Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.

Luis Serrano +3 enseignants de plus

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Spécialisation Mathematics for Machine Learning and Data ScienceDeepLearning.AI

Ce que vous allez apprendre

A deep understanding of the math that makes machine learning algorithms work.
Statistical techniques that empower you to get more out of your data analysis.
Fundamental skills that employers desire, helping you ace machine learning interview questions and land your dream job.
Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence

Compétences que vous acquerrez

À propos de ce Spécialisation

170 136 consultations récentes

Mathematics for Machine Learning and Data science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning. Many machine learning engineers and data scientists struggle with mathematics. Challenging interview questions often hold people back from leveling up in their careers, and even experienced practitioners can feel held by a lack of math skills. This specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works. Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques — plus the know-how to incorporate them into your machine learning career.

Projet d'apprentissage appliqué

By the end of this Specialization, you will be ready to:

Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence
Apply common vector and matrix algebra operations like dot product, inverse, and determinants
Express certain types of matrix operations as linear transformations
Apply concepts of eigenvalues and eigenvectors to machine learning problems
Optimize different types of functions commonly used in machine learning
Perform gradient descent in neural networks with different activation and cost functions
Describe and quantify the uncertainty inherent in predictions made by machine learning models
Understand the properties of commonly used probability distributions in machine learning and data science
Apply common statistical methods like MLE and MAP
Assess the performance of machine learning models using interval estimates and margin of errors
Apply concepts of statistical hypothesis testing

Certificat partageable

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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

Approximativement 3 mois pour terminer

Rythme recommandé de 5 heures/semaine

Anglais

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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

Approximativement 3 mois pour terminer

Rythme recommandé de 5 heures/semaine

Anglais

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Comment fonctionne la Spécialisation

Suivez les cours

Une Spécialisation Coursera est une série de cours axés sur la maîtrise d'une compétence. Pour commencer, inscrivez-vous directement à la Spécialisation ou passez en revue ses cours et choisissez celui par lequel vous souhaitez commencer. Lorsque vous vous abonnez à un cours faisant partie d'une Spécialisation, vous êtes automatiquement abonné(e) à la Spécialisation complète. Il est possible de terminer seulement un cours : vous pouvez suspendre votre formation ou résilier votre abonnement à tout moment. Rendez-vous sur votre tableau de bord d'étudiant pour suivre vos inscriptions aux cours et vos progrès.

Projet pratique

Chaque Spécialisation inclut un projet pratique. Vous devez réussir le(s) projet(s) pour terminer la Spécialisation et obtenir votre Certificat. Si la Spécialisation inclut un cours dédié au projet pratique, vous devrez terminer tous les autres cours avant de pouvoir le commencer.

Obtenir un Certificat

Lorsque vous aurez terminé tous les cours et le projet pratique, vous obtiendrez un Certificat que vous pourrez partager avec des employeurs éventuels et votre réseau professionnel.

Ce Spécialisation compte 3 cours

Cours1

Cours 1

Linear Algebra for Machine Learning and Data Science

After completing this course, learners will be able to:

• Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc. • Apply common vector and matrix algebra operations like dot product, inverse, and determinants • Express certain types of matrix operations as linear transformations • Apply concepts of eigenvalues and eigenvectors to machine learning problems Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning. Many machine learning engineers and data scientists struggle with mathematics. Challenging interview questions often hold people back from leveling up in their careers, and even experienced practitioners can feel held by a lack of math skills. This specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works. Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques — plus the know-how to incorporate them into your machine learning career.

Cours2

Cours 2

Calculus for Machine Learning and Data Science

After completing this course, learners will be able to:

• Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients • Approximately optimize different types of functions commonly used in machine learning using first-order (gradient descent) and second-order (Newton’s method) iterative methods • Visually interpret differentiation of different types of functions commonly used in machine learning • Perform gradient descent in neural networks with different activation and cost functions Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning. Many machine learning engineers and data scientists struggle with mathematics. Challenging interview questions often hold people back from leveling up in their careers, and even experienced practitioners can feel held by a lack of math skills. This specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works. Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques — plus the know-how to incorporate them into your machine learning career.

