269 Linear courses in Cardiff delivered On Demand

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 Introduction Welcome to the course 00:02:00 Step 1: Pricing Policy and Pricing Objective 6 Steps of setting a Pricing policy 00:03:00 Different Pricing Objectives 00:07:00 Step 2: 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 Elasticity2 00:05:00 Excel: Estimating Power Demand Curve with points2 00:03:00 Subjective Demand curve 00:01:00 Excel: Estimating Subjective Demand Curve2 00:02:00 Excel: Maximizing Revenue using Excel Solver 00:08:00 Step 3: Estimating Costs Estimating the cost function 00:05:00 Excel: Modeling Cost Function and Maximizing Profit 00:06:00 Including effect of complementary goods 00:01:00 Excel: Effect of complementary goods 00:05:00 Step 4: Analyzing competitors Analyzing Competitors 00:02:00 Step 5a : Price Bundling Strategy 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 Excel: Solving Bundling problem (Price Reversal) 00:08:00 Step 5b: Non-Linear Pricing Strategies Non-Linear Pricing Strategies 00:03:00 Willingness to Pay of customers 00:03:00 Willingness to Pay of customers 00:03:00 Example Problem Statement 00:01:00 Excel: Standard Quantity Discounts 00:21:00 Excel: Two-Tier Pricing 00:04:00 Step 5c: Price Skimming Price Skimming Strategy 00:05:00 Excel: Price Skimming Strategy 00:10:00 Step 5d: Revenue Management Revenue Management 00:03:00 Excel: Handling Uncertainity 00:07:00 Appendix: Using Lookup functions 00:08:00 Appendix 1: Excel Crash Course 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 Excel Charts: Categories Of Messages That Can Be Conveyed 00:04:00 Elements Of Charts 00:05:00 The Easy Way Of Creating Charts 00:03:00 Bar And Column Charts 00:12:00 Congratulations 00:01:00 Certificate of Achievement Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00

Dive into the world of image segmentation with PyTorch. From tensors to UNet and FPN architectures, grasp the theory behind convolutional neural networks, loss functions, and evaluation metrics. Learn to mold data and tackle real-world projects, equipping developers and data scientists with versatile deep-learning skills.

Overview This comprehensive course on MATLAB Simulink for Electrical Power Engineering will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This MATLAB Simulink for Electrical Power Engineering 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 MATLAB Simulink for Electrical Power Engineering. It is available to all students, of all academic backgrounds. Requirements Our MATLAB Simulink for Electrical Power Engineering 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 Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 8 sections • 47 lectures • 13:24:00 total length •Module 1- Solving One Non Linear Equation in MATLAB Using Fzero Function: 00:15:00 •Module 2-Example 1 on Solving Multiple Non Linear Equations in MATLAB Using Fsolve Function: 00:15:00 •Module 3- Example 2 on Solving Multiple Non Linear Equations in Matlab Using Fsolve: 00:13:00 •Module 4-Application Multi Level Inverter Part 1: 00:25:00 •Module 5- Application Multi Level Inverter Part 2: 00:05:00 •Module 1-Introduction to MATLAB Simulations Using Simulink: 00:04:00 •Module 2-Half Wave Uncontrolled Rectifier with R Load Principle of Operation: 00:21:00 •Module 3- Half Wave Controlled Rectifier R Load Principle of Operation: 00:05:00 •Module 4-Simulation of Half Wave Controlled Rectifier Using Simulink In Matlab: 00:26:00 •Module 5- Principle of Operation of Fully Controlled Bridge Rectifier Part 1: 00:06:00 •Module 6- Principle of Operation of Fully Controlled Bridge Rectifier Part 2: 00:06:00 •Module 7-Simulation of Bridge Controlled Rectifier: 00:16:00 •Module 8-AC Chopper with R Load Principle of Operation: 00:14:00 •Module 9- Simulation of AC Chopper with R and RL Loads in MATLAB: 00:11:00 •Module 10- Buck Regulator Principle of Operation Part 1: 00:16:00 •Module 11-Buck Regulator Principle of Operation Part 2: 00:17:00 •Module 12-Simulation of Buck Regulator in MATLAB: 00:14:00 •Module 13-Boost Regulator Principle of Operation: 00:23:00 •Module 14- Simulation of Boost Regulator in MATLAB: 00:12:00 •Module 15-Buck-Boost Regulator Principle of Operation: 00:17:00 •Module 16- Simulation of Buck-Boost