Location Restrictions 00:01:00 The Risk Of Delivering Less 00:02:00 The Risk Of Burnout 00:02:00 The Risk Of Losing The Headcount 00:02:00 Confirm The Job Is What They Think It Is 00:02:00 Sell Yourself, The Team & The Company 00:03:00 Don't Be A Jerk 00:02:00 It's Not About Making You Look Good 00:02:00 Managing Recruitment Agencies 00:04:00 Communications With The Candidate 00:03:00 Be Ready 00:03:00 Assign A Buddy 00:02:00 You Got It Wrong! 00:02:00 Conclusion 00:03:00 Section 03: Creating a Culture Of Learning In Your Organization What To Expect From The Course 00:02:00 Why Even Bother? 00:07:00 What Learning Used To Be Like 00:02:00 Individual Learner 00:01:00 Everyone Says They Are A Self-Learner 00:10:00 Online Communities 00:02:00 Embedding Learning Into Daily Life 00:03:00 Teach It To Learn It 00:02:00 Learning New Vs Becoming Expert 00:01:00 Listen Faster 00:01:00 Tom's Approach 00:02:00 The Paid Training Course Is History 00:03:00 Develop The Right Mental Habits 00:06:00 Make It Easier On Yourself 00:02:00 Manager Responsibilities 00:01:00 Are You Really A Centre Of Excellence? 00:02:00 Learning As Part Of Performance Evaluation 00:02:00 Don't Worry If People Leave 00:01:00 The Benefits Of A Skilled User Base 00:02:00 Push It, Push It, Push It 00:01:00 The Golden Hour 00:09:00 Internal Mobility 00:03:00 Team Secondment 00:02:00 Make People Good Enough To Quit 00:03:00 Lunch & Learns 00:02:00 Personalised Learning Plans 00:02:00 Use It Or Lose It 00:02:00 Certifiably Great 00:02:00 Get A Learning Buddy 00:02:00 Senior Leaders Responsibilities 00:01:00 Walk It From The Top 00:02:00 Learning Wherever, Whenever 00:02:00 Show Us The Data 00:02:00 Create Learning Champions 00:02:00 Gamification 00:02:00 Sell, Sell, Sell 00:03:00 Don't Block The Useful Sites 00:03:00 Create A Varied Library Of Resources 00:02:00 Encourage Conferences And Seminars 00:04:00 The Feedback Loop 00:02:00 Learning Is Expensive 00:03:00 Learning Takes Time 00:02:00 Concluding Remarks 00:04:00 Section 04: Job Interview Practicals: Complete Interview Skills Training Interview Masterclass - Maximize Your Chances of Success 00:03:00 Get Yourself Game Ready! 00:08:00 Final Prep Tips! 1-2 Days Before Interview 00:03:00 It's Interview Day! How to ACE the interview! 00:07:00 Positive Personality Traits Interviewers Are Desperate For 00:07:00 How to Engage & Impress in Conversation 00:05:00 The Art of Asking Questions 00:08:00 How to Answer Questions Using Skill & Psychological Tricks 00:10:00 After Interview - Still More You Can Do 00:05:00 Staying Professional 00:02:00 That Wasn't What You Expected!! 00:03:00 Waiting For The Answer 00:02:00 Let's Review The Tips - So Much To Consider 00:04:00 Section 05: Beating Burnout - Practical & Complete Stress Management Introduction to Overcoming Burnout 00:03:00 The Problem of Burnout in Modern Workplaces 00:06:00 Symptoms of Burnout - Do You Have It? 00:03:00 How Individuals Can Fight Back Against Burnout 00:12:00 How Leaders Can Create A Burnout Free Culture 00:10:00 Let's Review The Advice - What's Important? 00:03:00 Section 06: Practical Leadership: Complete Guide To Great Leadership Characteristics of Great Leaders - Introduction 00:02:00 Honesty, Coaching & The Mission 00:08:00 Empathy, Togetherness & Feedback 00:06:00 Change, Authenticity & Enablement 00:05:00 Perspective, Empowerment & Emotional Intelligence 00:05:00 My Favourite Tips & Bonus! 00:05:00 Section 07: Mastering Meetings - A Complete Practical Guide To Meetings What to Expect 00:04:00 What & Why? 00:03:00 The Cost of Meetings 00:02:00 Red Amber & Green Meetings 00:06:00 Do You Need A Meeting? 00:04:00 Timing Considerations 00:07:00 Contents of the Invite 00:03:00 Scheduling Etiquette 00:07:00 Considerations for Externals 00:05:00 The Meeting as an Opportunity 00:02:00 Chairing Meetings 00:01:00 Your Agenda 00:08:00 Kicking Off 00:06:00 During The Meeting 00:08:00 After Meeting Minutes 00:06:00 Considerations 00:06:00 Concluding Remarks 00:02:00 Section 08: Advanced People Management - What They Don't Tell You About Team Leading! What To Expect & About Me 00:02:00 Management Vs. Coaching 00:07:00 What Is Culture? 00:02:00 Culture of Fairness 00:08:00 How To Be A Great Listener 00:05:00 Mastering Performance Evaluations 00:07:00 Welcoming New Starters 00:07:00 Stupid Things NOT To Say To Your Team 00:07:00 Your Team Made A Mistake 00:06:00 Mastering Crucial Conversations 00:05:00 When to Fire Someone 00:06:00

