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IASSC lean six sigma black belt course. Online, 24/7 access to content and exam. Fee includes learning content, live webinars, tutor support, and official IASSC exam.

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

Duration 5 Days 30 CPD hours This course is intended for This course is recommended for IT Professionals and Consultants. Overview Identify risks and areas for improvement in a Citrix Virtual Apps and Desktops environment by assessing relevant information in an existing deployment. Determine core Citrix Virtual Apps and Desktops design decisions and align them to business requirements to achieve a practical solution. Design a Citrix Virtual Apps and Desktops disaster recovery plan and understand different disaster recovery considerations. This advanced 5-day training course teaches the design principles for creating a Citrix Virtual Apps and Desktops virtualization solution. In this training, you will also learn how to assess existing environments, explore different scenarios, and make design decisions based on business requirements. This course covers the Citrix Consulting approach to design and covers the key design decisions through lectures, lab exercises, and interactive discussions. You will also learn about additional considerations and advanced configurations for multi-location solutions and disaster recovery planning. This training will help you prepare for the Citrix Certified Expert in Virtualization (CCE-V) exam. Module 1: Methodology & Assessment The Citrix Consulting Methodology Citrix Consulting Methodology Use Business Drivers Prioritize Business Drivers User Segmentation User Segmentation Process App Assessment Introduction App Assessment Analysis Why Perform a Capabilities Assessment? Common Capabilities Assessment Risks Module 2: User Layer Endpoint Considerations Peripherals Considerations Citrix Workspace App Version Considerations Citrix Workspace App Multiple Version Considerations Network Connectivity and the User Experience Bandwidth and Latency Considerations Graphics Mode Design Considerations HDX Transport Protocols Considerations Media Content Redirection Considerations Session Interruption Management Session Reliability Feature Considerations Session Interruption Management Auto-Client Reconnect Feature Considerations Session Interruption Management ICA Keep-Alive Feature Considerations Module 3: Access Layer Access Matrix Access Layer Access Layer Communications Double-Hop Access Layer Considerations Citrix Cloud Access Layer Considerations Use Cases for Multiple Stores Define Access Paths per User Group Define Number of URLs Configuration and Prerequisites for Access Paths Citrix Gateway Scalability Citrix Gateway High Availability StoreFront Server Scalability StoreFront Server High Availability Module 4: Resource Layer - Images Flexcast Models VDA Machine Scalability VDA Machine Sizing with NUMA VDA Machine Sizing VDA Machine Scalability Cloud Considerations Scalability Testing and Monitoring Secure VDA Machines Network Traffic Secure VDA Machines Prevent Breakouts Secure VDA Machines Implement Hardening Secure VDA Machines Anti-Virus Review of Image Methods Citrix Provisioning Overall Benefits and Considerations Citrix Provisioning Target Device Boot Methods Citrix Provisioning Read Cache and Sizing Citrix Provisioning Write Cache Type Citrix Provisioning vDisk Store Location Citrix Provisioning Network Design Citrix Provisioning Scalability Considerations Citrix Machine Creation Services Overall Benefits and Considerations Citrix Machine Creation Services Cloning Types Citrix Machine Creation Services Storage Locations & Sizing Citrix Machine Services Read and Write Cache App Layering Considerations Image Management Methods Module 5: Resource Layer - Applications and Personalization Application Delivery Option Determine the Optimal Deployment Method for an App General Application Concerns Profile Strategy Profile Types Review Citrix Profile Management Design Considerations Citrix Profile Management Scaling Citrix Profile Management Permissions Policies Review Optimize Logon Performance with Policies Printing Considerations Module 6: Control Layer Pod Architecture Introduction Pod Architecture Considerations Citrix Virtual Apps and Desktops Service Design Considerations Implement User Acceptance Testing Load Balancing the Machine Running the VDA Citrix Director Design Considerations Management Console Considerations Change Control Delivery Controller Scalability and High Availability Control Layer Security Configuration Logging Considerations Session Recording Module 7: Hardware/Compute Layer Hypervisor Host Hardware Considerations Separating Workloads Considerations Workload Considerations VMs Per Host and Hosts Per Pool Citrix Hypervisor Scalability VM Considerations in Azure and Amazon Web Services Storage Tier Considerations Storage I/O Considerations Storage Architecture Storage RAID & Disk Type Storage Sizing LUNs Storage Bandwidth Storage in Public Cloud Datacenter Networking Considerations Securing Hypervisor Administrative Access Secure the Physical Datacenter Secure the Virtual Datacenter Module 8: Module 8: High Availability and Multiple Location Environments Redundancy vs. Fault Tolerance vs. High Availability Multi-Location Architecture Considerations Multi-Site Architecture Considerations Global Server Load Balancing Optimal Gateway Routing Zone Preference and Failover StoreFront Resource Aggregation StoreFront Subscription Sync Hybrid Environment Options Citrix Provisioning Across Site Site Database Scalability and High Availability Citrix Provisioning Across Sites Considerations Citrix Machine Creation Across Sites App Layering Across Sites Managing Roaming Profiles and Citrix Workspace App Configurations Across Devices Profile Management Multi-Site Replication Considerations Folder Redirections and Other User Data in a Multi-Location Environment Application Data Considerations Cloud-Based Storage Replication Options Multi-Location Printing Considerations Zone Considerations Active Directory Considerations Module 9: Disaster Recovery Tiers of Disaster Recovery Disaster Recovery Considerations Business Continuity Planning and Testing Citrix Standard of Business Continuity

