1 Linear Algebra courses in Glasgow

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.

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.

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 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 QuantFiQuantFi 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.

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.

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

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

This course equips learners with a comprehensive understanding of the NumPy stack, including NumPy, Matplotlib, Pandas, and SciPy, to effectively tackle common challenges in deep learning and data science. Master the basics with this carefully structured course.