Regulator: 00:09:00 •Module 17- Single Phase Half Bridge R-Load: 00:15:00 •Module 18- Single Phase Half Bridge RL-Load: 00:08:00 •Module 19-Simulation of Single Phase Half Bridge Inverter: 00:18:00 •Module 20-Single Phase Bridge Inverter R-Load: 00:06:00 •Module 21-Single Phase Bridge Inverter RL-Load: 00:07:00 •Module 22-Simulation of Single Phase Bridge Inverter: 00:10:00 •Module 23-Three Phase Inverters and Obtaining The Line Voltages: 00:15:00 •Module 24-Three Phase Inverters and Obtaining The Phase Voltages: 00:17:00 •Module 25-Simulation of Three Phase Inverter: 00:17:00 •Module 26-Simulation of Charging and Discharging Capacitor Using Matlab: 00:10:00 •Module 1-Separately Excited DC Machine: 00:21:00 •Module 2-DC Motor Modelling without Load Using Simulink in MATLAB: 00:25:00 •Module 3-DC Motor Modelling with Load Using Simulink in MALTAB: 00:23:00 •Module 4-DC Motor Block Simulation Using Power Library in MATLAB: 00:16:00 •Module 1-Construction and Principle of Operation of Synchronous Generator: 00:29:00 •Module 2-Equivalent Circuit and Phasor Diagram of Non Salient Synchronous Machine: 00:29:00 •Module 3-Equivalent Circuit and Phasor Diagram of Salient Synchronous Machine: 00:39:00 •Module 4-Simulation of Synchronous Machine Connected to Small Power System: 00:38:00 •Module 1-Construction and Theory of Operation of Induction Machines: 00:27:00 •Module 2-Equivalent Circuit and Power Flow in Induction Motor: 00:23:00 •Module 3-Torque-Speed Characteristics of Induction Motor: 00:20:00 •Module 4- Simulation of Induction Motor or Asynchronous Motor Using Simulink: 00:33:00 •Module 1- Importing Data from PSCAD Program for Fault Location Detection to MATLAB Program: 00:37:00 •Module 1-How to Implement PID Controller in Simulink of MATLAB: 00:14:00 •Module 2-Tuning a PID Controller In MATLAB Simulink: 00:17:00 •Assignment - MATLAB Simulink for Electrical Power Engineering: 00:00:00

The 'MATLAB Simulink for Electrical Power Engineering' course focuses on practical applications and simulations using MATLAB and Simulink for power electronics, solar energy, DC motors, synchronous generators, and induction motors. It aims to provide participants with hands-on experience in electrical power engineering simulations and analysis using MATLAB and Simulink. Learning Outcomes: Understand the applications of matrices in MATLAB and solve non-linear equations using appropriate functions. Simulate power electronics circuits, including rectifiers, choppers, regulators, and inverters, using Simulink in MATLAB. Analyze and simulate solar energy systems and separately excited DC machines in MATLAB. Model and simulate synchronous generators connected to a small power system using MATLAB and Simulink. Simulate induction motors and study their equivalent circuits and torque-speed characteristics using Simulink. Implement PID controllers in Simulink and tune them for effective control in power systems simulations. Acquire hands-on skills in using MATLAB and Simulink to perform various electrical power engineering simulations. Apply MATLAB and Simulink tools to solve practical electrical power engineering problems. Develop an in-depth understanding of power electronics, motor simulations, and solar energy systems. Successfully complete the course with the ability to perform advanced electrical power engineering simulations using MATLAB and Simulink. Why buy this MATLAB Simulink for Electrical Power Engineering? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the MATLAB Simulink for Electrical Power Engineering there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This MATLAB Simulink for Electrical Power Engineering course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This MATLAB Simulink for Electrical Power Engineering does not require you to have any prior qualifications or experience. You can just enrol and start learning.This MATLAB Simulink for Electrical Power Engineering was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This MATLAB Simulink for Electrical Power Engineering is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Unit 1- Applications on Matrices in MATLAB Module 1- Solving One Non Linear Equation in MATLAB Using Fzero Function 00:15:00 Module 2-Example 1 on Solving Multiple Non Linear