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Solution-Focused Brief Therapy (SFBT) is an evidence-based approach that focuses on the present and future rather than the past. It helps clients identify and achieve their desired goals in a short time by using their own resources and strengths. SFBT is based on the assumption that clients are experts on their own lives and have the ability to change.   This course is your gateway to mastering the art of SFBT-an approach that focuses on strengths, solutions, and client empowerment. It's about creating an environment where individuals can discover their own paths to positive change. Enroll now and be the change your clients are searching for.

Embark on a journey into the world of strategic analysis with this advanced course. Delve deep into risk management standards, the COSO framework for internal controls, competitive positioning strategies, BCG Matrix for portfolio analysis, pricing strategies, and the impact of economies of scale. Equip yourself with the tools and models essential for navigating complex business landscapes. This course is a comprehensive exploration of advanced tools and models crucial for strategic analysis. After the successful completion of this course, you will be able to: ⦁ Understand risk management standards' significance and application for organizational resilience and threat mitigation. ⦁ Identify components of the COSO framework for effective internal controls and risk assessment. ⦁ Analyze different types of competitive positions to strategize for market advantage. ⦁ Comprehend BCG Matrix for product/service portfolio analysis. ⦁ Explore pricing strategies and their impact on revenue and market position. ⦁ Understand economies of scale and their influence on cost efficiency and competitiveness. Elevate your risk analysis expertise with our advanced course, meticulously curated for professionals. Uncover sophisticated tools and models designed for strategic precision, empowering you to navigate complexities with confidence. Sharpen your analytical prowess and stay ahead in the dynamic business landscape. Enroll to master the art of strategic risk analysis. 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. Advanced Tools And Models For Strategic Analysis Self-paced pre-recorded learning content on this topic. Advanced Tools And Models For Strategic Analysis 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.  1. Risk Management Professionals 2. Financial Analysts3. Strategic Planners4. Business Intelligence Analysts5. Project Managers6. Compliance Officers7. Data Scientists8. Decision Analysts 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.

Embark on a transformative journey into the realm of emotional mastery with our course, 'Dialectical Behaviour Therapy (DBT) for Emotional Well-being'. This enlightening voyage through the intricacies of DBT will not only illuminate the path to personal emotional regulation but also equip you with the skills to facilitate others in their journey towards emotional well-being. Your exploration begins with an in-depth introduction to DBT, guiding you through its foundational principles and establishing a robust platform upon which to build your therapeutic skills. As you traverse through the carefully crafted modules, you'll delve into the core skills of mindfulness, emotion regulation, interpersonal effectiveness, and distress tolerance, each a vital beacon in the DBT landscape. These pillars of DBT are not merely theoretical constructs but practical tools, designed to be applied in real-world scenarios, ensuring that individuals can navigate through the emotional tumult of life with grace and resilience. Your journey will also guide you through the application of DBT across various populations, ensuring a versatile and adaptable therapeutic skill set. Learning Outcomes Understand and apply the foundational principles and techniques of dialectical behaviour therapy. Utilise mindfulness, emotion regulation, interpersonal effectiveness, and distress tolerance skills in therapeutic contexts. Implement DBT strategies effectively across various populations and diverse emotional challenges. Apply DBT in clinical settings, ensuring therapeutic interventions are both relevant and impactful. Navigate through ethical and professional considerations, ensuring DBT practice is conducted with integrity and adheres to professional standards. Why buy this Dialectical Behaviour Therapy (DBT) for Emotional Well-being? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success Who is this Dialectical Behaviour Therapy (DBT) for Emotional Well-being for? Individuals seeking personal emotional mastery and well-being through DBT. Mental health professionals aspiring to expand their therapeutic toolkit with DBT. Social workers desiring to provide enhanced emotional support to their clients. Counsellors looking to diversify their therapeutic approaches with validated techniques. Psychology students wishing to deepen their understanding and application of therapeutic modalities. Career path DBT Therapist: £30,000 - £45,000 Mental Health Counsellor: £23,000 - £40,000 Clinical Psychologist: £39,000 - £82,000 Social Worker: £25,000 - £40,000 Psychotherapist: £26,000 - £48,000 Mental Health Nurse: £25,000 - £35,000 Prerequisites This Dialectical Behaviour Therapy (DBT) for Emotional Well-being 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. Course Curriculum Module 1: Introduction to DBT Introduction to DBT 00:19:00 Module 2: Mindfulness Skills Training Mindfulness Skills Training 00:20:00 Module 3: Emotion Regulation Skills Emotion Regulation Skills 00:19:00 Module 4: Interpersonal Effectiveness Skills Interpersonal Effectiveness Skills 00:16:00 Module 5: Distress Tolerance Skills Distress Tolerance Skills 00:26:00 Module 6: Application of DBT in Specific Populations Application of DBT in Specific Populations 00:29:00 Module 7: Implementation of DBT in Clinical Settings Implementation of DBT in Clinical Settings 00:28:00 Module 8: Ethical and Professional Considerations in DBT Practice Ethical and Professional Considerations in DBT Practice 00:19:00

