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This ChatGPT for Marketing and Productivity with AI Tools course is your guide to using AI to boost your marketing results. Boost your marketing skills and productivity to the next level with our comprehensive ChatGPT for Marketing and Productivity with AI Tools course. Dive deep into the world of Artificial Intelligence (AI), its applications, and how it can revolutionise the way you work. This course is meticulously designed to empower marketing professionals, content creators, entrepreneurs, and anyone intrigued by the power of AI.  It's a blend of theoretical understanding, practical exposure, and foresight into the future of AI, particularly in the field of marketing and productivity. In Section 01, we unpack the 'AI Marketing Playbook'. Starting with an introduction to OpenAI's ChatGPT, its possibilities, and its limitations, you'll gain a fundamental understanding of AI capabilities. Following this, delve into practical aspects of using ChatGPT, from generating innovative ideas and content to cross-posting queries and simplifying complex information. Our experts will also guide you on how to leverage AI for business problem-solving and developing methodologies, wrapping up with insights on the future of ChatGPT. In Section 02 get teaching on how to use ChatGPT and other AI tools for effective marketing. Learn to work with Autonomous AI Agents and a variety of AI tools such as Durable, Eightify, Genei, and Ellicit, to name a few. By the end of this section, you'll be equipped with the skills to carry out high-quality research, build AI-based websites, determine research credibility, and clone voices. You'll also get an interesting perspective on the future of AI. Finally, Section 03 is all about enhancing your productivity with ChatGPT and AI tools. From meta-search sites to speech-to-text services, AI design tools, content improvement techniques, and more, this section aims to streamline your work processes. Learn to use tools like Microsoft Bing Search, Google Bard, Speechify, and Adobe for audio enhancements. Wrap up this course with an exploration of generative AI and a glance into the future of this exciting field. Whether you're a beginner or an experienced professional, this course promises to expand your horizons and make you proficient in harnessing AI's power for marketing and productivity. Unleash the potential of AI and transform your work efficiency with this ChatGPT for Marketing and Productivity with AI Tools course. Enrol today and start your AI journey with us! Learning Outcomes Upon completion of the ChatGPT for Marketing course, you will be able to: Understand the fundamentals of OpenAI's ChatGPT and its capabilities. Generate and qualify ideas effectively using ChatGPT. Learn to apply ChatGPT for solving specific business problems. Develop skills to connect with various Autonomous AI Agents. Learn to use AI tools for enhanced research and content creation. Understand how to determine research credibility using AI. Gain proficiency in utilising AI for website creation and voice cloning. Develop skills to leverage AI tools for improved productivity. Understand the future scope of generative AI in marketing. Master the use of various AI design and content improvement tools. Who is this course for? This ChatGPT for Marketing course is ideal for: Marketing professionals seeking to leverage AI in their strategies. Content creators interested in AI-powered idea generation and curation. Business owners looking to integrate AI into their operational processes. Individuals interested in exploring AI applications in marketing and productivity. Any tech enthusiast keen on understanding and applying AI tools. Career Path Our ChatGPT for Marketing course will help you to pursue a range of career paths, such as: AI Marketing Specialist: £45,000 - £70,000 Content Strategist: £35,000 - £55,000 Business Intelligence Analyst: £40,000 - £65,000 Productivity Consultant: £45,000 - £75,000 AI Research Analyst: £50,000 - £80,000 AI Application Developer: £55,000 - £90,000 Digital Transformation Consultant: £60,000 - £100,000 AI Solutions Architect: £65,000 - £110,000 Prerequisites This Photoshop Training for Beginners does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Photoshop Training for Beginners 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 of the Photoshop Training for Beginners 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. Course Curriculum Section 01: The AI Marketing Playbook Unit 01: Start an Account with ChatGPT 00:02:00 Unit 02: What the Company OpenAI Say About Itself 00:02:00 Unit 03: What OpenAI Say About The Limitations of the Chatbot 00:02:00 Unit 04: Chatbot Prompt Examples Given By Open AI 00:02:00 Unit 05: Will Chat GPT Be a Paid Application 00:01:00 Unit 06: Chat GPT Idea Generation 00:02:00 Unit 07: Chat GPT - Idea