Equations in MATLAB Using Fsolve Function 00:15:00 Module 3- Example 2 on Solving Multiple Non Linear Equations in Matlab Using Fsolve 00:13:00 Module 4-Application Multi Level Inverter Part 1 00:25:00 Module 5- Application Multi Level Inverter Part 2 00:05:00 Unit 2-Power Electronics Simulations Using Simulink in MATLAB Module 1-Introduction to MATLAB Simulations Using Simulink 00:04:00 Module 2-Half Wave Uncontrolled Rectifier with R Load Principle of Operation 00:21:00 Module 3- Half Wave Controlled Rectifier R Load Principle of Operation 00:05:00 Module 4-Simulation of Half Wave Controlled Rectifier Using Simulink In Matlab 00:26:00 Module 5- Principle of Operation of Fully Controlled Bridge Rectifier Part 1 00:06:00 Module 6- Principle of Operation of Fully Controlled Bridge Rectifier Part 2 00:06:00 Module 7-Simulation of Bridge Controlled Rectifier 00:16:00 Module 8-AC Chopper with R Load Principle of Operation 00:14:00 Module 9- Simulation of AC Chopper with R and RL Loads in MATLAB 00:11:00 Module 10- Buck Regulator Principle of Operation Part 1 00:16:00 Module 11-Buck Regulator Principle of Operation Part 2 00:17:00 Module 12-Simulation of Buck Regulator in MATLAB 00:14:00 Module 13-Boost Regulator Principle of Operation 00:23:00 Module 14- Simulation of Boost Regulator in MATLAB 00:12:00 Module 15-Buck-Boost Regulator Principle of Operation 00:17:00 Module 16- Simulation of Buck-Boost Regulator 00:09:00 Module 17- Single Phase Half Bridge R-Load 00:15:00 Module 18- Single Phase Half Bridge RL-Load 00:08:00 Module 19-Simulation of Single Phase Half Bridge Inverter 00:18:00 Module 20-Single Phase Bridge Inverter R-Load 00:06:00 Module 21-Single Phase Bridge Inverter RL-Load 00:07:00 Module 22-Simulation of Single Phase Bridge Inverter 00:10:00 Module 23-Three Phase Inverters and Obtaining The Line Voltages 00:15:00 Module 24-Three Phase Inverters and Obtaining The Phase Voltages 00:17:00 Module 25-Simulation of Three Phase Inverter 00:17:00 Module 26-Simulation of Charging and Discharging Capacitor Using Matlab 00:10:00 Unit 3- Solar Energy Simulation Using Simulink in MATLAB Module 1-Separately Excited DC Machine 00:21:00 Module 2-DC Motor Modelling without Load Using Simulink in MATLAB 00:25:00 Module 3-DC Motor Modelling with Load Using Simulink in MALTAB 00:23:00 Module 4-DC Motor Block Simulation Using Power Library in MATLAB 00:16:00 Unit 4- DC Motor Simulation Using Simulink in MATLAB Module 1-Construction and Principle of Operation of Synchronous Generator 00:29:00 Module 2-Equivalent Circuit and Phasor Diagram of Non Salient Synchronous Machine 00:29:00 Module 3-Equivalent Circuit and Phasor Diagram of Salient Synchronous Machine 00:39:00 Module 4-Simulation of Synchronous Machine Connected to Small Power System 00:38:00 Unit 5- Induction Motor Simulation Using Simulink in MATLAB Module 1-Construction and Theory of Operation of Induction Machines 00:27:00 Module 2-Equivalent Circuit and Power Flow in Induction Motor 00:23:00 Module 3-Torque-Speed Characteristics of Induction Motor 00:20:00 Module 4- Simulation of Induction Motor or Asynchronous Motor Using Simulink 00:33:00 Unit 6- Synchronous Generator Simulation in Simulink of MATLAB Module 1- Importing Data from PSCAD Program for Fault Location Detection to MATLAB Program 00:37:00 Unit 7- Power System Simulations Module 1-How to Implement PID Controller in Simulink of MATLAB 00:14:00 Module 2-Tuning a PID Controller In MATLAB Simulink 00:17:00 Assignment Assignment - MATLAB Simulink for Electrical Power Engineering 00:00:00

14 in 1 "Solidworks" Bundle only at £100 Get Hard Copy + PDF Certificates + Transcript + Student ID Card + e-Learning App as a Gift - Enrol Now Tired of browsing and searching for course you are looking for? Can't find the complete package that fulfils all your needs? Then don't worry as you have just found the solution. Take a minute and look through this extensive bundle that has everything you need to succeed. After surveying thousands of learners just like you and considering their valuable feedback, this all-in-one Solidworks bundle has been designed by industry experts. We prioritised what learners were looking for in a complete package and developed this in-demand course that will enhance your skills and prepare