In an era awash with data, understanding it is the key to unlocking significant opportunities. Enter the realm of 'SQL For Data Analytics & Database Development'. Dive deep into the heart of data manipulation, exploration, and insight generation. This course unfurls the magic behind SQL, drawing back the curtain on the tools that empower businesses globally. Understanding SQL's profound capabilities opens doors to a world where data-driven decision-making is not just an advantage but an imperative. It isn't simply about data storage. This course unravels the art of analytics, shedding light on how to glean valuable insights from vast data lakes. The path of SQL isn't merely technical; it's the spine of transformative decisions in today's leading industries. Offering a systematic approach to database development, we invite you to embark on this illuminative journey, stitching the threads of raw data into the fabric of meaningful stories. Come harness the power of SQL. Whether your intent is data analytics or the intricate craft of database development, this is your gateway. Let us guide you, from the rudimentary steps to mastering advanced commands, constructing your data narrative, and informing the future. Learning Outcomes: Comprehend the fundamental architecture of SQL and its environment. Acquire proficiency in basic and advanced SQL statements. Understand and implement GROUP BY statements for data aggregation. Master the concepts and applications of JOINS in databases. Cultivate the ability to create and structure databases and tables. Delve into advanced SQL commands, enhancing data manipulation and querying capabilities. Construct efficient database models, fostering optimal data storage and retrieval.   Video Playerhttps://studyhub.org.uk/wp-content/uploads/2021/03/SQL-DATA-ANALYSIS.mp400:0000:0000:00Use Up/Down Arrow keys to increase or decrease volume. Why choose this SQL For Data Analytics & Database Development 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 SQL For Data Analytics & Database Development 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 SQL For Data Analytics & Database Development course for? Aspirants seeking to transition into data-driven roles in various industries. Database administrators aiming to broaden their analytical capabilities. Business analysts eager to enhance their data querying skills. Researchers in need of comprehensive data management tools. Tech enthusiasts wanting to grasp the underpinnings of database systems. Career path Data Analyst - Average salary: £35,000 - £50,000 Per Annum Database Developer - Average salary: £40,000 - £55,000 Per Annum Business Intelligence Analyst - Average salary: £38,000 - £52,000 Per Annum SQL Developer - Average salary: £42,000 - £58,000 Per Annum Database Administrator (DBA) - Average salary: £45,000 - £60,000 Per Annum Data Engineer - Average salary: £47,000 - £63,000 Per Annum Prerequisites This SQL For Data Analytics & Database Development 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 Course Introduction Introduction 00:04:00 Course Curriculum Overview 00:05:00 Overview of Databases 00:10:00 SQL Environment Setup MySQL Installation 00:16:00 MySQL Workbench Installation 00:09:00 Connecting to MySQL using Console 00:09:00 SQL Statement Basics Overview of Challenges 00:04:00 SQL Statement Basic 00:16:00 SELECT Statement 00:09:00 SELECT DISTINCT 00:05:00 Column AS Statement 00:12:00 COUNT built-in Method usage 00:11:00 SELECT WHERE Clause - Part One 00:05:00 SELECT WHERE Clause - Part Two 00:11:00 SQL Statement Basic 00:16:00 SQL Limit Clause Statement 00:09:00 SQL Using BETWEEN with Same Column Data 00:11:00 How to Apply IN Operator 00:11:00 Wildcard Characters with LIKE and ILIKE 00:11:00 GROUP BY Statements Overview of GROUP BY 00:06:00 Aggregation function SUM() 00:09:00 Aggregation MIN() and MAX() 00:05:00 GROUP BY - One 00:09:00 GROUP BY - Two 00:12:00 HAVING Clause 00:05:00 JOINS Overview of JOINS 00:04:00 Introduction to JOINS 00:04:00 AS Statement table 00:03:00 INNER Joins 00:14:00 FULL Outer Join 00:11:00 LEFT Outer JOIN 00:08:00 RIGHT JOIN 00:08:00 Union 00:07:00 Advanced SQL Commands / Statements Timestamps 00:12:00 EXTRACT from timestamp 00:10:00 Mathematical Functions 00:12:00 String Functions 00:22:00 SUBQUERY 00:13:00 Creating Database and Tables Basic of Database and Tables 00:06:00 DataTypes 00:10:00 Primarykey and Foreignkey 00:06:00 Create Table in SQL Script 00:13:00 Insert 00:11:00 Update 00:07:00 Delete 00:04:00 Alter Table 00:09:00 Drop Table 00:05:00 NOT NULL Constraint 00:08:00 UNIQUE Constraint 00:09:00 Databases and Tables Creating a Database backup 00:12:00 10a Overview of Databases and Tables 00:05:00 10c Restoring a Database 00:07:00

