12 Linear Algebra courses

Get started with using linear algebra in your data science projects

Start your data science journey with this carefully constructed comprehensive course and get hands-on experience with Python for data science. Gain in-depth knowledge about core Python and essential mathematical concepts in linear algebra, probability, and statistics. Complete data science training with 13+ hours of content.

This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.

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 Getting Data Ready for Regression Model Transportation Problem in Excel using Goal Seek 00:12:00 Gathering Business Knowledge 00:03:00 Data Exploration 00:03:00 The Data and the Data Dictionary 00:07:00 Univariate analysis and EDD 00:03:00 Discriptive Data Analytics in Excel 00:10:00 Outlier Treatment 00:04:00 Identifying and Treating Outliers in Excel 00:04:00 Missing Value Imputation 00:03:00 Identifying and Treating missing values in Excel 00:04:00 Variable Transformation in Excel 00:03:00 Dummy variable creation: Handling qualitative data 00:04:00 Dummy Variable Creation in Excel 00:07:00 Correlation Analysis 00:09:00 Creating Correlation Matrix in Excel 00:08:00 Creating Regression Model The Problem Statement 00:01:00 Basic Equations and Ordinary Least Squares (OLS) method 00:08:00 Assessing accuracy of predicted coefficients 00:14:00 Assessing Model Accuracy: RSE and R squared 00:07:00 Creating Simple Linear Regression model 00:02:00 Multiple Linear Regression 00:05:00 The F - statistic 00:08:00 Interpreting results of Categorical variables 00:05:00 Creating Multiple Linear Regression model 00:07:00 What-if analysis Excel: Running Linear Regression using Solver 00:08:00 Assessment Assessment - Linear Regression Analysis In MS Excel 00:10:00 Certificate of Achievement Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00

Want to become an expert NLP engineer and a data scientist? Then this is the right course for you. In this course, we will be covering complex theory, algorithms, and coding libraries in a very simple way that can be easily grasped by any beginner as well.

You will learn Python-based deep learning and machine learning techniques through this course. With numerous real-world case studies, we will go over all the mathematics needed to master deep learning algorithms. We will study Backpropagation, Feed Forward Network, Artificial Neural Networks, CNN, RNN, Transfer Learning, and more.