you for the competitive job market. Also, our experts are available for answering your queries on Solidworks and help you along your learning journey. Advanced audio-visual learning modules of these Solidworks courses are broken down into little chunks so that you can learn at your own pace without being overwhelmed by too much material at once. Furthermore, to help you showcase your expertise in Solidworks, we have prepared a special gift of 1 hardcopy certificate and 1 PDF certificate for the title course completely free of cost. These certificates will enhance your credibility and encourage possible employers to pick you over the rest. This Bundle Consists of the following Premium courses: Course 01: Solidworks: Beginner to Advanced Course 02: Solidworks Foundation Training Course 03: Finite Element Analysis Using Solidworks Course 04: Drawing and Illustration Level 2 Course 05: Digital Art - Sketching In Photoshop Course 06: Diploma in Animation Design Course 07: Level 2 Adobe Graphics Design Course Course 08: Adobe Illustrator CC Masterclass Course 09: Advanced Diploma in User Experience UI/UX Design Course 10: Video Editing Course 11: Toon Boom: Create Your First Character Course 12: Freelancing on UpWork Course 13: Essential IT Skills Level 3 Course 14: Decision Making and Critical Thinking Enrol now in Solidworks to advance your career, and use the premium study materials from Apex Learning. The bundle incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can strengthen your expertise and essential knowledge, which will assist you in reaching your goal. Curriculum of this Bundle Course 01: Solidworks: Beginner to Advanced Introduction About the course Course concept Getting to Know Solidworks Creating a New Document User Interface Mouse Movements - Sketch Mouse Movements - Part Sketch Creating a Sketch Document Basic Sketch Tools Dimensions and Relations Mirror Entities Linear and Circular Sketch Pattern Units Sketch to Part Conversion Opening/Saving Part Document Part Modeling Basics Extruded Boss Base Extruded Boss Base Additional Extruded Cut Sketch on the Part Sketch on the New Plane Draft Shell Fillet and Chamfer Convert and Offset Entities Lofted Boss Revolved Boss Swept Boss Part and Sketch Modification Part Modeling Advanced 3D Sketch Density and Material Measuring Tools Linked Dimensions Equations Mirror Hole Wizard Reference Geometry Feature Scope Configurations And Design Table Creating a Design Table Adding Configurations Assembly Basics Inserting Part Relative to the Origin Inserting Parts Relative to Planes Standard Mates Move Components Mirror Component Assembly Advanced Distance and Angle Profile Centre Symmetry and Width Path Mate Linear Coupler Collision Detection Interference Detection Assembly Features Subassembly Drawing Basics Creating a Drawing Sheet Orthographic Views and Edit Sheet Projected View and View Properties Smart Dimension and Model Item Saving Options Relative to Model View View Auxiliary View Detail View Broken Out Section Break View Alternate Position View View Focus Drawing Advanced Bill of Materials (BOM) Bom Sort Missing Item in Bom Custom Properties Equations in Bom Show/Hide Component Linked Notes Measuring Tool Isometric View Appearance, Scene and Rendering Applying Materials Appearance Working with Woods Working with Glass Scene Light Camera Decals Text Options Rendering Panel Schedule Rendering Animation Creating Motion Study Animation Wizard Applying Motor Appearance and Camera Position Walkthrough Part-1 Walkthrough Part-2 How will I get my Certificate? After successfully completing the course, you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement. PDF Certificate: Free (For The Title Course ) Hard Copy Certificate: Free (For The Title Course ) CPD 145 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this bundle. Requirements Our course is fully compatible with PCs, Macs, laptops, tablets and Smartphone devices. Career path Having this expertise will increase the value of your CV and open you up to multiple job sectors. Certificates Certificate of completion Digital certificate - Included Certificate of completion Hard copy certificate - Included P.S. The delivery charge inside the UK is £3.99, and the international students have to pay £9.99.

