4757 Model courses delivered Online

Effecting Business Process Improvement: In-House Training Business analysts facilitate the solution of business problems. The solutions are put into practice as changes to the way people perform in their organizations and the tools they use. The business analyst is a change agent who must understand the basic principles of quality management. This course covers the key role that business analysts play in organizational change management. What you will Learn You will learn how to: Define and document a business process Work with various business modeling techniques Perform an enterprise analysis in preparation for determining requirements Analyze business processes to discern problems Foundation Concepts Overview of business analysis and process improvement Defining the business process Introducing the proactive business analyst Focusing on business process improvement for business analysts Launching a Successful Business Process Improvement Project Overview of the launch phase Understanding and creating organizational strategy Selecting the target process Aligning the business process improvement project's goals and objectives with organizational strategy Defining the Current Process Overview of current process phase Documenting the business process Business modeling options: work-flow models Business modeling options: Unified Modeling Language (UML) model adaptations for business processes Analyzing the Current Process Process analysis overview Evaluation: establishing the control group Opportunity techniques: multi-discipline problem-solving Opportunity techniques: matrices Building and Sustaining a Recommended Process Overview of the recommended process and beyond Impact analysis Recommended process Transition to the business case Return to proactive state

This course covers the fundamental topics of statistical methodology, enabling you to understand the application and interpretation of linear and non-linear regression modeling.

Learn to use cutting-edge language models ChatGPT, Dalle-2, and Midjourney to create high-quality written content and generative art in this course. Discover how to fine-tune these models for specific tasks and explore the ethical implications and future-proofing strategies for using AI in your work.

Are you a teacher that wants to improve your classroom environment? Do you have questions about supporting children with their behaviour, communication and relationships? Book a 30 minute / 1 hour 1:1 coaching call. My coaching model will provide you with practical solutions to help you create a calm classroom environment. Discounts offered to subscribers.

3G training course description This course is designed to give the delegate an understanding of the technologies used within a 3G UMTS mobile network. During the course we will investigate the UMTS air interface and the use of Wideband-Code Division Multiple Access (WCDMA) to facilitate high speed data access, together with HSPA to offer mobile broadband services. We will describe the use of soft handover rather than hard handover procedures and soft capacity sharing. The course includes a brief exploration of the UMTS protocol stack and the use of PDP Context and QoS support features. What will you learn Explain the 3G UMTS architecture. Describe the role of a Drifting & Serving RNC. Explain the use of ARQ & HARQ for mobile broadband. Describe how IMS integrates into the architecture. Describe the use of Media Gateway Controllers. Identify the temporary identities used within 3G UMTS. 3G training course details Who will benefit: Anyone working within the telecommunications area, especially within the mobile environment. Prerequisites: Mobile communications demystified Telecommunications Introduction Duration 2 days 3G training course contents D3GPP specifications 3GPP standards body, Evolution path, Frequency and bandwidth, Conceptual model, UMTS general architecture, UTRAN architecture & radio access bearer. CDMA principles CDMA principle, Code characteristics, Code requirements. CDMA requirements Synchronization, Power control, Soft handover, Rake receiver, Antenna consideration, Multi-user detection. Radio interface protocol architecture Access stratum & non-access stratum, Overall protocol structure, Logical and transport channels, Physical channels, Protocol termination. Layer 2 Protocols Medium Access Control (MAC) Protocol, Radio Link Control (RLC) Protocol, Packet Data Convergence Protocol (PDCP) protocol, Radio Interface for Broadcast/Multicast Services. Radio Resource Control (RRC) Protocol RRC Architecture, RRC Protocol State, Broadcast of information, RRC connection management, Radio bearer management, RRC connection mobility functions, Power control, Ciphering and Integrity. Mobile procedures Mobility management states and transitions, UMTS identities, Procedures in Idle mode (location updates, cell selection/ re-selection), Circuit-switched call set-up, Packet-switched context activation and context preservation, Data transfer initialization, Soft-handover procedure. Introduction to HSPA The need for high speed data, Fast HARQ, Improved scheduling, Additional channels, Soft combining, HS-DSCH codes, Uplink HSPA vs downlink HSPA, Full HSPA, Use of MIMO, Enhanced CELL_FACH.

This comprehensive course will help you learn the basics to advanced mechanisms of chatbot development using deep learning with Python. This course is a complete package for beginners to learn chatbot fundamentals with deep learning and its applications and build it from scratch using deep learning (RNNs) with Python.