Qualification and Accuracy 00:03:00 Unit 08: ChatGPT - Accuracy and Citations 00:02:00 Unit 09: Chat GPT - Creating HTML Instances 00:01:00 Unit 10: Chat GPT - How to Solve Specific Business Problems 00:02:00 Unit 11: Chat GPT - Statistical Verification of Information 00:01:00 Unit 12: Chat GPT - Rewrite Content for Different Contexts 00:02:00 Unit 13: ChatGPT - Content Checked With AI 00:02:00 Unit 14: ChatGPT - Simplifying Information 00:01:00 Unit 15: ChatGPT - How to Ask the Chatbot about Context 00:01:00 Unit 16: ChatGPT - How to Cross-Post Queries 00:01:00 Unit 17: ChatGPT - How to Narrow Down the Context of Your Query 00:02:00 Unit 18: ChatGPT - How to Solve a Business Process 00:02:00 Unit 19: ChatGPT - Developing a Methodology From Experts 00:01:00 Unit 20: The Future of ChatGPT 00:01:00 Section 02: How to Use ChatGPT and AI for Marketing Unit 01: Autonous AI Agents 00:01:00 Unit 02: Connecting to Open AI 00:01:00 Unit 03: Getting an OpenAI Key 00:01:00 Unit 04: Agent GPT - Autonomous AI 00:02:00 Unit 05: GoalGPT - Autonomous Agents 00:01:00 Unit 06: Cognosis - Autonomous AI 00:02:00 Unit 07: Aomni - Autonomous Agent 00:01:00 Unit 08: Durable - Build a Website with AI 00:01:00 Unit 09: Eightify Summaries 00:02:00 Unit 10: Genei - Do Higher Quality Research with AI 00:01:00 Unit 11: Ellicit - Do Higher Quality Research with AI 00:01:00 Unit 12: Inciteful - Do Higher Quality Research with AI 00:02:00 Unit 13: SciteAI Determine the Credibility of Your Research 00:01:00 Unit 14: Eleven Labs - Voice Cloning 00:02:00 Unit 15: AgentGPT - Wrap Up and Return 00:01:00 Unit 16: Cognosys - Wrap Up and Return 00:01:00 Unit 17: Aomni - Wrap Up and Return 00:01:00 Unit 18: Goal GPT - Wrap Up and Return 00:01:00 Unit 19: Uploading Research Reports to Summarization Applications 00:01:00 Unit 20: Perspective on The Future of AI 00:01:00 Section 03: Productivity with AI Tools Unit 01: Meta Search Sites 00:02:00 Unit 02: SMMRY for Summarzing 00:01:00 Unit 03: ChatGPT Plugins Waitlist 00:01:00 Unit 04: Using Microsoft Bing Search 00:02:00 Unit 05: Using Google Bard 00:01:00 Unit 06: Microsoft Word Speech To Text 00:01:00 Unit 07: Transcribe Audio in Microsoft Word 00:02:00 Unit 08: Speechify 00:02:00 Unit 09: Exact Image Creation 00:01:00 Unit 10: AI Design Tools 00:02:00 Unit 11: Learn How to Prompt 00:01:00 Unit 12: Content Improvement 00:01:00 Unit 13: Idea Generation 00:01:00 Unit 14: Audio Enhancement with Adobe 00:02:00 Unit 15: Clean up Audio With Cleaanvoice 00:01:00 Unit 16: Notion-AI 00:01:00 Unit 17: Pictory 00:01:00 Unit 18: Lex 00:01:00 Unit 19: ChatPDF 00:01:00 Unit 20: Conclusion and the Future of Generatie AI - Searchie 00:01:00

Ascend to the next level of Excel proficiency with our comprehensive Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query course. This in-depth training will equip you with the skills to create sophisticated dashboards using advanced Excel formulas, VBA, and Power Query, empowering you to transform raw data into actionable insights. Master the art of financial modeling with our prepaid expenses models, learning how to calculate amortization schedules and create detailed summaries using Excel formulas. Delve into the power of Power Query to effortlessly manipulate and analyze large datasets, building dynamic dashboards without the limitations of formulas. Learning Outcomes Gain proficiency in creating automated dashboards using advanced Excel formulas, VBA, and Power Query Develop expertise in financial modeling using prepaid expenses models Master the calculation of amortization schedules and prepaid expenses summaries Implement Power Query to manipulate and analyze large datasets Create dynamic dashboards without the limitations of formulas Enhance your Excel skills and data analysis capabilities Why choose this Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query course? 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 Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query course for? Accounting and finance professionals seeking to enhance their Excel skills for financial modeling and dashboard creation Business analysts and data analysts aiming to expand their expertise in data manipulation and visualization Excel enthusiasts interested in mastering advanced Excel formulas, VBA, and Power Query Individuals seeking to create interactive and insightful dashboards for data-driven decision-making Anyone seeking to elevate their Excel skills and become a proficient data analyst Career path Financial Analyst (£35,000 - £55,000) Business Analyst (£40,000 - £60,000) Data Analyst (£45,000 - £65,000) Management Accountant (£40,000 - £60,000) Business Intelligence Analyst (£45,000 - £65,000) Senior Financial Analyst (£50,000 - £70,000) Prerequisites This Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query 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 Introduction