Are basic leadership and critical thinking abilities your strong point? Do you like being able to create products and sell it? Do you know how to set goals clearly? Do you like managing your own business? If so, then this diploma course is for you. Description: Beginning your own business is an accomplishment in itself that you ought to be amped up for however it is just the beginning stage. The next thing you should know is to figure out how to make it become unique that will be recognized. In this course, you will be able to use the information about your business and what you can do to make it astounding. In this course, you will learn appropriate hints and thoughts for making your business emerge in the market. This will thus give you an edge over your competitions. Moreover, this course will set you up for a profession in a business administration condition where you will have the capacity to perform business-related exercises and in addition administration obligations viable. You will cover various scopes like Business Communication, Financial Accounting, Principles of Marketing, Human Resources and Project Management. Who is the course for? For employees, businessmen and entrepreneurs who are interested in upgrading their business administration skills. Or anyone who is interested in leading and managing their own business. Entry Requirement: This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of English language, numeracy and ICT are required to attend this course. Assessment:  At the end of the course, you will be required to sit an online multiple-choice test. Your test will be assessed automatically and immediately so that you will instantly know whether you have been successful. Before sitting for your final exam you will have the opportunity to test your proficiency with a mock exam. Certification: After you have successfully passed the test, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hard copy at a cost of £39 or in PDF format at a cost of £24. PDF certificate's turnaround time is 24 hours and for the hardcopy certificate, it is 3-9 working Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry leading experts; Study in a user-friendly, advanced online learning platform; Efficient exam systems for the assessment and instant result; The UK & internationally recognised accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. Career Path: This Diploma in Business Administration and Branding Course will be useful and would be beneficial for the management occupations especially the following careers: Administrative Services Manager Advertising, Promotions and Marketing Managers Agricultural Managers Architectural and Engineering Managers Compensation and Benefits Managers Computer and Information Systems Manager Construction Managers Financial Managers Food Services Managers Human Resources Managers Industrial Production Managers Management Directors Medical and Health Managers Natural Sciences Managers And other Top Executives. Business Administration What is Business Administration? 00:30:00 Business and Organisations 00:15:00 Management of Business 00:30:00 Business Organization Perspectives 00:30:00 Decision Making 00:30:00 Approaches to Leadership 00:30:00 Business Communication Communication Basics 00:30:00 Know Thyself and The Message 00:30:00 Learn To Use Terms of Business Communication That Invoke Emotions 00:30:00 Pay Attention To Body Language 00:30:00 Learn To Use Terms That Spark Interest 00:30:00 The Benefits Of Presenting Your Message In 1 Minute 00:30:00 Branding Basics Introduction 00:15:00 The Purple Cow 00:30:00 Try to be an Exceptionist 00:30:00 Make Yourself Well Recognised Before Your Customers 00:15:00 Wow Your Customers 00:30:00 Develop a Personal Style 00:30:00 Use Creative Imageries to Catch the Attention 00:30:00 Be a Traditionalist, Not a Fad Chaser 00:15:00 Bringing Out the Purple Cow in Your Business 00:30:00 Conclusion 00:15:00 Brand Management Defining Branding 00:15:00 What Are You All About? 00:30:00 Creating a Mission 00:15:00 Creating a Vision of the Future 00:15:00 Positioning Your Brand 00:15:00 Developing Your Style 00:15:00 Developing a Brand Name and Slogan 00:15:00 Creating a Visual Identity 00:30:00 Living Your Brand 00:15:00 Connecting with Customers 00:15:00 Launching Your Brand 00:15:00 Taking Your Brand's Pulse 00:15:00 Performing a SWOT Analysis 00:15:00 Measuring Brand Health with a Balanced Scorecard 00:15:00 Middleton's Brand Matrix 00:15:00 Interpreting Evaluation Results 00:15:00 Keeping the Brand Alive 00:15:00 Going Beyond the Brand 00:15:00 Business Branding What Is Branding 00:30:00 Research Your Topic Thoroughly 00:30:00 Let People Know Who You Are: Distinguish Yourself 01:00:00 Represent Yourself 00:30:00 Networking With Social Media 01:00:00 Use Videos 00:30:00 Offer Training Courses 00:30:00 Provide Quality Material And Customer Service 00:30:00 Live And Breathe Your Brand 00:30:00 How Not Branding Can Spell Disaster 00:15:00 Personal Branding Module One - Getting Started 00:30:00 Module Two - Defining Yourself (I) 00:30:00 Module Three - Defining Yourself (II) 00:30:00 Module Four - Controlling and Developing Your Image 00:30:00 Module Five - Personal and Professional Influences 00:30:00 Module Six - Sharpening Your Brand 00:30:00 Module Seven - Appearance Matters 00:30:00 Module Eight - Social Media (I) 00:30:00 Module Nine - Social Media (II) 02:00:00 Module Ten - Brand Management During a Crisis 01:00:00 Module Eleven - Branding Personality Traits 00:30:00 Module Twelve - Wrapping Up 00:30:00 Mock Exam Mock Exam- Advanced Diploma in Business Administration and Branding 00:20:00 Final Exam Final Exam- Advanced Diploma in Business Administration and Branding 00:20:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