Duration 3 Days 18 CPD hours This course is intended for Mid- and upper-level managers looking to hone their senior management skills will benefit from this course. Overview Use wisdom and understanding to lead others Deliver constructive critiques to your staff More effectively coach and mentor your staff Develop new managers Better motivate your staff Navigate organizational politics To maximize performance and foster a healthy environment, senior managers must also motivate others, nurturing and utilizing the distinctive skills of each team member. Private classes on this topic are available. We can address your organization?s issues, time constraints, and save you money, too. Contact us to find out how. Prerequisites While there are no prerequisites for this course, please ensure you have the right level of experience to be successful in this training. 1. Leading Others Comparing Vertical and Lateral Hierarchies- Leading in Different Structures of Command and Control Leveraging Your Organization\'s Structure Knowing Your Employees - Developing Empathy Beginning with the End in Mind and Back-Casting to the Desired Future State Setting S.M.A.R.T. Goals Earning Your Team\'s Trust with Honesty, Reliability, Availability, and Openness 2. Strategies for Course Correction Lighting a Fire- Motivating, Guiding, and Inspiring Resolving Conflict- Forcing, Accommodating, Avoiding, Compromising, and Collaborating Changing the Script- Trusting Your Team, Empowering Delegation, Celebrating Success, and Building & Reinforcing Your Team Defining Team Roles and Creating a Balanced Team 3. Effective Coaching and Mentoring Providing Clear and Timely Feedback Creating a Supportive Environment Building a Mentorship Plan - Mentoring for Success 4. Training New Managers Preparing, Developing, and Supporting New Managers Defining and Building Competencies Documenting Best Practices while Rewarding and Emulating Effective Managers Encouraging a Peer Network 5. Motivation Understanding the 8 Level Hierarchy of Needs Managing Across Generations Applying the CARE Model 6. Organizational Politics Being Politically Savvy, Ethical, and Effective Building Political Intelligence Understanding the Landscape 7. The Big Picture Thinking Through the Elements of Management

HTTP streaming training course description This course looks at the delivery of video streams using HTTP adaptive streaming. Both MPEG DASH and HLS are investigated. Hands on sessions primarily involve using Wireshark to analyse streams. What will you learn Use Wireshark to analyse and troubleshoot HTTP video streams. Explain HTTP adaptive streaming works. Evaluate and compare MPEG DASH and HLS. Use tools to create HTTP adaptive streams. HTTP streaming training course details Who will benefit: Anyone working in the broadcast industry. Prerequisites: TCP/IP foundation for engineers Duration 2 days HTTP streaming training course contents What is HTTP streaming? The old way. Progressive downloads versus streaming. Why not UDP and RTP for delivery? Adaptive bit rate streaming. Standards. Hands on Base network setup. Using WireShark for HTTP streams. HTTP protocol stack IP, TCP, IPv6. HTTP. HTTP 1.0, HTTP 1.1, HTTP 2.0, HTTP header fields. HTML 5. Hands on Analysing HTTP. Adaptive bitrate streaming principles Chunks, fragments, segments. Manifest files. Encoding, resolution, bitrates. Addressing, relative and absolute URLs, redirection. When does the client switch streams? Switch points. Hands on Walk through of client behaviours on a stream. HTTP streaming architecture Server components, distribution components, client software. CDN, caching, multiple servers. Hands on Analysing CDN and Internet delivery. TCP and HTTP streaming interactions TCP ACK, TCP connections, unicast only. TCP flow control, TCP and performance. Hands on TCP window sizes. MPEG DASH Stakeholders, DASH architecture and model, codec agnostic, XML, Media Presentation Description, Media Presentation, segment formats. Hands on MPEG DASH analysis. HTTP Live Streaming and others Stakeholders. Media segments, media playlists, master playlists. Adobe HTTP dynamic streaming, Microsoft smooth streaming. Hands on Analysing HLS. Tools mp4dash, mp4fragment, libdash. Apple developer tools for HLS. Hands on Creating segmented content. Security HTTPS, encryption, content protection. Hands on Encryption analysis. Summary Choosing a streaming method. Impact of live versus VoD. Web sockets.

Learn how to create a supportive and open environment for mental health and wellbeing at work, helping everyone thrive and perform at their best.

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