The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? 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 Complete Python Machine Learning & Data Science Fundamentals 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 Complete Python Machine Learning & Data Science Fundamentals 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 Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals 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 Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00

Are you embarking on the journey of mastering data analytics and visualisation in the UK? The 'Advanced Diploma in Microsoft Power BI 2021 at QLS Level 7' is your beacon. Positioned to illuminate the intricate realm of Power BI, this course offers a comprehensive look into the foundational aspects and the advanced features that make Microsoft's tool a standout. With sections meticulously designed ranging from the fundamentals, like data transformation, to advanced concepts, such as integrating Power BI with Python and storytelling with data, this course ensures learners grasp the complete spectrum. With the rising emphasis on data analytics in today's business world, this course acquaints you with Power BI's prowess. It prepares you for the sought-after Microsoft Power BI certification in the UK. Learning Outcomes Comprehend the fundamental aspects of Power BI, from initiating a project to understanding the user interface. Develop proficiency in advanced data transformation techniques and data model creation. Integrate Python with Power BI and harness the benefits of both for enhanced data analytics. Master the art of 'Storytelling with Data' to deliver impactful presentations and reports. Understand and implement Row-Level Security and harness Power BI Cloud services efficiently. Why choose this Advanced Diploma in Microsoft Power BI 2021 at QLS Level 7? 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 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 Advanced Diploma in Microsoft Power BI 2021 at QLS Level 7 for? Individuals keen on obtaining the Microsoft power bi certification UK. Analysts and data professionals aspiring to enhance their data visualisation skills. Business professionals wanting to leverage Power BI for insightful business decision-making. Tech enthusiasts aiming to amalgamate programming (Python) with data analytics. Those seeking to stay updated with the latest trends in Power BI and its evolving capabilities. Career path Data Analyst: Average Salary £30,000 - £40,000 Annually Business Intelligence Developer: Average Salary £35,000 - £45,000 Annually Power BI Developer: Average Salary £40,000 - £50,000 Annually Data Visualisation Specialist: Average Salary £32,000 - £42,000 Annually Business Intelligence Manager: Average Salary £45,000 - £55,000 Annually Data Strategy Consultant: Average Salary £50,000 - £60,000 Annually Prerequisites This Advanced Diploma in Microsoft Power BI 2021 at QLS Level 7 does not require you to have any prior qualifications or experience. You can just enrol and start learning. This 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. 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 £135 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 Section 01: Introduction Welcome! 00:01:00 What is Power BI? 00:03:00 Download & Installing Power BI Desktop 00:04:00 Getting to know the interface 00:03:00 Mini Project: Transform Data 00:07:00 Mini Project: Visualize Data 00:05:00 Mini Project: Creating a Data Model 00:07:00 Course Outline: What will you learn in this course? 00:05:00 How to learn best with this course? 00:03:00 Section 02: Preparing our Project Creating our initial project file 00:04:00 Working with the attached project files 00:04:00 Section 03: Data Transformation - The Query Editor Exploring the Query Editor 00:06:00 Connecting to our data source 00:07:00 Editing rows 00:08:00 Changing data types 00:08:00 Replacing values 00:03:00 Close & Apply 00:03:00 Connecting to a csv file 00:03:00 Connecting to a web page 00:05:00 Extracting characters 00:06:00 Splitting & merging columns 00:09:00 Creating conditional columns 00:06:00 Creating columns from examples 00:09:00 Merging Queries 00:17:00 Pivoting & Unpivoting 00:06:00 Appending Queries 00:08:00 Practice & Solution: Population table 00:15:00 The Fact-Dimension-Model 00:09:00 Practice: Load the dimension table 00:04:00 Organizing our queries in groups 00:03:00 Entering data manually 00:05:00 Creating an index column 00:03:00 Workflow & more transformations 00:05:00 Module summary 00:05:00 Exercise 1 - Instruction 00:02:00 Exercise Solution 00:11:00 Section 04: Data