Description: Do you ever notice things like audio levels, camera angle and focus, attention to detail, or production value while watching a video or a movie? If yes, then you will definitely want to became a part of out video production and editing program. This course is designed specifically for individuals with no previous experience as it begin by equipping students with basic skills in video production and editing. Students gain the skills required for camera operation, lighting a film and recording sound. They will also be given the opportunity to shoot a short video and finally edit and complete their collaborative project. Who is the course for? Existing journalism Media students Practitioners who seek to gain a better understanding of the digital and production elements related to their role Amateurs who wish to improve and polish their skills in the fields of digital video production and post-production Anyone interested in set-up Non-Linear Video Editing Studio at home with own Computer 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: Video Creation Secrets course is a useful qualification to possess, and would be beneficial for the following careers: Video Editor with any TV Channel and Production House Freelance Video Editor Sound Recordist Can set-up Non-Linear Video Editing Studio at home with own Computer. Video Creation Secrets Introduction 00:30:00 The Tools That You Must Need 01:00:00 How to Choose the Right Web Cam? 00:30:00 Creating and Editing Software for a Video Product 00:30:00 Plan! Before You Commence Shooting Your Video Product 01:00:00 How to Edit Your Video Product 01:00:00 How to Create a Video Tutorial for Your Customers 01:00:00 How to Include the Finished Video Product on Your Website 01:00:00 A Video Product Can Help Boosting Traffic and Sales to Your Website 00:30:00 Conclusion 00:15:00 Mock Exam Mock Exam- Video Creation Secrets 00:20:00 Final Exam Final Exam- Video Creation Secrets 00:20:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

Getting Started The QUALIFI Level 3 Diploma in Data Science aims to offer learners a comprehensive introduction to data science. This Level 3 Diploma provides a modern and all-encompassing overview of data science, artificial intelligence, and machine learning. It covers the evolution of artificial intelligence and machine learning from their beginnings in the late 1950s to the emergence of the "big data" era in the early 2000s. It extends to the current AI and machine learning applications, including the associated challenges. In addition to covering standard machine learning models like linear and logistic regression, decision trees, and k-means clustering, this diploma introduces learners to two emerging areas of data science: synthetic data and graph data science. Moreover, the diploma familiarizes learners with the landscape of data analysis and the relevant analytical tools. It includes introducing Python programming so learners can effectively analyse, explore, and visualize data and implement fundamental data science models. Key Benefits Acquire the essential mathematical and statistical knowledge necessary for conducting fundamental data analysis. Cultivate analytical and machine learning proficiency using Python. Foster a solid grasp of data and its related processes, encompassing data cleaning, data structuring, and data preparation for analysis and visualisation. Gain insight into the expansive data science landscape and ecosystem, including relational databases, graph databases, programming languages like Python, visualisation tools, and various analytical instruments. Develop expertise in comprehending the machine learning procedures, including the ability to discern which algorithms are suited for distinct problems and to navigate the steps involved in constructing, testing, and validating a model. Attain an understanding of contemporary and emerging facets of data science and their applicability to modern challenges Key Highlights This course module prepares learners for higher-level Data science positions through personal and professional development. We will ensure your access to the first-class education needed to achieve your goals and dreams and to maximize future opportunities. Remember! The assessment for the Qualification is done based on assignments only, and you do not need to worry about writing any exam. With the School of Business and Technology London, you can complete the Qualification at your own pace, choosing online or blended learning from the comfort of your home. Learning and pathway materials and study guides developed by our qualified tutors will be available around the clock in our cutting-edge learning management system. Most importantly, at the School of Business and Technology London, we will provide comprehensive tutor support through our dedicated support desk. If you choose your course with blended learning, you will also enjoy live sessions with an assigned tutor, which you can book at your convenience. Career Pathways Upon completing the QUALIFI Level 3 Diploma in Data Science, learners can advance their studies or pursue employment opportunities. Data Analyst with an estimated average salary of £39,445 per annum Business Intelligence Analyst with an estimated average salary of £40,000 per annum Data entry specialist with an estimated average salary of £22,425 per annum Database Administrator with an estimated average salary of £44,185 per annum About Awarding Body QUALIFI, recognised by Ofqual awarding organisation has assembled a reputation for maintaining significant skills in a wide range of job roles and industries which comprises Leadership, Hospitality & Catering, Health and Social Care, Enterprise and Management, Process Outsourcing and Public Services. They are liable for awarding organisations and thereby ensuring quality assurance in Wales and Northern Ireland. What is included? Outstanding tutor support that gives you supportive guidance all through the course accomplishment through the SBTL Support Desk Portal. Access our cutting-edge learning management platform to access vital learning resources and communicate with the support desk team. Quality learning materials such as structured lecture notes, study guides, and practical