Learn Mathematics with free online courses and MOOCs from HKUST, Doon University, Dehradun, Galileo University, SFI and other top universities around the world. Read reviews to decide if a class is right for you.
La Geometría Diferencial es una rama de las Matemáticas que estudia la noción de variedad. Ésta incluye, entre otros, a las curvas, las superficies y a los espacios de configuración de sistemas mecánicos. La conexión de la Geometría Diferencial con la Física es explícita desde sus orígenes, siendo a veces motivadora del desarrollo de algunas teorías geométricas o permitiendo explicar algunos fenómenos físicos. Este curso pretende despertar la curiosidad entorno a esta parte de las Matemáticas. El curso está estructurado en 5 módulos (además de un módulo 0 motivador y con algunos referentes históricos) en los que se hace una introducción a los conceptos y resultados básicos de esta teoría para finalmente usarlos en la descripción geométrica de las ecuaciones de movimiento de un sistema mecánico. Se presentan numerosos ejemplos para ilustrar la teoría y se aportan textos teóricos en donde muchos de los resultados descritos son demostrados con detalle.
Premier volet d’une série de cours consacrés aux équations d’évolution, ce cours introduit l’auditeur à la théorie des équations différentielles. Les équations différentielles ordinaires sont d’usage universel aujourd’hui, dans toutes les sciences et dans l’industrie. Ce cours permettra de se familiariser avec leur langage et leur théorie, et de maîtriser les principales techniques d’étude qualitative (comportement en temps grand, stabilité, approximation...). Il s’agit aussi d’une préparation à un cours d’équations aux dérivées partielles, qui comptent parmi les outils mathématiques les plus importants et multiformes du monde contemporain.Le cours commence par une mise en perspective des équations d’évolution, puis se concentre sur les équations différentielles ordinaires. On y abordera les thèmes suivants : étude locale, existence et unicité, étude qualitative du comportement à long terme, introduction à la modélisation.Les participants disposeront ainsi du bagage permettant d’étudier et d’utiliser les modèles d’EDO.
Discrete Mathematics is the study of discrete objects as opposed to continuous objected like real numbers. The discrete objects like proofs, sets, graphs, colorings, algebraic structures, algorithms etc. These object arise naturally and frequently in many areas of mathematics and computer science and are fundamental in an undergraduate curriculum of computer science and mathematics. In this course, we will focus on Logic, Set Theory, Number Theory, Algebraic Structures, Combinatorics and Graph Theory. INTENDED AUDIENCE: UG students interested in computer science, combinatorics, etc. PRE-REQUISITES: NIL
Se prestará especial atención al correcto uso de la leguna jurídica, a la percepción del derecho como experiencia de vida y al análisis de los argumentos presentes en las soluciones ofrecidas por el derecho a los casos.
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What does every hiring manager look for in an employee? Set yourself apart from the pack by getting the essential soft skills--abilities that help people interact effectively with others--that employers value most, from communication fundamentals and being a team player to advancing your critical thinking skills.Build a solid process for achieving your goals and bouncing back from challenging situations.Learn to communicate, listen, and work well with team members and peers.Think critically as a problem solver.
Master the basics of pre-algebra: working with integers, expressions, and concepts such as the order of operations and distribution.Pre-algebra is the first step in high school math, forming the building blocks that lead to geometry, trigonometry, and calculus. This course will help you master the basics: from addition, subtraction, multiplication, and division to new types of numbers (integers and negative numbers) and concepts such as the order of operations and distribution.This course includes practice challenges and worksheets, as well as tips for educators who are helping students master pre-algebra for Common Core. Karin Hutchinson also helps you evaluate, write, and simplify expressions, and solve word problems and complex algebraic expressions.
Introducimos la teoría combinatoria: variaciones, permutaciones, combinaciones, números binomiales…, aprendiendo los contenidos básicos de conteo que podemos aplicar en áreas donde se requiere contar con elementos de conjuntos finitos, como Matemática Discreta y Probabilidad.
Este es un curso básico de matemáticas con énfasis en las operaciones de números reales y complejos. Esta dirigido a estudiantes de secundaria (bachillerato) de los tres últimos grados y a estudiantes universitarios de primer año. A los primeros les permitirá estudiar los contenidos básicos de los números reales y complejos y a los segundos repasar y profundizar en la aplicación de los conocimientos adquiridos en bachillerato, también a estos últimos les ayudará a superar los cursos universitarios de Fundamento de matemática que son los de mayor mortalidad en primer año de Universidad.