IASSC lean six sigma green belt course. Online, 24/7 access to content and exam. Fee includes learning content, live webinars, tutor support, and official IASSC exam.

Take our part-time online “Conversion Course into Pharmaceutical Manufacturing“ NO Previous Industry Experience or Science Qualifications Required

Campaigning For Change Course Overview Are you looking to begin your campaigning career or want to develop more advanced skills in campaigning? Then this campaigning for change online course will set you up with a solid foundation to become a confident professional and help you to develop your expertise in campaigning. This campaigning for change online course is accredited by the CPD UK & IPHM. CPD is globally recognised by employers, professional organisations and academic intuitions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement. CPD certified certificates are accepted by thousands of professional bodies and government regulators here in the UK and around the world. Whether you are self-taught and you want to fill in the gaps for better efficiency and productivity, this campaigning for change online course will set you up with a solid foundation to become a confident professional and develop more advanced skills. Gain the essential skills and knowledge you need to propel your career forward as a professional. The campaigning for change online course will set you up with the appropriate skills and experience needed for the job and is ideal for both beginners and those currently working as a professional. This comprehensive campaigning for change online course is the perfect way to kickstart your career in the field of campaigning. This campaigning for change online course will give you a competitive advantage in your career, making you stand out from all other applicants and employees. If you're interested in working as a professional or want to learn more skills on campaigning but unsure of where to start, then this campaigning for change online course will set you up with a solid foundation to become a confident professional and develop more advanced skills. As one of the leading course providers and most renowned e-learning specialists online, we're dedicated to giving you the best educational experience possible. This campaigning for change online course is crafted by industry expert, to enable you to learn quickly and efficiently, and at your own pace and convenience. Who should take this course? This comprehensive campaigning for change online course is suitable for anyone looking to improve their job prospects or aspiring to accelerate their career in this sector and want to gain in-depth knowledge of campaigning. Entry Requirement There are no academic entry requirements for this campaigning for change online course, and it is open to students of all academic backgrounds. As long as you are aged seventeen or over and have a basic grasp of English, numeracy and ICT, you will be eligible to enrol. Career path This campaigning for change online course opens a brand new door for you to enter the relevant job market and also provides you with the chance to accumulate in-depth knowledge at the side of needed skills to become flourishing in no time. You will also be able to add your new skills to your CV, enhance your career and become more competitive in your chosen industry. Course Curriculum Introduction Introduction What is Campaigning What is Your Problem What is Your Problem and Why is it a Problem What is Your Problem - an Example The External Environment Your External Environment Evidence for Campaigning What Evidence Do You Have The Tea Test Do You Pass The Tea Test The Elevator Pitch Can You Do The Elevator Pitch Credibility and Risk How Credible are You and What are The Risks Influence Tree Do You Have an Influence Tree Opposition The Opposition Matrix Opposition - Examples Theory of Change Do You Have a Theory of Change Campaigning and Story Telling Plan for Success Do You Have a Plan for Success Conclusion When are You Next Going to Review Progress Recognised Accreditation CPD Certification Service 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. CPD certificates are accepted by thousands of professional bodies and government regulators here in the UK and around the world. Many organisations look for employees with CPD requirements, which means, that by doing this course, you would be a potential candidate in your respective field. Quality Licence Scheme Endorsed 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. It will give you a competitive advantage in your career, making you stand out from all other applicants and employees.     