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This course teaches the fundamental concepts of DAX in Power BI. If you have the questions: How do I learn DAX? What is the best way to learn DAX fast?-then this is the best course for you. This course teaches fundamental concepts and does not cover visualization or various advanced DAX patterns for specific questions.

Duration 4 Days 24 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm ? YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions

Duration 1 Days 6 CPD hours This course is intended for This course is designed for data scientists with experience of Python who need to learn how to apply their data science and machine learning skills on Azure Databricks. Overview After completing this course, you will be able to: Provision an Azure Databricks workspace and cluster Use Azure Databricks to train a machine learning model Use MLflow to track experiments and manage machine learning models Integrate Azure Databricks with Azure Machine Learning Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this course, students will learn how to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning. Introduction to Azure Databricks Getting Started with Azure Databricks Working with Data in Azure Databricks Training and Evaluating Machine Learning Models Preparing Data for Machine Learning Training a Machine Learning Model Managing Experiments and Models Using MLflow to Track Experiments Managing Models Managing Experiments and Models Using MLflow to Track Experiments Managing Models Integrating Azure Databricks and Azure Machine Learning Tracking Experiments with Azure Machine Learning Deploying Models

Recognised Accreditation This course is accredited by continuing professional development (CPD). CPD UK is globally recognised by employers, professional organisations, and academic institutions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. What is CPD? Employers, professional organisations, and academic institutions all recognise CPD, therefore a credential from CPD Certification Service adds value to your professional goals and achievements. Benefits of CPD Improve your employment prospects Boost your job satisfaction Promotes career advancement Enhances your CV Provides you with a competitive edge in the job market Demonstrate your dedication Showcases your professional capabilities What is IPHM? The IPHM is an Accreditation Board that provides Training Providers with international and global accreditation. The Practitioners of Holistic Medicine (IPHM) accreditation is a guarantee of quality and skill. Benefits of IPHM It will help you establish a positive reputation in your chosen field You can join a network and community of successful therapists that are dedicated to providing excellent care to their client You can flaunt this accreditation in your CV It is a worldwide recognised accreditation What is Quality Licence Scheme? This course is endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. Benefits of Quality License Scheme Certificate is valuable Provides a competitive edge in your career It will make your CV stand out Course Curriculum Welcome to the course Introduction 00:02:00 Setting up R Studio and R crash course Installing R and R studio 00:05:00 Basics of R and R studio 00:10:00 Packages in R 00:10:00 Inputting data part 1: Inbuilt datasets of R 00:04:00 Inputting data part 2: Manual data entry 00:03:00 Inputting data part 3: Importing from CSV or Text files 00:06:00 Creating Barplots in R 00:13:00 Creating Histograms in R 00:06:00 Basics of Statistics Types of Data 00:04:00 Types of Statistics 00:02:00 Describing the data graphically 00:11:00 Measures of Centers 00:07:00 Measures of Dispersion 00:04:00 Introduction to Machine Learning Introduction to Machine Learning 00:16:00 Building a Machine Learning Model 00:08:00 Data Preprocessing for Regression Analysis Gathering Business Knowledge 00:03:00 Data Exploration 00:03:00 The Data and the Data Dictionary 00:07:00 Importing the dataset into R 00:03:00 Univariate Analysis and EDD 00:03:00 EDD in R 00:12:00 Outlier Treatment 00:04:00 Outlier Treatment in R 00:04:00 Missing Value imputation 