Course Outline and Introduction 00:04:00 Minimum Requirements for the Course 00:01:00 Prepayments Introduction 00:01:00 Prepaid Expenses Models: Resources Download Month End Date Prepaid Expenses Amortization Calculation 00:00:00 Exact Prepaid Expenses Payment Date Calculation 00:00:00 Accounting for Prepaid Expenses Prepaid Expenses Accounting Definition: Prepayments 00:03:00 Prepaid Expense Example: How Accounting works for Prepayments 00:03:00 Advantages and Disadvantages of Prepaid Expenses 00:03:00 Excel Formulas Detailed: Introduction to three Excel Models Introduction to PRO Excel Models and Formulas 00:06:00 Date Function 00:05:00 EOMONTH Function 00:04:00 DATEVALUE function 00:03:00 IF Function 00:08:00 IFS Function (Office 365 Only) 00:07:00 VLOOKUP Function 00:07:00 MATCH Function 00:05:00 INDIRECT Function 00:02:00 NAMED Ranges: Name Manager 00:03:00 Advanced Version of VLOOKUP Function 00:07:00 Formula Based Prepaid Expenses Model Introduction to Model and Control Panel Tab (Important Sheet Tab) 00:08:00 Formula Based Prepaid Expenses Model - Deep Dive (Part 1) 00:05:00 Formula Based Prepaid Expenses Model - Deep Dive (Part 2) 00:06:00 Formula Based Prepaid Expenses Model - Deep Dive (Part 3) 00:06:00 IFS Function - Month End date Prepayment calculation 00:04:00 Prepaid Expenses - Closing Balance Summary Tab (Formula Based Summary) 00:09:00 Protecting Formulas Cells and Fields in the Model 00:04:00 Calculate Prepaid Expenses Amortisation from Exact Start Date Exact Date Prepaid Amortisation calculation Intro 00:03:00 Formulas update and Model Changes for Exact Prepaid Exps Calculation 00:03:00 Formulas Update for Exact Date Prepaid Exps Amortisation (Part 1) 00:04:00 Formulas Update for Exact Date Prepaid Exps Amortisation (Part 2) 00:03:00 Formulas Update for Exact Date Prepaid Exps Amortisation (Part 3) 00:02:00 Formulas Update for Exact Date Prepaid Exps Amortisation (Part 4) 00:07:00 IFS Function - Exact Date Prepayments Amortisation 00:04:00 Data Validation Controls (Enhancing Data Input Controls with Protection) 00:10:00 Bonus: Prepayment Model with Opening Balance Calculation (Part 1) 00:08:00 Bonus: Prepayment Model with Opening Balance Calculation (Part 2) 00:09:00 Additional Material: Resources 00:00:00 Prepaid Expenses Summary with Power Query and Pivot Table Power Query and Pivot Table Prepayment Summary Table Intro 00:06:00 What is Power Query and Some Awesome Resources for Power Query learning 00:07:00 Power Query and Pivot Table Summary - Deep Dive (Part 1) 00:05:00 Power Query and Pivot Table Summary - Deep Dive (Part 2) 00:04:00 Power Query and Pivot Table Summary - Deep Dive (Part 3) 00:05:00 Power Query and Pivot Table Summary - Deep Dive (Part 4) 00:09:00 Using Array Formulas to Add Formula Protection 00:04:00 Bonus: Allocate Prepaid Expenditure Cost Centre Wise - 1 00:02:00 Bonus: Allocate Prepaid Expenditure Cost Centre Wise - 2 00:08:00 Bonus: Prepayment Model with Opening Balance Calculation (PQ and PT Version) 00:13:00 Advanced VBA Prepaid Expenses Amortisation Model Changing Macros Security in Excel 00:05:00 Complete Walkthrough - Advanced VBA Prepaid Expenses Amortisation Model 00:06:00 Bonus : New Version - Excel VBA Model for Prepayment Expenditure 00:08:00 BONUS: Dynamic Dashboard for Divisional Profit and Loss statements: Easy Way Dynamic Dashboard Overview 00:07:00 Importing Profit and Loss Statements Source Files and creating YTD P&L Sheets 00:08:00 Creating Dynamic Data Validation 00:02:00 Creating Named Ranges for Dynamic Table Arrays 00:03:00 Dynamic Date Column Headings for each Divisional PL Table 00:02:00 Dynamic Month and YTD Dashboard tables headings (PRO TIP) 00:03:00 Dynamic VLOOKUP Formula - Preparing First section of the Dashboard 00:04:00 Creating Rolling Dashboard with Dynamic VLOOKUP Function 00:08:00 IMPORTANT : Error Checking for your reports/Dashboard (PRO TIP) 00:03:00 Data Prep for Visualization: AREA Charts (Awesome trick using #NA Function) 00:05:00 Visualization: AREA Charts for Month - Revenue, Gross Profit and Net Profit 00:05:00 Visualization DONUT Charts Revenue, Gross Profit and Net Profit (Part 1) 00:03:00 Visualization DONUT Charts Revenue, Gross Profit and Net Profit (Part 2) 00:06:00 Power Query & Pivot Tables based Dashboard without any Formulas, Fully Dynamic Introduction - Formula-less Dashboard - Fully Dynamic and easily refreshed 00:05:00 Understanding the data files before building dashboard 00:02:00 Consolidating Reports with Power Query (Get & Transform) , How to install PQ 00:08:00 Dynamic File Path Trick in Power Query with Parameters (Amazing trick) 00:06:00 Conditional Cumulative totals with SUMIFS Function 00:04:00 Bonus: Conditional Cumulative totals with Power Query Custom Formula (M Code) 00:06:00 Dashboard Creation - Pivot Table showing Month and YTD KPIs division wise 00:06:00 Dashboard Creation Donuts Charts linked with Pivot Table (Replicate Charts fast) 00:08:00 Dashboard Creation - Line Charts 00:08:00 Update Dashboard with Additional Divisional Data with Few Click (Magical) 00:03:00 Thank you Thank you 00:02:00 Ultimate Prepaid Expenditure Model (Super Bonus) 00:02:00 Resources Resources - Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query 00:00:00 Assignment Assignment - Microsoft Excel: Automated Dashboard Using Advanced Formula VBA Power Query 00:00:00