Certificate of Achievement Endorsed Certificate from Quality Licence Scheme After successfully passing the MCQ exam you will be eligible to order the Endorsed Certificate by Quality Licence Scheme. 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. It will give you a competitive advantage in your career, making you stand out from all other applicants and employees. There is a Quality Licence Scheme endorsement fee to obtain an endorsed certificate which is £65. Certificate of Achievement from Lead Academy After successfully passing the MCQ exam you will be eligible to order your certificate of achievement as proof of your new skill. The certificate of achievement is an official credential that confirms that you successfully finished a course with Lead Academy. Certificate can be obtained in PDF version at a cost of £12, and there is an additional fee to obtain a printed copy certificate which is £35.   FAQs Is CPD a recognised qualification in the UK? CPD is globally recognised by employers, professional organisations and academic intuitions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement. CPD-certified certificates are accepted by thousands of professional bodies and government regulators here in the UK and around the world. Are QLS courses recognised? Although QLS courses are not subject to Ofqual regulation, they must adhere to an extremely high level that is set and regulated independently across the globe. A course that has been approved by the Quality Licence Scheme simply indicates that it has been examined and evaluated in terms of quality and fulfils the predetermined quality standards. When will I receive my certificate? For CPD accredited PDF certificate it will take 24 hours, however for the hardcopy CPD certificate takes 5-7 business days and for the Quality License Scheme certificate it will take 7-9 business days. Can I pay by invoice? Yes, you can pay via Invoice or Purchase Order, please contact us at info@lead-academy.org for invoice payment. Can I pay via instalment? Yes, you can pay via instalments at checkout. How to take online classes from home? Our platform provides easy and comfortable access for all learners; all you need is a stable internet connection and a device such as a laptop, desktop PC, tablet, or mobile phone. The learning site is accessible 24/7, allowing you to take the course at your own pace while relaxing in the privacy of your home or workplace. Does age matter in online learning? No, there is no age limit for online learning. Online learning is accessible to people of all ages and requires no age-specific criteria to pursue a course of interest. As opposed to degrees pursued at university, online courses are designed to break the barriers of age limitation that aim to limit the learner's ability to learn new things, diversify their skills, and expand their horizons. When I will get the login details for my course? After successfully purchasing the course, you will receive an email within 24 hours with the login details of your course. Kindly check your inbox, junk or spam folder, or you can contact our client success team via info@lead-academy.org

Overview This comprehensive course on Data Science & Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Science & Machine Learning with Python comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Data Science & Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Data Science & Machine Learning with Python is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 2 sections • 90 lectures • 10:24:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:08:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:07:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

