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Mergers and Acquisitions - Virtual Learning Why Attend This practical course covers the key steps in the Mergers and Acquisitions(M&A) process, from the initial step of valuing the shares in a company through to closing the deal. Whether or not participants practice M&A, this course will provide them an insider's look into what is an undeniable major force in today's corporate arena. This course will give participants an A-Z understanding of the M&A process and the ability to evaluate whether a merger or acquisition fits with their organization's strategy. As a result they will identify the most lucrative M&A opportunities, select the best partners and get the maximum reward from the deal. Course Methodology In this interactive training course participants will frequently work in pairs as well as in larger groups to complete exercises, and regional and international case studies. Course Objectives By the end of the course, participants will be able to: Identify attractive Mergers and Acquisitions (M&A) opportunities Formulate the initial steps and the preliminary agreements for a merger or acquisition Carry out a full due diligence into the state of affairs of a target company Understand the Share Purchase Agreement (SPA) and the Asset Purchase Agreement (APA) Take an active role in the exchange and completion stages of a merger or acquisition Be an effective part of the post-merger integration to ensure the smooth running of the new organization Target Audience This course is suitable for anyone involved in the identification, planning and execution of a Mergers and Acquisitions opportunity. This includes, CEOs, managing directors, general managers, financial directors, accountants, board members, commercial directors, business development directors, strategy planners and analysts, and in-house council. Target Competencies Identifying M&A opportunities Due Diligence Organizing Acquisitions Structuring Negotiations Post-acquisition Integration Post-acquisition Audit Note The Dubai Government Legal Affairs Department has introduced a Continuing Legal Professional Development (CLPD) programme to legal consultants authorised to practise through a licensed firm in the Emirate of Dubai. We are proud to announce that the Dubai Government Legal Affairs Department has accredited EMG Associates as a CLPD provider. In addition, all our legal programmes have been approved. This PLUS Specialty Training Legal course qualifies for 4 elective CLPD points. Fundamentals of Mergers and Acquisitions ( M&A) Distinction between Mergers and Acquisitions Types of Mergers & Acquisitions Horizontal Vertical conglomerate Knowledge of areas of law required in M& A The Preliminary documents required in M&A Heads of terms- legally binding? Confidentiality - do they need to be in writing? Lockout/exclusivity agreements- requirements for enforceability How to structure the Acquisition Share sale Advantages and disadvantages from the buyer's perspective Advantages and disadvantages from the seller's perspective Business sale Advantages and disadvantages from the buyer's perspective Advantages and disadvantages from the seller's perspective Hive down A combination of assert sale and share sale Looking at different valuation techniques Real Estate Value Relief from Royalty Discounted Cash Flow Market Multiples Dividend Yield Net Assets The Due Diligence Process What is it? Why do it? Scope of due diligence Legal Financial  Commercial Operational The Purchase Agreements Share Sale Purchase Agreement v Asset Purchase Agreement v Business Purchase Agreements Provisions in a Share Purchase Agreement Importance of warranties and indemnities in purchase agreements Negotiating warranties from a Share Purchase Agreement Contractual protection for the seller  Disclosure letter Intellectual property What happens to IP in M&A  Stages of the IP during the M&A process Dispute Resolution in M&A Litigation Arbitration Mediation The Exchange and completion stages of M&A Seller's document Buyer's document The auction process The relevant stages Advantages and disadvantages from the buyer's and the seller's perspective

This course provides a deep dive into how learning needs can be identified at both levels, evaluating ways in which people learn, and the importance of managing knowledge and transferring information within an organization. By the end of this course, you'll be equipped with the skills to develop effective learning strategies and improve performance in both individuals and organizations. After the successful completion of the course, you will be able to learn about the following, Different perspectives of learning at the organisational and individual levels. Understand how to identify the learning needs of the organisation and the individual. Evaluate different ways in which people learn. How knowledge can be managed by an organisation and how information can be transferred successfully within it. This course provides a deep dive into how learning needs can be identified at both levels, evaluating ways in which people learn, and the importance of managing knowledge and transferring information within an organization. By the end of this course, you'll be equipped with the skills to develop effective learning strategies and improve performance in both individuals and organizations. This course provides a deep dive into how learning needs can be identified at both levels, evaluating ways in which people learn, and the importance of managing knowledge and transferring information within an organization. By the end of this course, you'll be equipped with the skills to develop effective learning strategies and improve performance in both individuals and organizations. VIDEO - Course Structure and Assessment Guidelines Watch this video to gain further insight. Navigating the MSBM Study Portal Watch this video to gain further insight. Interacting with Lectures/Learning Components Watch this video to gain further insight. Organisational Learning and Knowledge Management Self-paced pre-recorded learning content on this topic. Organisational Learning and Knowledge Management Put your knowledge to the test with this quiz. Read each question carefully and choose the response that you feel is correct. All MSBM courses are accredited by the relevant partners and awarding bodies. Please refer to MSBM accreditation in about us for more details. There are no strict entry requirements for this course. Work experience will be added advantage to understanding the content of the course. The certificate is designed to enhance the learner's knowledge in the field. This certificate is for everyone eager to know more and get updated on current ideas in their respective field. We recommend this certificate for the following audience. CEO, Director, Manager, Supervisor Human resource professionals and managers Trainers and facilitators Organizational development consultants Business owners and entrepreneurs Educators and trainers Average Completion Time 2 Weeks Accreditation 3 CPD Hours Level Advanced Start Time Anytime 100% Online Study online with ease. Unlimited Access 24/7 unlimited access with pre-recorded lectures. Low Fees Our fees are low and easy to pay online.

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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

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This course is for new entrants to the gas industry. The courses include both practical and theoretical training. Candidates will be required to gain evidence of on-site gas work experience under the supervision of a Gas Safe registered engineer.