Do you want to master the essential mathematical skills for data science and machine learning? Do you want to learn how to apply statistics and probability to real-world problems and scenarios? If yes, then this course is for you! In this course, you will learn the advanced concepts and techniques of statistics and probability that are widely used in data science and machine learning. You will learn how to describe and analyse data using descriptive statistics, distributions, and probability theory. You will also learn how to perform hypothesis testing, regressions, ANOVA, and machine learning algorithms to make predictions and inferences from data. You will gain hands-on experience with practical exercises and projects using Python and R. Learning Outcomes By the end of this course, you will be able to: Apply descriptive statistics, distributions, and probability theory to summarise and visualise data Perform hypothesis testing, regressions, ANOVA, and machine learning algorithms to make predictions and inferences from data Use Python and R to implement statistical and machine learning methods Interpret and communicate the results of your analysis using appropriate metrics and visualisations Solve real-world problems and scenarios using statistics and probability Why choose this Advanced Diploma in Statistics & Probability for Data Science & Machine Learning at QLS Level 7 course? 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 Advanced Diploma in Statistics & Probability for Data Science & Machine Learning at QLS Level 7 course for? This course is for anyone who wants to learn the advanced concepts and techniques of statistics and probability for data science and machine learning. This course is suitable for: Data scientists, machine learning engineers, and analysts who want to enhance their skills and knowledge Students and researchers who want to learn the mathematical foundations of data science and machine learning Professionals and managers who want to understand and apply data-driven decision making Hobbyists and enthusiasts who want to explore and learn from data Anyone who loves statistics and probability and wants to challenge themselves Career path Data Scientist (£35,000 - £55,000) Machine Learning Engineer (£40,000 - £60,000) Statistician (£35,000 - £55,000) Data Analyst (£40,000 - £60,000) Business Intelligence Analyst (£45,000 - £65,000) Senior Data Analyst (£50,000 - £70,000) Prerequisites This Advanced Diploma in Statistics & Probability for Data Science & Machine Learning at QLS Level 7 does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Advanced Diploma in Statistics & Probability for Data Science & Machine Learning at QLS Level 7 was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £135 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. Course Curriculum Section 01: Let's get started Welcome! 00:02:00 What will you learn in this course? 00:06:00 How can you get the most out of it? 00:06:00 Section 02: Descriptive statistics Intro 00:03:00 Mean 00:06:00 Median 00:05:00 Mode 00:04:00 Mean or Median? 00:08:00 Skewness 00:08:00 Practice: Skewness 00:01:00 Solution: Skewness 00:03:00 Range & IQR 00:10:00 Sample vs. Population 00:05:00 Variance & Standard deviation 00:11:00 Impact of Scaling & Shifting 00:19:00 Statistical moments 00:06:00 Section 03: Distributions What is a distribution? 00:10:00 Normal distribution 00:09:00 Z-Scores 00:13:00 Practice: Normal distribution 00:04:00 Solution: Normal distribution 00:07:00 Section 04: Probability theory Intro 00:01:00 Probability Basics 00:10:00 Calculating simple Probabilities 00:05:00 Practice: Simple Probabilities 00:01:00 Quick solution: Simple Probabilities 00:01:00 Detailed solution: Simple Probabilities 00:06:00 Rule of addition 00:13:00 Practice: Rule of addition 00:02:00 Quick solution: Rule of addition 00:01:00 Detailed solution: Rule of addition 00:07:00 Rule of multiplication 00:11:00 Practice: Rule of multiplication 00:01:00 Solution: Rule of multiplication 00:03:00 Bayes Theorem 00:10:00 Bayes Theorem - Practical example 00:07:00 Expected value 00:11:00 Practice: Expected value 00:01:00 Solution: Expected value 00:03:00 Law of Large Numbers 00:08:00 Central Limit Theorem - Theory 00:10:00 Central Limit Theorem - Intuition 00:08:00 Central Limit Theorem - Challenge 00:11:00 Central Limit Theorem - Exercise 00:02:00 Central Limit Theorem - Solution 00:14:00 Binomial distribution 00:16:00 Poisson distribution 00:17:00 Real life problems 00:15:00 Section 05: Hypothesis testing Intro 00:01:00 What is a hypothesis? 