applications, which include real-world examples and case studies, will enable you to apply your knowledge. Learning materials are provided in one of the three formats: PDF, PowerPoint, or Interactive Text Content on the learning portal. The tutors will provide Formative assessment feedback to improve the learners' achievements. Assessment materials are accessible through our online learning platform. Supervision for all modules. Multiplatform accessibility through an online learning platform facilitates SBTL in providing learners with course materials directly through smartphones, laptops, tablets or desktops, allowing students to study at their convenience. Live Classes (for Blended Learning Students only) Assessment Time-constrained scenario-based assignments No examinations Entry Requirements The qualification has been intentionally designed to ensure accessibility without imposing artificial barriers that limit entry. To enrol in this qualification, applicants must be 18 years of age or older. Admittance to the qualification will be managed through centre-led registration processes, which may involve interviews or other appropriate procedures. Despite the presence of advanced mathematics and statistics in higher-level data science courses, encompassing subjects such as linear algebra and differential calculus, this Level 3 Diploma only requires learners to be comfortable with mathematics at the GCSE level. The diploma's mathematical and statistical concepts are based on standard mathematical operations like addition, multiplication, and division. Before commencing the Level 3 Diploma in Data Science, learners are expected to meet the following minimum requirements: i) GCSE Mathematics with a grade of B or higher (equivalent to the new level 6 or above); and ii) GCSE English with a grade of C or higher (equivalent to the new level 4 or above). Furthermore, prior coding experience is not mandatory, although learners should be willing and comfortable with learning Python. Python has been selected for its user-friendly and easily learnable nature. In exceptional circumstances, applicants with substantial experience but lacking formal qualifications may be considered for admission, contingent upon completing an interview and demonstrating their ability to meet the demands of the capability. Progression Upon successful completion of the QUALIFI Level 3 Diploma in Data Science, learners will have several opportunities: Progress to QUALIFI Level 4 Diploma in Data Science: Graduates can advance their education and skills by enrolling in the QUALIFI Level 4 Diploma in Data Science, which offers a more advanced and comprehensive study of the field. Apply for Entry to a UK University for an Undergraduate Degree: This qualification opens doors to higher education, allowing learners to apply for entry to a UK university to pursue an undergraduate degree in a related field, such as data science, computer science, or a related discipline. Progress to Employment in an Associated Profession: Graduates of this program can enter the workforce and seek employment opportunities in professions related to data science, artificial intelligence, machine learning, data analysis, and other relevant fields. These progression options provide learners with a diverse range of opportunities for further education, career advancement, and professional development in the dynamic and rapidly evolving field of data science Why gain a QUALIFI Qualification? This suite of qualifications provides enormous opportunities to learners seeking career and professional development. The highlighting factor of this qualification is that: The learners attain career path support who wish to pursue their career in their denominated sectors; It helps provide a deep understanding of the health and social care sector and managing the organisations, which will, in turn, help enhance the learner's insight into their chosen sector. The qualification provides a real combination of disciplines and skills development opportunities. The Learners attain in-depth awareness concerning the organisation's functioning, aims and processes. They can also explore ways to respond positively to this challenging and complex health and social care environment. The learners will be introduced to managing the wide range of health and social care functions using theory, practice sessions and models that provide valuable knowledge. As a part of this suite of qualifications, the learners will be able to explore and attain hands-on training and experience in this field. Learners also acquire the ability to face and solve issues then and there by exposure to all the Units. The qualification will also help to Apply scientific and evaluative methods to develop those skills. Find out threats and opportunities. Develop knowledge in managerial, organisational and environmental issues. Develop and empower critical thinking and innovativeness to handle problems and difficulties. Practice judgement, own and take responsibility for decisions and actions. Develop the capacity to perceive and reflect on individual learning and improve their social and other transferable aptitudes and skills Learners must request before enrolment to interchange unit(s) other than the preselected units shown in the SBTL website because we need to make sure the availability of learning materials for the requested unit(s). SBTL will reject an application if the learning materials for the requested interchange unit(s) are unavailable. Learners are not allowed to make any request to interchange unit(s) once enrolment is complete. UNIT1- The Field of Data Science Reference No : H/650/4951 Credit : 6 || TQT : 60 This unit provides learners with an introduction to the field of data science, tracing its origins from the emergence of artificial intelligence and machine learning in the late 1950s, through the advent of the "big data" era in the early 2000s, to its contemporary applications in AI, machine learning, and deep learning, along with the associated challenges. UNIT2- Python for Data Science Reference No : J/650/4952 Credit : 9 || TQT : 90 This unit offers learners an introductory approach to Python programming tailored for data science. It begins by assuming no prior coding knowledge or