Mathematics for Machine Learning and Data Science
DeepLearning.AI via Coursera Specialization
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Overview
Class Central Tips
Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly Specialization is where you’ll master the fundamental mathematics toolkit of machine learning.
Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back 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.
We recommend you have a high school level of mathematics (functions, basic algebra) and familiarity with a programming language (loops, functions, if/else statements, lists/dictionaries, importing libraries). Assignments and labs are written in Python but the course introduces all the machine learning libraries you’ll use.
Syllabus
Course 1: Linear Algebra for Machine Learning and Data Science
- Offered by DeepLearning.AI. Newly updated for 2024! After completing this course, learners will be able to: • Represent data as vectors ... Enroll for free.
Course 2: Calculus for Machine Learning and Data Science
- Offered by DeepLearning.AI. After completing this course, learners will be able to: • Analytically optimize different types of functions ... Enroll for free.
Course 3: Probability & Statistics for Machine Learning & Data Science
- Offered by DeepLearning.AI. Mathematics for Machine Learning and Data science is a foundational online program created by DeepLearning.AI ... Enroll for free.
- Offered by DeepLearning.AI. Newly updated for 2024! After completing this course, learners will be able to: • Represent data as vectors ... Enroll for free.
Course 2: Calculus for Machine Learning and Data Science
- Offered by DeepLearning.AI. After completing this course, learners will be able to: • Analytically optimize different types of functions ... Enroll for free.
Course 3: Probability & Statistics for Machine Learning & Data Science
- Offered by DeepLearning.AI. Mathematics for Machine Learning and Data science is a foundational online program created by DeepLearning.AI ... Enroll for free.
Courses
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Newly updated for 2024! 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 need help with mathematics, and even experienced practitioners can feel held back 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. This is a beginner-friendly program, with a recommended background of at least high school mathematics (functions, basic algebra). We also recommend a basic familiarity with Python (loops, functions, if/else statements, lists/dictionaries, importing libraries), as labs use Python and Jupyter Notebooks to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science. If you are already familiar with the concepts of linear algebra, Course 1 will provide a good review, or you can choose to take Course 2: Calculus for Machine Learning and Data Science and Course 3: Probability and Statistics for Machine Learning and Data Science, of this specialization.
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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 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 need help with mathematics, and even experienced practitioners can feel held back 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. This is a beginner-friendly program, with a recommended background of at least high school mathematics (functions, basic algebra). We also recommend a basic familiarity with Python (loops, functions, if/else statements, lists/dictionaries, importing libraries) , as labs use Python and Jupyter Notebooks to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science.
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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. 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 • Perform Exploratory Data Analysis on a dataset to find, validate, and quantify patterns. 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.
Taught by
Anshuman Singh, Elena Sanina, Luis Serrano and Magdalena Bouza