00:19:00 Significance level and p-value 00:06:00 Type I and Type II errors 00:05:00 Confidence intervals and margin of error 00:15:00 Excursion: Calculating sample size & power 00:11:00 Performing the hypothesis test 00:20:00 Practice: Hypothesis test 00:01:00 Solution: Hypothesis test 00:06:00 T-test and t-distribution 00:13:00 Proportion testing 00:10:00 Important p-z pairs 00:08:00 Section 06: Regressions Intro 00:02:00 Linear Regression 00:11:00 Correlation coefficient 00:10:00 Practice: Correlation 00:02:00 Solution: Correlation 00:08:00 Practice: Linear Regression 00:01:00 Solution: Linear Regression 00:07:00 Residual, MSE & MAE 00:08:00 Practice: MSE & MAE 00:01:00 Solution: MSE & MAE 00:03:00 Coefficient of determination 00:12:00 Root Mean Square Error 00:06:00 Practice: RMSE 00:01:00 Solution: RMSE 00:02:00 Section 07: Advanced regression & machine learning algorithms Multiple Linear Regression 00:16:00 Overfitting 00:05:00 Polynomial Regression 00:13:00 Logistic Regression 00:09:00 Decision Trees 00:21:00 Regression Trees 00:14:00 Random Forests 00:13:00 Dealing with missing data 00:10:00 Section 08: ANOVA (Analysis of Variance) ANOVA - Basics & Assumptions 00:06:00 One-way ANOVA 00:12:00 F-Distribution 00:10:00 Two-way ANOVA - Sum of Squares 00:16:00 Two-way ANOVA - F-ratio & conclusions 00:11:00 Section 09: Wrap up Wrap up 00:01:00 Assignment Assignment - Statistics & Probability for Data Science & Machine Learning 00:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00

Uncover Excel 2019's potential through our comprehensive Microsoft Excel course. It empowers you to master features, calculations, data analysis, and automation. Whether you're new to spreadsheets or aiming for data expertise, this course is tailored for you. Our Microsoft Excel course simplifies Excel's complexities, making it beginner-friendly. It equips you with skills vital in today's data-driven landscape. Beyond personal growth, this Microsoft Excel course boosts career prospects. Excel proficiency is valuable in a competitive job market, opening doors to diverse opportunities. Our course is a transformative journey into Excel 2019, unlocking potential, enhancing skills, and advancing careers. Whether you're a novice or aspiring data pro, it's your key to Excel's power and your potential. Learning Outcomes of our Microsoft Excel course: Master Microsoft Excel 2019's latest features. Perform complex calculations with ease. Create visually appealing and well-formatted worksheets. Analyze and visualize data effectively using charts and PivotTables. Automate workbook tasks with Excel VBA. Why buy this Advanced Diploma in Microsoft Excel Complete Course 2019 at QLS Level 7? 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 Who is this Advanced Diploma in Microsoft Excel Complete Course 2019 at QLS Level 7 for? Individuals new to Microsoft Excel looking to build a strong foundation. Students and job seekers aiming to enhance their employability. Business professionals wanting to improve data management and analysis skills. Entrepreneurs seeking to streamline their business processes. Anyone interested in harnessing the power of Excel for personal or professional growth. Prerequisites This Advanced Diploma in Microsoft Excel Complete Course 2019 at QLS Level 7 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 Data Analyst: £25,000 - £40,000 per year Financial Analyst: £30,000 - £50,000 per year Business Intelligence Analyst: £30,000 - £55,000 per year Operations Manager: £35,000 - £70,000 per year Project Manager: £40,000 - £70,000 per year Excel VBA Developer: £35,000 - £60,000 per year Certification After studying the course materials of the Advanced Diploma in Microsoft Excel Complete Course 2019 at QLS Level 7 you will be able to take the MCQ test that will assess your knowledge. After successfully passing the test you will be able to claim the pdf certificate for £4.99. Original Hard Copy certificates need to be ordered at an additional cost of £8. Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £135 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. Course Curriculum Microsoft Excel 2019 New Features Introduction to Microsoft Excel 2019 New Features 00:07:00 CONCAT 00:02:00 IFS 00:01:00 MAXIFS 00:01:00 MINIFS 00:01:00 SWITCH 00:02:00 TEXTJOIN 00:01:00 Map Chart 00:02:00 Funnel Chart 00:01:00 Better Visuals 00:06:00 Pivot Table Enhancements 00:02:00 Power Pivot Updates 00:01:00 Getting Started with Microsoft Office Excel Navigate the Excel User Interface 00:28:00 Use Excel Commands 00:10:00 Create and Save a Basic Workbook 00:19:00 Enter Cell Data 00:12:00 Use Excel Help 00:05:00 Performing Calculations Create Worksheet Formulas 00:15:00 Insert Functions 00:17:00 Reuse