OVERVIEW This comprehensive course on Algebra Fundamentals will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Algebra Fundamentals 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 Algebra Fundamentals. It is available to all students, of all academic backgrounds. REQUIREMENTS Our Algebra Fundamentals 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 17 sections • 83 lectures • 11:02:00 total length •Lecture 1 Introduction: 00:03:00 •Lecture 2 What is Algebra: 00:02:00 •Lecture 3 Simple Equations: 00:05:00 •Lecture 4 What are Polynomials: 00:04:00 •Lecture 5 Terms in Polynomials: 00:03:00 •Lecture 6 Degree of Polynomials: 00:05:00 •Lecture 7 Writing statements to algebraic form: 00:04:00 •Lecture 8 Integers and common mistakes in solving integers: 00:13:00 •Lecture 9 Arrangement of Terms: 00:07:00 •Lecture 10 Powers on integers: 00:04:00 •Lecture11 Simplification using BODMAS: 00:08:00 •Lecture 12 Distributive Properties in Polynomials: 00:04:00 •Lecture 13 Simplify Polynomials: 00:10:00 •Lecture 14 Additions of Polynomials: 00:06:00 •Lecture 15 Subtractions of Polynomials: 00:10:00 •Lecture 16 The rules of Indices in algebra: 00:11:00 •Lecture 17 Fractional indices: 00:10:00 •Lecture 18 Understanding indices (practice questions): 00:07:00 •Lecture 19 Problems from IGCSE Last year papers: 00:09:00 •Lecture 20 Multiplication of monomial to Polynomial: 00:09:00 •Lecture 21 Multiplication of Polynomial by Polynomial: 00:06:00 •Lecture 22 Division of algebraic expression by a monomial: 00:08:00 •Lecture 23 Division of algebraic expression by another polynomial: 00:09:00 •Lecture 24 Division of a polynomial by another polynomial with remainder: 00:11:00 •Lecture 25 Rules of brackets: 00:04:00 •Lecture 26 Simplification by removing brackets: 00:11:00 •Lecture 27 Simplification of algebraic fractions: 00:07:00 •Lecture 28 Rules to solve linear equations in one variable: 00:03:00 •Lecture 29 Solving linear equations in one variable: 00:07:00 •Lecture 30 Solving complex linear equations in one variable: 00:10:00 •Lecture 31 Word problems on linear equations in one variable: 00:13:00 •Lecture 32 What are Identities?: 00:05:00 •Lecture 33 Identity ( a + b ) ²: 00:13:00 •Lecture 35 Identity a² - b² = (a-b) (a +b ) new: 00:07:00 •Lecture 36 -- Standard Identities ( a + b + c ) ² = a ² + b ² + c ² + 2 a b + 2 a c +2 b c old: 00:07:00 •Lecture 37 Identity (x + a) (x + b) Identity Derivation & Application new: 00:08:00 •Lecture 38 Pascal's Triangle _ Identity ( a + b ) ³ new: 00:07:00 •Lecture 39 Identities( a - b ) ³, ( a ³ + b ³) and (a ³ - b ³) new: 00:13:00 •Lecture 40 - Standard Identities a ³ + b ³ + c ³ - 3 a b c: 00:10:00 •Lecture 41 -Changing the subject of formula: 00:08:00 •Lecture 42 - Linear Inequalities: 00:12:00 •Lecture 43 - Factorization by taking out common factor: 00:10:00 •Lecture 44 - Factorization by grouping the terms: 00:09:00 •Lecture 45 - factorize using identity a ² - b ²: 00:07:00 •Lecture 46 - factorize using identity (a + b )² and (a - b )² (2): 00:08:00 •Lecture 47 - factorize using identity ( a + b + c ) ²: 00:05:00 •Lecture 48 - factorization by middle term split: 00:12:00 •Lecture 49 -Simplification of algebraic fractions: 00:06:00 •Lecture 50 All that you need to know about co ordinate axis: 00:04:00 •Lecture 51 Some important facts needed to draw line graph: 00:03:00 •Lecture 52 - How to draw a line graph on coordinate plane: 00:03:00 •Lecture 53 Drawing line graphs: 00:06:00 •Lecture 54 Simultaneous Linear Equations in two variables- intro: 00:03:00 •Lecture 55 Graphical method of solving linear equations: 00:06:00 •Lecture 56 Graphical method - more problems: 00:10:00 •Lecture 57 Method of Elimination by substitution: 00:09:00 •Lecture 58 Method of Elimination by Equating coefficients: 00:11:00 •Lecture 59 Method of Elimination by cross multiplication: 00:07:00 •Lecture 60 Equations reducible to simultaneous linear equations: 00:12:00 •Lecture 61 Word Problems on Linear equations: 00:18:00 •Lecture 62 Polynomials and Zeros of polynomials: 00:10:00 •Lecture 63 Remainder Theorem: 00:04:00 •Lecture 64 Factor Theorem: 00:08:00 •Lecture 65 Practice problems on Remainder and Factor Theorem: 00:09:00 •Lecture 66 Factorization using factor Theorem: 00:10:00 •Lecture 67 Zeros of polynomials α, β & γ: 00:10:00 •Lecture 68 Relation between zeros and coefficients of a polynomials: 00:13:00 •Lecture 69 Finding polynomials if zeros are known: 00:06:00 •Lecture 70 Practice problems on zeros of polynomials: 00:10:00 •Lecture 71Problems solving with α and β (part 1): 00:11:00 •Lecture 72 Problems solving with α and β (part 2): 00:10:00 •Lecture73 what are Quadratic equations: 00:03:00 •Lecture 74 Solutions by factorization method: 00:12:00 •Lecture 75 Solutions by completing square formula: 00:06:00 •Lecture 76 Deriving Quadratic formula: 00:05:00 •Lecture 77 Practice problems by Quadratic formula: 00:07:00 •Lecture 78 Solving complex quadratic equations by Quadratic Formula: 00:11:00 •Lecture 79 Solutions of reducible to Quadratic Formula: 00:09:00 •Lecture 80 Skilled problems on Quadratic Equations: 00:07:00 •Lecture 81 Exponential problems reducible to Quadratic Equations: 00:06:00 •Lecture 82 Nature of Roots of Quadratic Equations: 00:09:00 •Lecture 83 Word problems on quadratic Equations Part 1: 00:13:00 •Lecture 84 Word problems on quadratic Equations Part 2: 00:11:00

