Eigenvalues And EigenvectorsPrincipal Component Analysis (PCA)Multivariable CalculusLinear AlgebraBasis (Linear Algebra)Transformation MatrixLinear RegressionVector CalculusGradient DescentDimensionality ReductionPython Programming
Spécialisation Mathématiques pour l'apprentissage automatique
Mathématiques pour l'apprentissage automatique. Learn about the prerequisite mathematics for applications in data science and machine learning
Offert par
Spécialisation Mathématiques pour l'apprentissage automatiqueImperial College LondonCe que vous allez apprendre
Implement mathematical concepts using real-world data
Derive PCA from a projection perspective
Understand how orthogonal projections work
Master PCA
Compétences que vous acquerrez
À propos de ce Spécialisation
117,683 consultations récentes
For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.
In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.
The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.
The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.
At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.
Projet d'apprentissage appliqué
Through the assignments of this specialisation you will use the skills you have learned to produce mini-projects with Python on interactive notebooks, an easy to learn tool which will help you apply the knowledge to real world problems. For example, using linear algebra in order to calculate the page rank of a small simulated internet, applying multivariate calculus in order to train your own neural network, performing a non-linear least squares regression to fit a model to a data set, and using principal component analysis to determine the features of the MNIST digits data set.
Résultats de carrière des étudiants
47 %
ont commencé une nouvelle carrière après avoir terminé ce spécialisation.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
Cours en ligne à 100 %
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Planning flexible
Définissez et respectez des dates limites flexibles.
Niveau débutant
Aucune connaissance prérequise.
Approx. 4 mois pour terminer
4 heures/semaine recommandées
Anglais
Sous-titres : Anglais, Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Espagnol, Grec
Résultats de carrière des étudiants
47 %
ont commencé une nouvelle carrière après avoir terminé ce spécialisation.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
Cours en ligne à 100 %
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Planning flexible
Définissez et respectez des dates limites flexibles.
Niveau débutant
Aucune connaissance prérequise.
Approx. 4 mois pour terminer
4 heures/semaine recommandées
Anglais
Sous-titres : Anglais, Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Espagnol, Grec
Cette Spécialisation compte 3 cours
Mathematics for Machine Learning: Linear Algebra
8,863 évaluations
In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
Mathematics for Machine Learning: Multivariate Calculus
4,258 évaluations
This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.
Mathematics for Machine Learning: PCA
2,276 évaluations
This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.
Offert par
Imperial College London
Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges.
Foire Aux Questions
Puis-je obtenir des crédits universitaires si je réussis la Spécialisation ?
Can I just enroll in a single course?
Puis-je m'inscrire à un seul cours ?
Can I take the course for free?
Puis-je suivre le cours gratuitement ?
Ce cours est-il vraiment accessible en ligne à 100 % ? Dois-je assister à certaines activités en personne ?
Quelle est la durée nécessaire pour terminer la Spécialisation ?
Do I need to take the courses in a specific order?
Will I earn university credit for completing the Specialization?
Puis-je obtenir des crédits universitaires si je réussis la Spécialisation ?
D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.