Formulas and Functions 00:17:00 Modifying a Worksheet Insert, Delete, and Adjust Cells, Columns, and Rows 00:10:00 Search for and Replace Data 00:09:00 Use Proofing and Research Tools 00:07:00 Formatting a Worksheet Apply Text Formats 00:16:00 Apply Number Format 00:08:00 Align Cell Contents 00:09:00 Apply Styles and Themes 00:12:00 Apply Basic Conditional Formatting 00:11:00 Create and Use Templates 00:08:00 Printing Workbooks Preview and Print a Workbook 00:10:00 Set Up the Page Layout 00:09:00 Configure Headers and Footers 00:07:00 Managing Workbooks Manage Worksheets 00:05:00 Manage Workbook and Worksheet Views 00:07:00 Manage Workbook Properties 00:06:00 Working with Functions Work with Ranges 00:18:00 Use Specialized Functions 00:11:00 Work with Logical Functions 00:23:00 Work with Date & Time Functions 00:08:00 Work with Text Functions 00:11:00 Working with Lists Sort Data 00:10:00 Filter Data 00:10:00 Query Data with Database Functions 00:09:00 Outline and Subtotal Data 00:09:00 Analyzing Data Apply Intermediate Conditional Formatting 00:07:00 Apply Advanced Conditional Formatting 00:05:00 Visualizing Data with Charts Create Charts 00:13:00 Modify and Format Charts 00:12:00 Use Advanced Chart Features 00:12:00 Using PivotTables and PivotCharts Create a PivotTable 00:13:00 Analyze PivotTable Data 00:12:00 Present Data with PivotCharts 00:07:00 Filter Data by Using Timelines and Slicers 00:11:00 Working with Multiple Worksheets and Workbooks Use Links and External References 00:12:00 Use 3-D References 00:06:00 Consolidate Data 00:05:00 Using Lookup Functions and Formula Auditing Use Lookup Functions 00:12:00 Trace Cells 00:09:00 Watch and Evaluate Formulas 00:08:00 Sharing and Protecting Workbooks Collaborate on a Workbook 00:19:00 Protect Worksheets and Workbooks 00:08:00 Automating Workbook Functionality Apply Data Validation 00:13:00 Search for Invalid Data and Formulas with Errors 00:04:00 Work with Macros 00:18:00 Creating Sparklines and Mapping Data Create Sparklines 00:07:00 MapData 00:07:00 Forecasting Data Determine Potential Outcomes Using Data Tables 00:08:00 Determine Potential Outcomes Using Scenarios 00:09:00 Use the Goal Seek Feature 00:04:00 Forecasting Data Trends 00:05:00 Excel VBA Data Management Create a Macro Using the Macro Recorder 01:00:00 Edit a Macro 01:00:00 Debug a Macro 00:30:00 Customize the Quick Access Toolbar and Hotkeys 00:30:00 Set Macro Security 01:00:00 Insert Text 00:30:00 Format Text 00:30:00 Sort Data 00:30:00 Duplicate Data 01:00:00 Generate a Report 01:00:00 Determine the Dialog Box Type 00:15:00 Capture User Input 01:00:00 Insert, Copy, and Delete Worksheets 00:30:00 Rename Worksheets 00:30:00 Modify the Order of Worksheets 00:15:00 Print Worksheets 00:30:00 Create User-Defined Functions 00:30:00 Automate SUM Functions 00:30:00 Excel Templates Excel Templates 00:00:00 Resources Resources - Microsoft Excel - Beginner Course - Cpd Accredited 00:00:00 Mock Exam Mock Exam - Microsoft Excel Complete Course 2019 00:20:00 Final Exam Final Exam - Microsoft Excel Complete Course 2019 00:20:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00

Delve into the world of data analysis with 'R Programming for Data Science,' a course designed to guide learners through the intricacies of R, a premier programming language in the data science domain. The course opens with a broad perspective on data science, illuminating the pivotal role of R in this field. Learners are then introduced to R and RStudio, equipping them with the foundational tools and interfaces essential for R programming. The curriculum progresses with an introduction to the basics of R, ensuring learners grasp the core principles that underpin more complex operations. A highlight of this course is its in-depth exploration of R's versatile data structures, including vectors, matrices, factors, and data frames. Each unit is crafted to provide learners with a comprehensive understanding of these structures, pivotal for effective data handling and manipulation. The course also emphasizes the importance of relational and logical operators in R, key elements for executing data operations. As the course advances, learners will engage with the nuances of conditional statements and loops, essential for writing efficient and dynamic R scripts. Moving into more advanced territories, the course delves into the creation and usage of functions, an integral part of R programming, and the exploration of various R packages that extend the language's capabilities. Learners will also gain expertise in the 'apply' family of functions, crucial for streamlined data processing. Further units cover regular expressions and effective strategies for managing dates and times in data sets. The course concludes with practical applications in data acquisition, cleaning, visualization, and manipulation, ensuring learners are well-prepared to tackle real-world data science challenges using R. Learning Outcomes Develop a foundational understanding of R's role in data science and proficiency in RStudio. Gain fluency in R programming basics, enabling the handling of complex data tasks. Acquire skills in managing various R data structures for efficient data analysis. Master relational and logical operations for advanced data manipulation in R. Learn to create functions and utilize R packages for expanded analytical capabilities. Why choose this R Programming for Data Science course? 