ABOUT THE COURSE “Quantum Computing for Finance” is an emerging multidisciplinary field of quantum physics, finance, mathematics, and computer science, in which quantum computations are applied to solve complex problems. “Introduction to Quantitative and Computational Finance” provides a basis to step into the world of Quantum Computing for Finance. This introductory course will develop fundamental concepts required for an understanding of quantum algorithms and more advanced topics in computational finance. Through this course, you will learn the basics of derivative products, the Black-Scholes-Merton model for pricing vanilla derivatives, and how to compute the price of exotic options with a computer.  This course is designed for all those who wish to develop their skills and start a career in quantitative finance. This course is the first part of the specialised training program: “Quantum Computing for Finance”. WHAT SKILLS YOU WILL LEARN * The fundamentals of derivative products, their types – forwards and options, and their pricing. * An understanding of the Black-Scholes-Merton model, hedging and volatility modelling. * The computational and modelling techniques for pricing options such as Monte-Carlo simulations and the Finite Difference method. * A strong foundation in quantitative and computational skills for modelling and solving complex financial problems using Python programming language. * The skills for a career in the finance industry, including quantitative asset management and trading, financial engineering, risk management, and applied research. COURSE PREREQUISITES All potential learners should have prior knowledge of the following content areas, either through completion of academic studies or relative professional preparation: * Basic calculus (partial derivatives) * Probability theory (with an exposure to measure theory if possible) * Basic linear algebra (matrix operations) * Numerical Python (NumPy essentially) The course contains several Python based programming exercises. We recommend that you install Python on your local system to practice and implement the programs explained throughout the course. For instructions and tutorials for beginners, please click on the following link: Python installation instructions and tutorials for beginners [https://platform.qureca.com/wp-content/uploads/2020/09/python-prep.pdf] DURATION The estimated duration to complete this course is approximately 4 weeks (~3hrs/week). COURSE ASSESSMENT To complete the course and earn certification, you must pass all the quizzes at the end of each lesson by scoring 80% or more. INSTRUCTORS [https://platform.qureca.com/courses/introduction-to-quantitative-and-computational-finance/#course-section__instructors] https://platform.qureca.com/author/kevincallaghan/ QuantFi [https://platform.qureca.com/author/kevincallaghan/]QuantFi is a French start-up research firm formed in 2019 with the objective of using the science of quantum computing to provide solutions to the financial services industry. With its staff of PhD's and PhD students, QuantFi engages in fundamental and applied research in in the field of quantum finance, collaborating with industrial partners and universities in seeking breakthroughs in such areas as portfolio optimisation, asset pricing, and trend detection.

Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. SOLVING BUSINESS PROBLEMS USING AI AND ML * Topic A: Identify AI and ML Solutions for Business Problems * Topic B: Formulate a Machine Learning Problem * Topic C: Select Approaches to Machine Learning PREPARING DATA * Topic A: Collect Data * Topic B: Transform Data * Topic C: Engineer Features * Topic D: Work with Unstructured Data TRAINING, EVALUATING, AND TUNING A MACHINE LEARNING MODEL * Topic A: Train a Machine Learning Model * Topic B: Evaluate and Tune a Machine Learning Model BUILDING LINEAR REGRESSION MODELS * Topic A: Build Regression Models Using Linear Algebra * Topic B: Build Regularized Linear Regression Models * Topic C: Build Iterative Linear Regression Models BUILDING FORECASTING MODELS * Topic A: Build Univariate Time Series Models * Topic B: Build Multivariate Time Series Models BUILDING CLASSIFICATION MODELS USING LOGISTIC REGRESSION AND K-NEAREST NEIGHBOR * Topic A: Train Binary Classification Models Using Logistic Regression * Topic B: Train Binary Classification Models Using k-Nearest Neighbor * Topic C: Train Multi-Class Classification Models * Topic D: Evaluate Classification Models * Topic E: Tune Classification Models BUILDING CLUSTERING MODELS * Topic A: Build k-Means Clustering Models * Topic B: Build Hierarchical Clustering Models BUILDING DECISION TREES AND RANDOM FORESTS * Topic A: Build Decision Tree Models * Topic B: Build Random Forest Models BUILDING SUPPORT-VECTOR MACHINES * Topic A: Build SVM Models for Classification * Topic B: Build SVM Models for Regression BUILDING ARTIFICIAL NEURAL NETWORKS * Topic A: Build Multi-Layer Perceptrons (MLP) * Topic B: Build Convolutional Neural Networks (CNN) * Topic C: Build Recurrent Neural Networks (RNN) OPERATIONALIZING MACHINE LEARNING MODELS * Topic A: Deploy Machine Learning Models * Topic B: Automate the Machine Learning Process with MLOps * Topic C: Integrate Models into Machine Learning Systems MAINTAINING MACHINE LEARNING OPERATIONS * Topic A: Secure Machine Learning Pipelines * Topic B: Maintain Models in Production

Explore the world of Artificial Intelligence with our comprehensive Foundations Course. From understanding the basics of AI and essential mathematical principles to delving into advanced topics like Deep Learning, Natural Language Processing, and Robotics – this course equips you with the knowledge and skills needed to navigate the dynamic landscape of AI. Whether you're a student, professional, or enthusiast, join us on a journey to build a solid foundation in AI and develop practical applications that shape the future. Enroll now and empower yourself to contribute to the exciting field of Artificial Intelligence.