DeepLearning.AI
Mathematics for Machine Learning and Data Science Specialization
DeepLearning.AI

Mathematics for Machine Learning and Data Science Specialization

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.

Taught in English

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Luis Serrano

Instructor: Luis Serrano

73,583 already enrolled

Specialization - 3 course series

Get in-depth knowledge of a subject

4.6

(1,925 reviews)

Intermediate level

Recommended experience

3 months at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • 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.

Details to know

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Specialization - 3 course series

Get in-depth knowledge of a subject

4.6

(1,925 reviews)

Intermediate level

Recommended experience

3 months at 5 hours a week
Flexible schedule
Learn at your own pace

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Specialization - 3 course series

Linear Algebra for Machine Learning and Data Science

Course 134 hours4.5 (1,444 ratings)

What you'll learn

  • 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 transformation, and apply concepts of eigenvalues and eigenvectors to machine learning problems

Skills you'll gain

Category: Eigenvalues And Eigenvectors
Category: Linear Equation
Category: Determinants
Category: Machine Learning
Category: Linear Algebra

Calculus for Machine Learning and Data Science

Course 226 hours4.8 (642 ratings)

What you'll learn

  • 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

  • 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

Skills you'll gain

Category: Calculus
Category: Machine Learning
Category: Newton'S Method
Category: Gradient Descent
Category: Mathematical Optimization

What you'll learn

  • Describe and quantify the uncertainty inherent in predictions made by machine learning models

  • Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science

  • 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

Skills you'll gain

Category: Probability And Statistics
Category: Machine Learning (ML) Algorithms
Category: Statistical Analysis
Category: Probability
Category: Statistical Hypothesis Testing

Instructor

Luis Serrano
DeepLearning.AI
4 Courses130,384 learners

Offered by

DeepLearning.AI

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