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 R Programming for Data Science course for? Beginners in data science eager to learn R programming. Data analysts and scientists looking to enhance their skills in R. Researchers in various fields requiring advanced data analysis tools. Statisticians seeking to adopt R for more sophisticated data manipulations. Professionals in finance, healthcare, and other sectors needing data-driven insights. Career path Data Scientist (R Expertise): £30,000 - £70,000 Data Analyst (R Programming Skills): £27,000 - £55,000 Bioinformatics Scientist (R Proficiency): £35,000 - £60,000 Quantitative Analyst (R Knowledge): £40,000 - £80,000 Research Analyst (R Usage): £25,000 - £50,000 Business Intelligence Developer (R Familiarity): £32,000 - £65,000 Prerequisites This R Programming for Data Science does not require you to have any prior qualifications or experience. You can just enrol and start learning.This R Programming for Data Science was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Unit 01: Data Science Overview Introduction to Data Science 00:01:00 Data Science: Career of the Future 00:04:00 What is Data Science? 00:02:00 Data Science as a Process 00:02:00 Data Science Toolbox 00:03:00 Data Science Process Explained 00:05:00 What's next? 00:02:00 Unit 02: R and RStudio Engine and coding environment 00:03:00 Installing R and RStudio 00:04:00 RStudio: A quick tour 00:04:00 Unit 03: Introduction to Basics Arithmetic with R 00:03:00 Variable assignment 00:04:00 Basic data types in R 00:03:00 Unit 04: Vectors Creating a vector 00:05:00 Naming a vector 00:04:00 Arithmetic calculations on vectors 00:07:00 Vector selection 00:06:00 Selection by comparison 00:04:00 Unit 05: Matrices What's a Matrix? 00:02:00 Analyzing Matrices 00:03:00 Naming a Matrix 00:05:00 Adding columns and rows to a matrix 00:06:00 Selection of matrix elements 00:03:00 Arithmetic with matrices 00:07:00 Additional Materials 00:00:00 Unit 06: Factors What's a Factor? 00:02:00 Categorical Variables and Factor Levels 00:04:00 Summarizing a Factor 00:01:00 Ordered Factors 00:05:00 Unit 07: Data Frames What's a Data Frame? 00:03:00 Creating Data Frames 00:20:00 Selection of Data Frame elements 00:03:00 Conditional selection 00:03:00 Sorting a Data Frame 00:03:00 Additional Materials 00:00:00 Unit 08: Lists Why would you need lists? 00:01:00 Creating a List 00:06:00 Selecting elements from a list 00:03:00 Adding more data to the list 00:02:00 Additional Materials 00:00:00 Unit 09: Relational Operators Equality 00:03:00 Greater and Less Than 00:03:00 Compare Vectors 00:03:00 Compare Matrices 00:02:00 Additional Materials 00:00:00 Unit 10: Logical Operators AND, OR, NOT Operators 00:04:00 Logical operators with vectors and matrices 00:04:00 Reverse the result: (!) 00:01:00 Relational and Logical Operators together 00:06:00 Additional Materials 00:00:00 Unit 11: Conditional Statements The IF statement 00:04:00 IFELSE 00:03:00 The ELSEIF statement 00:05:00 Full Exercise 00:03:00 Additional Materials 00:00:00 Unit 12: Loops Write a While loop 00:04:00 Looping with more conditions 00:04:00 Break: stop the While Loop 00:04:00 What's a For loop? 00:02:00 Loop over a vector 00:02:00 Loop over a list 00:03:00 Loop over a matrix 00:04:00 For loop with conditionals 00:01:00 Using Next and Break with For loop 00:03:00 Additional Materials 00:00:00 Unit 13: Functions What is a Function? 00:02:00 Arguments matching 00:03:00 Required and Optional Arguments 00:03:00 Nested functions 00:02:00 Writing own functions 00:03:00 Functions with no arguments 00:02:00 Defining default arguments in functions 00:04:00 Function scoping 00:02:00 Control flow in functions 00:03:00 Additional Materials 00:00:00 Unit 14: R Packages Installing R Packages 00:01:00 Loading R Packages 00:04:00 Different ways to load a package 00:02:00 Additional Materials 00:00:00 Unit 15: The Apply Family - lapply What is lapply and when is used? 00:04:00 Use lapply with user-defined functions 00:03:00 lapply and anonymous functions 00:01:00 Use lapply with additional arguments 00:04:00 Additional Materials 00:00:00 Unit 16: The apply Family - sapply & vapply What is sapply? 00:02:00 How to use sapply 00:02:00 sapply with your own function 00:02:00 sapply with a function returning a vector 00:02:00 When can't sapply simplify? 