00:03:00 Missing Value imputation in R 00:03:00 Seasonality in Data 00:03:00 Bi-variate Analysis and Variable Transformation 00:16:00 Variable transformation in R 00:09:00 Non Usable Variables 00:04:00 Dummy variable creation: Handling qualitative data 00:04:00 Dummy variable creation in R 00:05:00 Correlation Matrix and cause-effect relationship 00:10:00 Correlation Matrix in R 00:08:00 Linear Regression Model The problem statement 00:01:00 Basic equations and Ordinary Least Squared (OLS) method 00:08:00 Assessing Accuracy of predicted coefficients 00:14:00 Assessing Model Accuracy - RSE and R squared 00:07:00 Simple Linear Regression in R 00:07:00 Multiple Linear Regression 00:05:00 The F - statistic 00:08:00 Interpreting result for categorical Variable 00:05:00 Multiple Linear Regression in R 00:07:00 Test-Train split 00:09:00 Bias Variance trade-off 00:06:00 Test-Train Split in R 00:08:00 Regression models other than OLS Linear models other than OLS 00:04:00 Subset Selection techniques 00:11:00 Subset selection in R 00:07:00 Shrinkage methods - Ridge Regression and The Lasso 00:07:00 Ridge regression and Lasso in R 00:12:00 Classification Models: Data Preparation The Data and the Data Dictionary 00:08:00 Importing the dataset into R 00:03:00 EDD in R 00:11:00 Outlier Treatment in R 00:04:00 Missing Value imputation in R 00:03:00 Variable transformation in R 00:06:00 Dummy variable creation in R 00:05:00 The Three classification models Three Classifiers and the problem statement 00:03:00 Why can't we use Linear Regression? 00:04:00 Logistic Regression Logistic Regression 00:08:00 Training a Simple Logistic model in R 00:03:00 Results of Simple Logistic Regression 00:05:00 Logistic with multiple predictors 00:02:00 Training multiple predictor Logistic model in R 00:01:00 Confusion Matrix 00:03:00 Evaluating Model performance 00:07:00 Predicting probabilities, assigning classes and making Confusion Matrix in R 00:06:00 Linear Discriminant Analysis Linear Discriminant Analysis 00:09:00 Linear Discriminant Analysis in R 00:09:00 K-Nearest Neighbors Test-Train Split 00:09:00 Test-Train Split in R 00:08:00 K-Nearest Neighbors classifier 00:08:00 K-Nearest Neighbors in R 00:08:00 Comparing results from 3 models Understanding the results of classification models 00:06:00 Summary of the three models 00:04:00 Simple Decision Trees Basics of Decision Trees 00:10:00 Understanding a Regression Tree 00:10:00 The stopping criteria for controlling tree growth 00:03:00 The Data set for this part 00:03:00 Importing the Data set into R 00:06:00 Splitting Data into Test and Train Set in R 00:05:00 Building a Regression Tree in R 00:14:00 Pruning a tree 00:04:00 Pruning a Tree in R 00:09:00 Simple Classification Tree Classification Trees 00:06:00 The Data set for Classification problem 00:01:00 Building a classification Tree in R 00:09:00 Advantages and Disadvantages of Decision Trees 00:01:00 Ensemble technique 1 - Bagging Bagging 00:06:00 Bagging in R 00:06:00 Ensemble technique 2 - Random Forest Random Forest technique 00:04:00 Random Forest in R 00:04:00 Ensemble technique 3 - GBM, AdaBoost and XGBoost Boosting techniques 00:07:00 Gradient Boosting in R 00:07:00 AdaBoosting in R 00:09:00 XGBoosting in R 00:16:00 Maximum Margin Classifier Content flow 00:01:00 The Concept of a Hyperplane 00:05:00 Maximum Margin Classifier 00:03:00 Limitations of Maximum Margin Classifier 00:02:00 Support Vector Classifier Support Vector classifiers 00:10:00 Limitations of Support Vector Classifiers 00:01:00 Support Vector Machines Kernel Based Support Vector Machines 00:06:00 Creating Support Vector Machine Model in R The Data set for the Classification problem 00:01:00 Importing Data into R 00:08:00 Test-Train Split 00:09:00 Classification SVM model using Linear Kernel 00:16:00 Hyperparameter Tuning for Linear Kernel 00:06:00 Polynomial Kernel with Hyperparameter Tuning 00:10:00 Radial Kernel with Hyperparameter Tuning 00:06:00 The Data set for the Regression problem 00:03:00 SVM based Regression Model in R 00:11:00 Assessment Assessment - Machine Learning Masterclass 00:10:00 Certificate of Achievement Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00