familiarity with Python and proceeds to elucidate Python's fundamentals, including its design philosophy, syntax, naming conventions, and coding standards. UNIT3- Creating and Interpreting Visualisations in Data Science Reference No : K/650/4953 Credit : 3 || TQT : 30 This unit initiates learners into the realm of fundamental charts and visualisations, teaching them the art of creating and comprehending these graphical representations. It commences by elucidating the significance of visualisations in data comprehension and discerns the characteristics distinguishing effective visualisations from subpar ones. UNIT4- Data and Descriptive Statistics in Data Science Reference No : L/650/4954 Credit : 6 || TQT : 60 The primary objective of this unit is to acquaint learners with the foundational concepts of descriptive statistics and essential methods crucial for data analysis and data science. UNIT5- Fundamentals of Data Analytics Reference No : M/650/4955 Credit : 3 || TQT : 30 This unit will enable learners to distinguish between the roles of a Data Analyst, Data Scientist, and Data Engineer. Additionally, learners can provide an overview of the data ecosystem, encompassing databases and data warehouses, and gain familiarity with prominent vendors and diverse tools within this data ecosystem. UNIT6- Data Analysis with Python Reference No : R/650/4956 Credit : 3 || TQT : 30 This unit initiates learners into the fundamentals of data analysis using Python. It acquaints them with essential concepts like Pandas Data Frames and Series and the techniques of merging and joining data. UNIT7- Data Analysis with Python Reference No : R/650/4956 Credit : 3 || TQT : 30 This unit initiates learners into the fundamentals of data analysis using Python. It acquaints them with essential concepts like Pandas Data Frames and Series and the techniques of merging and joining data. UNIT8- Machine Learning Methods and Models in Data Science Reference No : T/650/4957 Credit : 3 || TQT : 30 This unit explores the practical applications of various methods in addressing real-world problems. It provides a summary of the key features of these different methods and highlights the challenges associated with each of them. UNIT9- The Machine Learning Process Reference No : Y/650/4958 Credit : 3 || TQT : 30 This unit provides an introduction to the numerous steps and procedures integral to the construction and assessment of machine learning models. UNIT10- Linear Regression in Data Science Reference No : A/650/4959 Credit : 3 || TQT : 30 This unit offers a foundational understanding of simple linear regression models, a crucial concept for predicting the value of one continuous variable based on another. Learners will gain the capability to estimate the best-fit line by computing regression parameters and comprehend the accuracy associated with this line of best-fit. UNIT11- Logistic Regression in Data Science Reference No : H/650/4960 Credit : 3 || TQT : 30 This unit introduces logistic regression, emphasizing its role as a classification algorithm. It delves into the fundamentals of binary logistic regression, covering essential concepts such as the logistic function, Odds ratio, and the Logit function. UNIT12- Decision Trees in Data Science Reference No : J/650/4961 Credit : 3 || TQT : 30 This unit offers an introductory exploration of decision trees' fundamental theory and practical application. It elucidates the process of constructing basic classification trees employing the standard ID3 decision-tree construction algorithm, including the node-splitting criteria based on information theory principles such as Entropy and Information Gain. Additionally, learners will gain hands-on experience in building and assessing decision tree models using Python. UNIT13- K-means clustering in Data Science Reference No : K/650/4962 Credit : 3 || TQT : 30 This unit initiates learners into unsupervised machine learning, focusing on the k-means clustering algorithm. It aims to give learners an intuitive understanding of the k-means clustering method and equip them with the skills to determine the optimal number of clusters. UNIT14- Synthetic Data for Privacy and Security in Data Science Reference No : L/650/4963 Credit : 6 || TQT : 60 This unit is designed to introduce learners to the emerging field of data science, specifically focusing on synthetic data and its applications in enhancing data privacy and security. UNIT15- Graphs and Graph Data Science Reference No : M/650/4964 Credit : 6 || TQT : 60 This unit offers a beginner-friendly introduction to graph theory, a foundational concept that underlies modern graph databases and graph analytics. Delivery Methods School of Business & Technology London provides various flexible delivery methods to its learners, including online learning and blended learning. Thus, learners can choose the mode of study as per their choice and convenience. The program is self-paced and accomplished through our cutting-edge Learning Management System. Learners can interact with tutors by messaging through the SBTL Support Desk Portal System to discuss the course materials, get guidance and assistance and request assessment feedbacks on assignments. We at SBTL offer outstanding support and infrastructure for both online and blended learning. We indeed pursue an innovative learning approach where traditional regular classroom-based learning is replaced by web-based learning and incredibly high support level. Learners enrolled at SBTL are allocated a dedicated tutor, whether online or blended learning, who provide learners with comprehensive guidance and support from start to finish. The significant difference between blended learning and online learning methods at SBTL is the Block Delivery of Online Live Sessions. Learners enrolled at SBTL on blended learning are offered a block delivery of online live sessions, which can be booked in advance on their convenience at additional cost. These live sessions are relevant to the learners' program of study and aim to enhance the student's comprehension of research, methodology and other essential study skills. We try to make these live sessions as communicating as possible by providing interactive activities and presentations. Resources and Support School