Cours3

Cours 3

Probability & Statistics for Machine Learning & Data Science

After completing this course, learners will be able to:

• Describe and quantify the uncertainty inherent in predictions made by machine learning models, using the concepts of probability, random variables, and probability distributions. • Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science like Bernoulli, Binomial, and Gaussian distributions • Apply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problems • Assess the performance of machine learning models using interval estimates and margin of errors • Apply concepts of statistical hypothesis testing to commonly used tests in data science like AB testing Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning. Many machine learning engineers and data scientists struggle with mathematics. Challenging interview questions often hold people back from leveling up in their careers, and even experienced practitioners can feel held by a lack of math skills. This specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works. Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques — plus the know-how to incorporate them into your machine learning career.

Enseignants

Offert par

DeepLearning.AI

Foire Aux Questions

What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Can I just enroll in a single course?
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Can I take the course for free?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
Is this course really 100% online? Do I need to attend any classes in person?
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
I’m not good at math, is this course still for me?
Yes! We want to break down the barriers that hold people back from advancing their math skills. In this course, we flip the traditional mathematics pedagogy on its head, starting with the real world use-cases and working back to theory.
Most people who are good at math simply have more practice doing math, and through that, more comfort with the mindset needed to be successful. This course is the perfect place to start or advance those fundamental skills, and build the mindset required to be good at math.
What areas of mathematics will I learn in this course?
Linear algebra (matrices, vectors, and their applications)
Calculus (particularly function optimization using differential calculus)
Probability (common probability distributions like Binomial, Gaussian)
Statistics (parameter estimation, hypothesis testing)
Why is mathematics for machine learning and data science important?
With out-of-the-box tools, it’s easier than ever to begin a career as a machine learning engineer or data scientist. But to advance deeper in your career, create efficient models, troubleshoot algorithms, and incorporate creative thinking, a deeper understanding of the mathematics behind the models is needed.
How long does it take to complete the Specialization?
This specialization consists of three courses. At the rate of 5 hours per week, it will take you around 4 weeks to complete Course 1, 4 weeks to complete Course 2, and 4 weeks to complete Course 3 of the Mathematics for Machine Learning and Data Science Specialization.
What background knowledge is necessary?
This course is designed to be beginner-friendly, though familiarity with functions, basic algebra, and Python programming will help learners get the most out of this specialization.
Do I need to take the courses in a specific order?
No! Most learners would benefit from taking courses one and two together, as they introduce concepts that build upon each other, but course three is independent from the other courses in this specialization.
Will I earn university credit for completing the Specialization?
No, this Specialization is not for college credit.
What will I be able to do after completing Mathematics for Machine Learning and Data Science?
With Mathematics for Machine Learning and Data Science, you will have a foundation of knowledge that will equip you to go deeper in your machine learning and data science career.
Is this a standalone course or a specialization?
The Mathematics for Machine Learning and Data Science Specialization is made up of three courses.
Do I need to take the courses in a specific order?
No! Most learners would benefit from taking courses one and two together, as they introduce concepts that build upon each other, but course three is independent from the other courses in this specialization.
Can I apply for financial aid?
Yes, Coursera provides financial aid to learners who cannot afford the fee.
How much does the specialization cost?
A Coursera subscription costs $49 / month.
Can I apply for financial aid?
Yes, Coursera provides financial aid to learners who cannot afford the fee.
Can I audit the specialization?
You can audit the courses in the Mathematics for Machine Learning and Data Science for free. Note that you will not receive a certificate at the end of the course if you choose to audit it for free instead of purchasing it.
Will I receive a certificate at the end of the specialization?
You will receive a certificate at the end of each course if you pay for the courses and complete the programming assignments. There is a limit of 180 days of certificate eligibility, after which you must re-purchase the course to obtain a certificate. If you audit the course for free, you will not receive a certificate.
If you complete all 4 courses in the Specialization, you will also receive an additional certificate showing that you completed the entire Specialization.
I want to purchase this specialization for my employees. How can I do that?
Visit coursera.org/business for more information, to pick up a plan, and to contact Coursera. For each plan, you decide the number of courses every member can enroll in and the collection of courses they can choose from.
How do I get a receipt to get this reimbursed by my employer?
Go to your Coursera account.
Click on My Purchases and find the relevant course or Specialization.
Click Email Receipt and wait up to 24 hours to receive the receipt.
You can read more about it here.

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Spécialisation Mathematics for Machine Learning and Data Science

Ce que vous allez apprendre

Compétences que vous acquerrez

À propos de ce Spécialisation

Projet d'apprentissage appliqué