00:02:00 What is vapply and why is it used? 00:04:00 Additional Materials 00:00:00 Unit 17: Useful Functions Mathematical functions 00:05:00 Data Utilities 00:08:00 Additional Materials 00:00:00 Unit 18: Regular Expressions grepl & grep 00:04:00 Metacharacters 00:05:00 sub & gsub 00:02:00 More metacharacters 00:04:00 Additional Materials 00:00:00 Unit 19: Dates and Times Today and Now 00:02:00 Create and format dates 00:06:00 Create and format times 00:03:00 Calculations with Dates 00:03:00 Calculations with Times 00:07:00 Additional Materials 00:00:00 Unit 20: Getting and Cleaning Data Get and set current directory 00:04:00 Get data from the web 00:04:00 Loading flat files 00:03:00 Loading Excel files 00:05:00 Additional Materials 00:00:00 Unit 21: Plotting Data in R Base plotting system 00:03:00 Base plots: Histograms 00:03:00 Base plots: Scatterplots 00:05:00 Base plots: Regression Line 00:03:00 Base plots: Boxplot 00:03:00 Unit 22: Data Manipulation with dplyr Introduction to dplyr package 00:04:00 Using the pipe operator (%>%) 00:02:00 Columns component: select() 00:05:00 Columns component: rename() and rename_with() 00:02:00 Columns component: mutate() 00:02:00 Columns component: relocate() 00:02:00 Rows component: filter() 00:01:00 Rows component: slice() 00:04:00 Rows component: arrange() 00:01:00 Rows component: rowwise() 00:02:00 Grouping of rows: summarise() 00:03:00 Grouping of rows: across() 00:02:00 COVID-19 Analysis Task 00:08:00 Additional Materials 00:00:00 Assignment Assignment - R Programming for Data Science 00:00:00

Are you ready to be at the helm, steering the ship into a realm where data is the new gold? In the infinite world of data, where information spirals at breakneck speed, lies a universe rich in potential and discovery: the domain of Data Science and Visualisation. This 'Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3' course unravels the wonders of extracting meaningful insights using Python, the worldwide leading language of data experts. Harnessing the strength of Python, you'll delve deep into data analysis, experience the finesse of visualisation tools, and master the art of Machine Learning. The need to understand, interpret, and act on this data has become paramount, with vast amounts of data increasing the digital sphere. Envision a canvas where raw numbers are transformed into visually compelling stories, and machine learning models foretell future trends. This course provides a meticulous pathway for anyone eager to learn the data representation paradigms backed by Python's robust libraries. Dive into a curriculum rich with analytical explorations, visual artistry, and machine learning predictions. Learning Outcomes Understanding the foundations and functionalities of Python, focusing on its application in data science. Applying various Python libraries like NumPy and Pandas for effective data analysis. Demonstrating proficiency in creating detailed visual narratives using tools like matplotlib, Seaborn, and Plotly. Implementing Machine Learning algorithms in Python using scikit-learn, ranging from regression models to clustering techniques. Designing and executing a holistic data analysis and visualisation project, encapsulating all learned techniques. Exploring advanced topics, encompassing recommender systems and natural language processing with Python. Attaining the confidence to independently analyse complex data sets and translate them into actionable insights.   Video Playerhttps://studyhub.org.uk/wp-content/uploads/2021/03/Data-Science-and-Visualisation-with-Machine-Learning.mp400:0000:0000:00Use Up/Down Arrow keys to increase or decrease volume. Why buy this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Who is this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 course for? Aspiring data scientists aiming to harness the power of Python. Researchers keen to enrich their analytical and visualisation skills. Analysts aiming to add machine learning to their toolkit. Developers striving to integrate data analytics into applications. Business professionals desiring data-driven decision-making capabilities. Career path Data Scientist: £55,000 - £85,000 Per Annum Machine Learning Engineer: £60,000 - £90,000 Per Annum Data Analyst: £30,000 - £50,000 Per Annum Data Visualisation Specialist: £45,000 - £70,000 Per Annum Natural Language Processing Specialist: £65,000 - £95,000 Per Annum Business Intelligence Developer: £40,000 - £65,000 Per Annum Prerequisites This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £85 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. Course Curriculum Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Visualisation with Machine Learning 00:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00

Level 7 QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support

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