# Mathematics for Machine Learning and Data Science

DeepLearning.AI

Mathematics for Machine Learning and Data Science, offered by DeepLearning.AI, is a beginner-friendly Specialization designed to provide a deep understanding of the mathematical foundations of machine learning. This comprehensive program covers linear algebra, calculus, probability, and statistics, offering essential knowledge for aspiring machine learning engineers and data scientists.

Throughout the course, learners will delve into the core concepts of linear algebra, enabling them to represent data as vectors and matrices, perform vector and matrix algebra operations, and apply concepts of eigenvalues and eigenvectors to machine learning problems. The module on calculus equips learners with the analytical and visual interpretation of differentiation, enabling them to optimize functions commonly used in machine learning and perform gradient descent in neural networks.

Moreover, the probability and statistics module empowers learners to describe and quantify uncertainty in predictions made by machine learning models, apply common statistical methods to machine learning problems, and assess the performance of machine learning models using interval estimates and margin of errors.

• Master the fundamental mathematics toolkit of machine learning
• Gain a deep understanding of the math behind machine learning algorithms
• Learn statistical techniques to enhance data analysis
• Develop fundamental skills desired by employers for machine learning roles

Certificate Available ✔

##### Course Modules

The Mathematics for Machine Learning and Data Science course comprises three modules: Linear Algebra, Calculus, and Probability & Statistics. Each module provides essential mathematical knowledge and practical applications for machine learning and data science.

#### Linear Algebra for Machine Learning and Data Science

The Linear Algebra module delves into the representation of data as vectors and matrices, common vector and matrix algebra operations, and the application of concepts of eigenvalues and eigenvectors to machine learning problems. Learners will gain a comprehensive understanding of linear algebra's essential role in machine learning and data science.

#### Calculus for Machine Learning and Data Science

The Calculus module equips learners with the analytical optimization of functions commonly used in machine learning, the visual interpretation of differentiation, and the practical application of gradient descent in neural networks. This module provides a solid foundation in calculus for machine learning and data science.

#### Probability & Statistics for Machine Learning & Data Science

The Probability & Statistics module empowers learners to describe and quantify uncertainty in predictions made by machine learning models, visually and intuitively understand probability distributions, apply common statistical methods to machine learning problems, and assess the performance of machine learning models using interval estimates and margin of errors. This module provides essential statistical knowledge for machine learning and data science.

#### Algorithms

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#### BioData Processing

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#### Graph Search, Shortest Paths, and Data Structures

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#### Tracking Objects in Video with Particle Filters

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