of Business & Technology London is dedicated to offering excellent support on every step of your learning journey. School of Business & Technology London occupies a centralised tutor support desk portal. Our support team liaises with both tutors and learners to provide guidance, assessment feedback, and any other study support adequately and promptly. Once a learner raises a support request through the support desk portal (Be it for guidance, assessment feedback or any additional assistance), one of the support team members assign the relevant to request to an allocated tutor. As soon as the support receives a response from the allocated tutor, it will be made available to the learner in the portal. The support desk system is in place to assist the learners adequately and streamline all the support processes efficiently. Quality learning materials made by industry experts is a significant competitive edge of the School of Business & Technology London. Quality learning materials comprised of structured lecture notes, study guides, practical applications which includes real-world examples, and case studies that will enable you to apply your knowledge. Learning materials are provided in one of the three formats, such as PDF, PowerPoint, or Interactive Text Content on the learning portal. How does the Online Learning work at SBTL? We at SBTL follow a unique approach which differentiates us from other institutions. Indeed, we have taken distance education to a new phase where the support level is incredibly high.Now a days, convenience, flexibility and user-friendliness outweigh demands. Today, the transition from traditional classroom-based learning to online platforms is a significant result of these specifications. In this context, a crucial role played by online learning by leveraging the opportunities for convenience and easier access. It benefits the people who want to enhance their career, life and education in parallel streams. SBTL's simplified online learning facilitates an individual to progress towards the accomplishment of higher career growth without stress and dilemmas. How will you study online? With the School of Business & Technology London, you can study wherever you are. You finish your program with the utmost flexibility. You will be provided with comprehensive tutor support online through SBTL Support Desk portal. How will I get tutor support online? School of Business & Technology London occupies a centralised tutor support desk portal, through which our support team liaise with both tutors and learners to provide guidance, assessment feedback, and any other study support adequately and promptly. Once a learner raises a support request through the support desk portal (Be it for guidance, assessment feedback or any additional assistance), one of the support team members assign the relevant to request to an allocated tutor. As soon as the support receive a response from the allocated tutor, it will be made available to the learner in the portal. The support desk system is in place to assist the learners adequately and to streamline all the support process efficiently. Learners should expect to receive a response on queries like guidance and assistance within 1 - 2 working days. However, if the support request is for assessment feedback, learners will receive the reply with feedback as per the time frame outlined in the Assessment Feedback Policy.

Overview This comprehensive course on Advanced Mathematics will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Advanced Mathematics 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 Advanced Mathematics. It is available to all students, of all academic backgrounds. Requirements Our Advanced Mathematics 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 8 sections • 30 lectures • 03:23:00 total length •Introduction: 00:01:00 •Introduction to Mathematical Logic, What is Sentence, Statements and their Types: 00:02:00 •Introduction to Logical Connectivity, Tautology, Contradiction, Contingency, Pattern: 00:06:00 •Quantitative and Quantified Statement and types and example: 00:03:00 •Dual: Replacing of Connections and Symbols: 00:02:00 •Negations of Compound Statement, Converse, Inverse, & Contrapositive: 00:03:00 •Algebra of Statements and Law: 00:05:00 •Real Life application of Logic to Switching Electric Circuit: 00:05:00 •Introduction to Matrices, Multiplication and Addition using Matrix: 00:06:00 •Inverse of Matrix Uniqueness of Inverse, Elementary Transformation: 00:09:00 •Method of REDUCTION AND INVERSION with real life example how we can implement: 00:17:00 •Introduction to Trigonometric Function: 00:03:00 •General Solutions and Theorem: 00:10:00 •Solution of Triangle: Polar Co-ordinates: 00:21:00 •Rules and Theorems of Sin Cosine and Tan: 00:22:00 •Introduction & Combined Equations: 00:07:00 •Degrees and Types: 00:13:00 •Some Theorem: 00:17:00 •Introduction - vector Cartesian theorem: 00:02:00 •Cartesian Equation & 2 Point Theorem: 00:03:00 •Theorems & Problem Solving: 00:05:00 •Distance of Point Line: 00:05:00 •Skew Lines: 00:01:00 •Distance of skew lines: 00:03:00 •Distance between parallel lines: 00:02:00 •Equation of Plane and Cartesian Form: 00:10:00 •Linear Programming Introduction: 00:08:00 •Introduction to LPP (Linear Programming Problem): 00:05:00 •LPP Problem Solving: 00:07:00 •Assignment - Advanced Mathematics: 00:00:00

The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. 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 00:08:00 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 Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 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 00:07:00 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 - Python Machine Learning & Data Science Fundamentals 00:00:00

Dive deeper into the world of mathematics with our 'Advanced Mathematics' course. Explore complex concepts and problem-solving techniques that will challenge and expand your mathematical proficiency. Whether you're a student aiming for higher academic achievements or a professional seeking to strengthen your analytical skills, this course will equip you with the knowledge and tools to excel. Enroll now and unlock the next level of mathematical understanding and capability.