Transformation - Advanced Advanced Editor - Best practices 00:09:00 Performance: References vs. Duplicating 00:10:00 Performance: Enable / Disable Load & Report Refresh 00:05:00 Group by 00:05:00 Mathematical Operations 00:05:00 Run R Script 00:15:00 Using Parameters to dynamically transform data 00:06:00 M formula language: Basics 00:07:00 M formula language: Values, Lists & Tables 00:14:00 M formula language: Functions 00:13:00 M formula language: More functions & steps 00:05:00 Exercise 2 - Instructions 00:01:00 Exercise 2 - solution 00:05:00 Section 05: Creating a Data Model Understanding the relationship 00:05:00 Create & edit relationships 00:06:00 One-to-many & one-to-one relationship 00:06:00 Many-to-many (m:n) relationship 00:08:00 Cross filter direction 00:06:00 Activate & deactivate relationships 00:06:00 Model summary 00:03:00 Exercise 3 Create Model 00:03:00 Exercise 3 Solution 00:02:00 Section 06: Data Visualization Our first visual 00:08:00 The format tab 00:12:00 Understanding tables 00:10:00 Conditional formatting 00:09:00 The Pie Chart 00:06:00 All about the filter visual 00:13:00 The filter pane for developers 00:09:00 Cross filtering & edit interactions 00:04:00 Syncing slicers across pages 00:07:00 Creating drill downs 00:08:00 Creating drill throughs 00:07:00 The tree map visual 00:07:00 The decomposition tree 00:05:00 Understanding the matrix visual 00:05:00 Editing pages 00:07:00 Buttons & Actions 00:09:00 Bookmarks to customize your report 00:10:00 Analytics and Forecasts with line charts 00:10:00 Working with custom visuals 00:07:00 Get data using R Script & R Script visual 00:08:00 Asking questions - Q&A visual 00:04:00 Wrap up - data visualization 00:08:00 Section 07: Power BI & Python Python in Power BI - Plan of attack 00:03:00 Setting up Python for Power BI 00:03:00 Transforming data using Python 00:11:00 Creating visualizations using Python 00:08:00 Violin plots, pair plots & ridge plots using Python 00:15:00 Machine learning (BayesTextAnalyzer) using Python 00:00:00 Performance & Troubleshooting 00:03:00 Section 08: Storytelling with Data Introduction 00:01:00 Show Empathy & Identify the Requirement 00:03:00 Finding the Most Suitable KPI's 00:02:00 Choose an Effective Visualization 00:04:00 Make Use of Natural Reading Pattern 00:03:00 Tell a Story Using Visual Cues 00:05:00 Avoid Chaos & Group Information 00:02:00 Warp Up - Storytelling with Data 00:02:00 Section 09: DAX - The Essentials Introduction 00:03:00 The project data 00:04:00 Measures vs. Calculated Columns 00:15:00 Automatically creating a date table in DAX 00:08:00 CALENDAR 00:05:00 Creating a complete date table with features 00:04:00 Creating key measure table 00:03:00 Aggregation functions 00:06:00 The different versions of COUNT 00:14:00 SUMX - Row based calculations 00:09:00 Section 10: DAX - The CALCULATE function CALCULATE - The basics 00:11:00 Changing the context with FILTER 00:07:00 ALL 00:08:00 ALL SELECTED 00:03:00 ALL EXCEPT 00:07:00 Section 11: Power BI Service - Power BI Cloud How to go on now? 00:03:00 Power BI Pro vs Premium & Signing up 00:04:00 Exploring the interface 00:04:00 Discovering your workspace 00:03:00 Connecting Power BI Desktop & Cloud 00:04:00 Understanding datasets & reports 00:03:00 Working on reports 00:04:00 Updating reports from Power BI Desktop 00:04:00 Creating and working with workspaces 00:07:00 Installing & using a data gateway 00:13:00 Get Quick Insights 00:03:00 Creating dashboards 00:04:00 Sharing our results through Apps 00:10:00 Power BI Mobile App 00:05:00 Creating the layout for the Mobile App 00:04:00 Wrap up Power BI Cloud 00:07:00 Section 12: Row-Level Security Introduction 00:03:00 Creating a Row-Level Security 00:05:00 Row-Level Security in the Cloud 00:04:00 Row-Level Security & Data Model 00:05:00 Dynamic Row-Level Security 00:07:00 Dynamic Many-to-Many RLS 00:04:00 Hierarchical Row-Level Security 00:13:00 Section 13: More data sources JSON & REST API 00:10:00 Setting up a local MySQL database 00:14:00 Connecting to a MySQL database in Power BI 00:05:00 Connecting to a SQL database (PostgreSQL) 00:05:00 Section 14: Next steps to improve & stay up to date Congratulations & next steps 00:06:00 The End 00:01:00 Resources Resources - Advanced Diploma in Microsoft Power BI 2021 at QLS Level 7 00:00:00 Assignment Assignment - Advanced Diploma in Microsoft Power BI 2021 at QLS Level 7 04:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00

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

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