
Mathematics for Machine Learning: Linear Algebra





This course on Linear Algebra explores the fundamentals of vectors and matrices, as well as the application of eigenvalues and eigenvectors to solve problems. It also covers the use of linear algebra to manipulate datasets, such as rotating images and understanding the Pagerank algorithm.▼
Course Feature
Cost:
Free
Provider:
Coursera
Certificate:
Paid Certification
Language:
English
Start Date:
24th Apr, 2023
Course Overview
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Updated in [March 06th, 2023]
This course, Mathematics for Machine Learning: Linear Algebra, provides an introduction to linear algebra and its applications in machine learning. Students will learn the fundamentals of vectors and matrices, including eigenvalues and eigenvectors, and how to use them to solve problems. The course will also cover how to use these concepts to do fun things with datasets, such as rotating images of faces and extracting eigenvectors to understand the Pagerank algorithm. Students will also gain experience in coding with Python and Jupyter notebooks. Upon completion of the course, students will have an intuitive understanding of vectors and matrices and how to apply them to machine learning.
[Applications]
Upon completion of this course, students can apply the concepts of linear algebra to machine learning by using vectors and matrices to solve problems. They can also use the code blocks and Jupyter notebooks in Python to implement the ideas they have learned. Additionally, they will have an intuitive understanding of vectors and matrices that will help them bridge the gap into linear algebra problems.
[Career Paths]
1. Data Scientist: Data Scientists are responsible for collecting, analyzing, and interpreting large amounts of data to identify trends and patterns. They use their knowledge of mathematics, statistics, and machine learning to develop algorithms and models that can be used to make predictions and decisions. Data Scientists are in high demand and the field is expected to continue to grow in the coming years.
2. Machine Learning Engineer: Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They use their knowledge of mathematics, statistics, and machine learning to develop algorithms and models that can be used to make predictions and decisions. They also need to be able to interpret and explain the results of their models.
3. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for designing, developing, and deploying artificial intelligence systems. They use their knowledge of mathematics, statistics, and machine learning to develop algorithms and models that can be used to make predictions and decisions. They also need to be able to interpret and explain the results of their models.
4. Research Scientist: Research Scientists are responsible for conducting research and developing new algorithms and models. They use their knowledge of mathematics, statistics, and machine learning to develop algorithms and models that can be used to make predictions and decisions. They also need to be able to interpret and explain the results of their models.
The demand for these positions is expected to continue to grow as the use of machine learning and artificial intelligence becomes more widespread. Companies are increasingly looking for professionals with the skills to develop and deploy machine learning and artificial intelligence models. As the technology advances, the need for professionals with the skills to develop and deploy these models will only increase.
[Education Paths]
1. Bachelor of Science in Mathematics: This degree program provides students with a comprehensive understanding of mathematics, including linear algebra. Students will learn the fundamentals of linear algebra, including vectors, matrices, and eigenvalues, as well as how to apply these concepts to machine learning. Additionally, students will gain an understanding of the principles of calculus, probability, and statistics, which are essential for data analysis. As the demand for data-driven applications continues to grow, this degree program will become increasingly valuable.
2. Master of Science in Data Science: This degree program provides students with a comprehensive understanding of data science, including linear algebra. Students will learn the fundamentals of linear algebra, including vectors, matrices, and eigenvalues, as well as how to apply these concepts to machine learning. Additionally, students will gain an understanding of the principles of calculus, probability, and statistics, which are essential for data analysis. As the demand for data-driven applications continues to grow, this degree program will become increasingly valuable.
3. Master of Science in Artificial Intelligence: This degree program provides students with a comprehensive understanding of artificial intelligence, including linear algebra. Students will learn the fundamentals of linear algebra, including vectors, matrices, and eigenvalues, as well as how to apply these concepts to machine learning. Additionally, students will gain an understanding of the principles of calculus, probability, and statistics, which are essential for data analysis. As the demand for data-driven applications continues to grow, this degree program will become increasingly valuable.
4. Doctor of Philosophy in Computer Science: This degree program provides students with a comprehensive understanding of computer science, including linear algebra. Students will learn the fundamentals of linear algebra, including vectors, matrices, and eigenvalues, as well as how to apply these concepts to machine learning. Additionally, students will gain an understanding of the principles of calculus, probability, and statistics, which are essential for data analysis. As the demand for data-driven applications continues to grow, this degree program will become increasingly valuable.
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