Fundamental Linear Algebra Concepts with Python
Learn the basics of Fundamental Linear Algebra Concepts with Python ▼
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Course Feature
Cost:
Free
Provider:
Coursera
Certificate:
Paid Certification
Language:
English
Start Date:
29th May, 2023
Course Overview
❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [March 06th, 2023]
This course introduces students to fundamental linear algebra concepts using Python. Students will learn how to find inverses and perform matrix algebra, as well as how to use row reduction to solve linear equations and define linear transformations. Through this course, students will gain a better understanding of linear algebra and its applications in Python.
[Applications]
Upon completion of this course, students should be able to apply the fundamental linear algebra concepts they have learned to solve real-world problems. They should be able to use Python to find inverses and matrix algebra, as well as use row reduction to solve linear equations and define linear transformations. Additionally, students should be able to apply the concepts to other programming languages.
[Career Paths]
1. Data Scientist: Data Scientists use their knowledge of mathematics, statistics, and programming to analyze large datasets and uncover insights. They use their findings to develop predictive models and algorithms that can be used to make decisions and solve problems. Data Scientists are in high demand, and the field is expected to continue to grow as more organizations rely on data-driven decision making.
2. Machine Learning Engineer: Machine Learning Engineers use their knowledge of mathematics, statistics, and programming to develop algorithms and models that can be used to automate tasks. They are responsible for designing, building, and deploying machine learning systems that can be used to solve real-world problems. This field is expected to continue to grow as more organizations rely on machine learning to automate tasks and make decisions.
3. Artificial Intelligence Engineer: Artificial Intelligence Engineers use their knowledge of mathematics, statistics, and programming to develop algorithms and models that can be used to automate tasks. They are responsible for designing, building, and deploying AI systems that can be used to solve real-world problems. This field is expected to continue to grow as more organizations rely on AI to automate tasks and make decisions.
4. Quantitative Analyst: Quantitative Analysts use their knowledge of mathematics, statistics, and programming to analyze large datasets and uncover insights. They use their findings to develop predictive models and algorithms that can be used to make decisions and solve problems. This field is expected to continue to grow as more organizations rely on quantitative analysis to make decisions and solve problems.
[Education Paths]
1. Bachelor of Science in Mathematics: A Bachelor of Science in Mathematics is a great degree path for those interested in learning more about linear algebra concepts. This degree will provide students with a strong foundation in mathematics, including linear algebra, calculus, and statistics. Additionally, students will learn how to apply these concepts to real-world problems. As technology continues to advance, the demand for mathematicians with a strong understanding of linear algebra is expected to increase.
2. Master of Science in Computer Science: A Master of Science in Computer Science is a great degree path for those interested in applying linear algebra concepts to computer programming. This degree will provide students with a strong foundation in computer science, including programming languages, algorithms, and data structures. Additionally, students will learn how to use linear algebra to solve complex problems in computer science. As technology continues to advance, the demand for computer scientists with a strong understanding of linear algebra is expected to increase.
3. Bachelor of Science in Data Science: A Bachelor of Science in Data Science is a great degree path for those interested in applying linear algebra concepts to data analysis. This degree will provide students with a strong foundation in data science, including data mining, machine learning, and artificial intelligence. Additionally, students will learn how to use linear algebra to analyze and interpret data. As data continues to become more complex, the demand for data scientists with a strong understanding of linear algebra is expected to increase.
4. Master of Science in Applied Mathematics: A Master of Science in Applied Mathematics is a great degree path for those interested in applying linear algebra concepts to real-world problems. This degree will provide students with a strong foundation in applied mathematics, including numerical analysis, optimization, and probability. Additionally, students will learn how to use linear algebra to solve complex problems in a variety of fields. As technology continues to advance, the demand for applied mathematicians with a strong understanding of linear algebra is expected to increase.
Pros & Cons
Interesting lectures, knowledgeable instructors.
Quiz questions worded incorrectly, out of order, or no valid choices
fastpaced lectures
broken course.
Course Provider
Provider Coursera's Stats at AZClass
In Fundamental Linear Algebra Concepts with Python, learners will gain a comprehensive understanding of fundamental linear algebra concepts using Python. They will learn how to invert and use matrix algebra, and practice row reduction to solve linear equations. Additionally, learners will understand how to define linear transformations. Armed with this knowledge, learners will be able to apply linear algebra to a variety of real-world problems. Learners will also understand the importance of linear algebra in data science and machine learning. They will learn how to use linear algebra to analyze data and create models. Additionally, learners will understand how to use linear algebra to create algorithms and solve complex problems.
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