Learn to understand your data using basics of statistics, such as defining the middle, mean, and median of your data set; measuring the standard deviation; and finding outliers.
The course entitled “Differential Calculus” deals with the basic aspects differential calculus. The contents of this course are inevitable for many branches of sciences. The students of Mathematics, Physics, Chemistry, Computer Science, Statistics, etc., are equally benefited with this course as a stepping stone to the broad areas of Calculus.The objective of this course is to familiarize students with important concepts coming under the branch “Differential Calculus” and to develop strong foundations on these concepts. Upon successful completion of this course, the students are expected to:1. Familiarize with the concept of Limit and Continuity (Both informal and ε- δ definition)2. Learn through examples Successive differentiation and Leibniz’s theorem.3. Study Rolle’s theorem, Mean Value theorems through examples.4. Study the concepts tangents and normals.5. Familiarize with methods of finding Curvature, Asymptotes and Singular points.6. Learn methods of Tracing of curves in Cartesian, Parametric and Polar forms7. A study on sequences and series8. A detailed study on Taylor’s theorem, Taylor’s series and Maclaurin’s series through examples.9. Familiarise with functions of several variable.10. Study Euler’s theorem on homogeneous functions and some of its mathematical applications.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThis is a hands-on, guided project on deploying deep learning models using TensorFlow Serving with Docker. In this 1.5 hour long project, you will train and export TensorFlow models for text classification, learn how to deploy models with TF Serving and Docker in 90 seconds, and build simple gRPC and REST-based clients in Python for model inference.With the worldwide adoption of machine learning and AI by organizations, it is becoming increasingly important for data scientists and machine learning engineers to know how to deploy models to production. While DevOps groups are fantastic at scaling applications, they are not the experts in ML ecosystems such as TensorFlow and PyTorch. This guided project gives learners a solid, real-world foundation of pushing your TensorFlow models from development to production in no time!Prerequisites:In order to successfully complete this project, you should be familiar with Python, and have prior experience with building models with Keras or TensorFlow.Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
En este curso mostramos el proceso de modelización en dos familias de problemas (modelos discretos y continuos) usando técnicas de primeros cursos de Grados en Ciencias e Ingeniería. Los ejemplos del curso se apoyan en el uso de la herramienta informática Easy Java Simulations para la creación de simulaciones interactivas por computador que permite experimentar con los modelos presentados.
Discover how to install TensorFlow and use it to create, train, and deploy a machine learning model.TensorFlow is one of the most popular deep learning frameworks available. It's used for everything from cutting-edge machine learning research to building new features for the hottest start-ups in Silicon Valley. In this course, learn how to install TensorFlow and use it to build a simple deep learning model. After he shows how to get TensorFlow up and running, instructor Adam Geitgey demonstrates how to create and train a machine learning model, as well as how to leverage visualization tools to analyze and improve your model. Finally, he explains how to deploy models locally or in the cloud. When you wrap up this course, you'll be ready to start building and deploying your own models with TensorFlow.IntroductionWelcomeWhat you should knowUsing the exercise files1. Setting Up TensorFlowInstall TensorFlow on macOSInstall TensorFlow on Windows2. TensorFlow OverviewWhat is TensorFlow?Why is it called TensorFlow?Hardware, software, and language requirementsThe train/test/evaluation flow in TensorFlowBuild a simple model in TensorFlow3. Creating a TensorFlow ModelOptions for loading dataLoad the data setDefine the model structureSet up the model training loop4. Training a Model in TensorFlowTrainLogSave and load trained models5. TensorBoardVisualize the computational graphVisualize training runsAdd custom visualizations to TensorBoard6. Using a Trained TensorFlowExport models for use in productionConfigure a new Google Cloud accountHost your model in the cloud with Google CloudUse a model in the cloudConclusionNext steps
Understand topics at a deeper level by learning about statistics. Master the terms, formulas, and techniques needed to perform the most common types of statistics. Explore probability and ways to calculate future outcomes and understand past events.Explore basic statistics.Identify how to interpret charts and graphs.Determine the likelihood of events using probability.