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 Course Introduction Introduction 00:03:00 Part 1: Forecasting Basics of Forecasting 00:05:00 Creating Linear Model with Trendlines 00:08:00 1.1 Getting Data Ready For Regression Model Gathering Business Knowledge 00:03:00 Data Exploration 00:03:00 The Data and the Data Dictionary 00:07:00 Univariate analysis and EDD 00:03:00 Discriptive Data Analytics in Excel 00:10:00 Outlier Treatment 00:04:00 Identifying and Treating Outliers in Excel 00:04:00 Missing Value Imputation 00:03:00 Identifying and Treating missing values in Excel 00:04:00 Variable Transformation in Excel 00:03:00 Dummy variable creation: Handling qualitative data 00:04:00 Dummy Variable Creation in Excel 00:07:00 Correlation Analysis 00:09:00 Creating Correlation Matrix in Excel 00:08:00 1.2 Forecasting Using Regression Model The Problem Statement 00:01:00 Basic Equations and Ordinary Least Squares (OLS) method 00:08:00 Assessing accuracy of predicted coefficients 00:14:00 Assessing Model Accuracy: RSE and R squared 00:07:00 Creating Simple Linear Regression model 00:02:00 Multiple Linear Regression 00:05:00 The F - statistic 00:08:00 Interpreting results of Categorical variables 00:05:00 Creating Multiple Linear Regression model 00:07:00 1.3 Handling Special Events Like Holiday Sales Forecasting in presence of special events 00:02:00 Excel: Running Linear Regression using Solver 00:08:00 Excel: Including the impact of Special Events 00:22:00 1.4 Identifying Seasonality & Trend for Forecasting Models to identify Trend & Seasonality 00:06:00 Excel: Additive model to identify Trend & Seasonality 00:09:00 Excel: Multiplicative model to identify Trend & Seasonality 00:06:00 Market Basket Analysis Market Basket and Lift - Introduction 00:08:00 Named Ranges - Excel 00:10:00 Indirect Function - Excel 00:05:00 2-way lift calculation in Excel 00:11:00 2-way lift calculation - Dynamic 00:07:00 2-way lift data table creation 00:07:00 3-way lift calculation 00:19:00 Store Layout optimization using Lift values 00:15:00 RFM (Recency, Frequency, Monetary) Analysis RFM (recency, frequency, monetary) Analysis 00:08:00 RFM Analysis in Excel- Part 1 00:16:00 RFM Analysis in Excel- Part 2 00:12:00 Part 2: Pricing Part 2: Pricing Steps of setting a Pricing policy 00:03:00 Different Pricing Objectives 00:07:00 2.1 Estimating Demand Estimating Demand 00:07:00 Forms of Demand Curve 00:02:00 Excel: Estimating Linear Demand Curve 00:08:00 Excel: Estimating Power Demand curve with Elasticity 00:05:00 Excel: Estimating Power Demand Curve with points 00:03:00 Subjective Demand curve 00:01:00 Excel: Estimating Subjective Demand Curve 00:02:00 2.3 Evaluating Pricing Strategies Price Bundling 00:07:00 Types of Bundling 00:08:00 The Bundling Problem 00:04:00 Excel: Solving Bundling problem Part 1 00:14:00 Excel: Solving Bundling problem Part 2 00:08:00 Non-Linear Pricing Strategies 00:03:00 Excel: Solving Bundling problem (Price Reversal) 00:08:00 3.1 Lifetime Customer Value Lifetime Customer Value - Key concepts 00:09:00 Lifetime Customer Value - Excel model 00:11:00 3.2 Variations And Sensitivity Analysis Sensitivity Analysis in Excel 00:07:00 Variations in finding customer value 00:07:00 Appendix 1: Excel Crash Course Basics 00:08:00 Worksheet Basics 00:16:00 Entering values and Formulas 00:07:00 Data Handling Basics - Cut, Copy and Paste 00:14:00 Saving and Printing - Basics 00:09:00 Basic Formula Operations 00:13:00 Mathematical Formulas 00:19:00 Textual Formulas 00:17:00 Logical Formulas 00:11:00 Date-Time Formulas 00:07:00 Lookup Formulas ( V Lookup, Hlookup, Index-Match ) 00:08:00 Data Tools 00:19:00 Formatting data and tables 00:18:00 Pivot Tables 00:08:00 Advance Excel- Solver, Data tables 00:15:00 Assessment Assessment - Retail Analytics In Microsoft Excel Diploma 00:10:00 Obtain Your Certificate Order Your Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00

Overview of Data Science & Machine Learning with Python Join our Data Science & Machine Learning with Python course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Data Science & Machine Learning with Python course has everything you need to get a great start in this sector. Improving and moving forward is key to getting ahead personally. The Data Science & Machine Learning with Python course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now! This Data Science & Machine Learning with Python Course will help you to learn: Learn strategies to boost your workplace efficiency. Hone your skills to help you advance your career. Acquire a comprehensive understanding of various topics and tips. Learn in-demand skills that are in high demand among UK employers This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Data Science & Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever. Details Perks of Learning with IOMH One-To-One Support from a Dedicated Tutor Throughout Your Course. Study Online - Whenever and Wherever You Want. Instant Digital/ PDF Certificate. 100% Money Back Guarantee. 12 Months Access. Process of Evaluation After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the Data Science & Machine Learning with Python course, you can order your CPD Accredited Digital / PDF Certificate for £5.99.  Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. Shipping Charges: Inside the UK: £3.99 International: £10.99 Who Is This Course for? This Data Science & Machine Learning with Python is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.  On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  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 You don't need any educational qualification or experience to enrol in the Data Science & Machine Learning with Python 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 course. Career Path The certification and skills you get from this Data Science & Machine Learning with Python Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors. 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 00:04:00 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 Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using NumPy 00:04:00 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 00:06:00 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 - Data Science & Machine Learning with Python 00:00:00