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Do you want to learn linear algebra?

You have come to the right place.

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Do you want to learn linear algebra?

You have come to the right place.

First and foremost, we want to congratulate you because you have realized the importance of obtaining this skill. Whether you want to pursue a career in data science, machine learning, data analysis, software engineering, or statistics, you will need to know how to apply linear algebra.

This course will allow you to become a professional who understands the math on which algorithms are built, rather than someone who applies them blindly without knowing what happens behind the scenes.

But let’s answer a pressing question you probably have at this point:

“What can I expect from this course and how it will help my professional development?”

In brief, we will provide you with the theoretical and practical foundations for two fundamental parts of data science and statistical analysis – linear algebra and dimensionality reduction.

Linear algebra is often overlooked in data science courses, despite being of paramount importance. Most instructors tend to focus on the practical application of specific frameworks rather than starting with the fundamentals, which leaves you with knowledge gaps and a lack of full understanding. In this course, we give you an opportunity to build a strong foundation that would allow you to grasp complex ML and AI topics.

The course starts by introducing basic algebra notions such as vectors, matrices, identity matrices, the linear span of vectors, and more. We’ll use them to solve practical linear equations, determine linear independence of a random set of vectors, and calculate eigenvectors and eigenvalues, all preparing you for the second part of our learning journey - dimensionality reduction.

The concept of dimensionality reduction is crucial in data science, statistical analysis, and machine learning. This isn’t surprising, as the ability to determine the important features in a dataset is essential - especially in today’s data-driven age when one must be able to work with very large datasets.

Imagine you have hundreds or even thousands of attributes in your data. Working with such complex information could lead to a variety of problems – slow training time, the possibility of multicollinearity, the curse of dimensionality, or even overfitting the training data.

Dimensionality reduction can help you avoid all these issues, by selecting the parts of the data which actually carry important information and disregarding the less impactful ones.

In this course, we’ll discuss two staple techniques for dimensionality reduction – Principal Components Analysis (PCA), and Linear Discriminant Analysis (LDA). These methods transform the data you work with and create new features that carry most of the variance related to a given dataset. First, you will learn the theory behind PCA and LDA. Then, going through two complete examples in Python, you will see how data transformation occurs in practice. For this purpose, you will get one step-by-step application of PCA and one of LDA. Finally, we will compare the two algorithms in terms of speed and accuracy.

We’ve put a lot of effort to make this course the perfect foundational training for anyone who wants to become a data analyst, data scientist, or machine learning engineer.

Enroll now

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops a strong understanding of linear algebra, which is foundational in data science and applied mathematics
Covers concepts such as vectors, matrices, and linear equations, which are essential for data analysis and modeling
Provides practical examples in Python, making it applicable to real-world scenarios
Focuses on dimensionality reduction, a key technique for managing large datasets
Compares and contrasts Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) for dimensionality reduction
Suitable for data scientists, analysts, and machine learning engineers seeking to build a solid foundation in linear algebra and dimensionality reduction

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Linear Algebra and Feature Selection in Python with these activities:
Review Pre-requisite Math
Ensure a solid foundation by reviewing relevant pre-requisite math concepts such as matrices and determinants
Browse courses on Linear Algebra
Show steps
  • Go over notes or textbooks from previous math courses
  • Complete practice problems to test understanding
Khan Academy Linear Algebra Tutorials
Review the basics of linear algebra and reinforce your understanding by following guided tutorials
Browse courses on Linear Algebra
Show steps
  • Watch video tutorials on key concepts
  • Complete practice exercises within the tutorials
Join a Linear Algebra Study Group
Engage with peers in a study group to discuss concepts, work through problems, and provide support
Browse courses on Linear Algebra
Show steps
  • Find a study group or create one with classmates
  • Meet regularly to discuss course material and solve problems
Five other activities
Expand to see all activities and additional details
Show all eight activities
Linear Algebra Practice Problems
Practice solving common linear algebra problems to improve understanding of the subject and prepare for more advanced topics
Browse courses on Linear Algebra
Show steps
  • Solve practice problems focusing on matrix manipulation
  • Solve practice problems focusing on solving systems of linear equations
Create a Linear Algebra Cheat Sheet
Summarize key concepts and formulas from linear algebra into a single reference document that can be used for quick review
Browse courses on Linear Algebra
Show steps
  • Identify key concepts and formulas
  • Organize and present the information in a clear and concise manner
Read Introduction to Linear Algebra by Gilbert Strang
Gain a comprehensive understanding of linear algebra concepts and applications through a widely acclaimed textbook
Show steps
  • Read each chapter thoroughly
  • Work through the practice exercises
  • Apply the concepts to real-world examples
Read Linear Algebra Done Right by Sheldon Axler
Familiarize yourself with a more advanced treatment of linear algebra which will help reinforce concepts taught during the course and allow you to apply those concepts in a more advanced setting.
Show steps
  • Read each chapter in its entirety
  • Complete practice questions at the end of each chapter
Develop a Linear Algebra Project
Apply linear algebra concepts to solve a real-world problem or create an original application
Browse courses on Linear Algebra
Show steps
  • Identify a problem or application that can be addressed using linear algebra
  • Design and implement a solution using linear algebra techniques
  • Evaluate and present the results

Career center

Learners who complete Linear Algebra and Feature Selection in Python will develop knowledge and skills that may be useful to these careers:
Market Researcher
Market Researchers collect, analyze, and interpret data to understand consumer behavior and market trends. This course may be useful for individuals in this role as it provides a foundation in linear algebra, which is used to analyze survey data and identify patterns, and dimensionality reduction techniques, which can help identify key variables and reduce model complexity.
Machine Learning Engineer
Machine Learning Engineers design, develop, and implement machine learning models to solve business problems. This course may be useful for individuals in this role by helping them build a strong foundation in linear algebra, which is essential for understanding and applying machine learning algorithms, as well as dimensionality reduction techniques, which can help optimize model performance.
Business Analyst
Business Analysts use data and analysis to understand business needs and make recommendations for improvement. This course may be useful for individuals in this role as it provides a foundation in linear algebra, which is used to analyze data and identify patterns, and dimensionality reduction techniques, which can help identify key variables and reduce model complexity.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course may be useful for individuals in this role as it provides a foundation in linear algebra, which is used to model and solve optimization problems, and dimensionality reduction techniques, which can help identify key variables and reduce model complexity.
Statistician
Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course may be useful for individuals in this role as it covers fundamental concepts in linear algebra and dimensionality reduction, which are essential for understanding statistical techniques and developing predictive models.
Data Architect
Data Architects design and manage data systems and infrastructure. This course may be useful for individuals in this role as it provides a foundation in linear algebra, which is used to model and optimize data structures, and dimensionality reduction techniques, which can help reduce data complexity and improve system performance.
Data Scientist
A Data Scientist collects, analyzes, and interprets data to build predictive models and algorithms that can determine future trends. This course may be useful for individuals in this role by helping them understand foundational concepts in linear algebra and dimensionality reduction, which are critical for developing and evaluating machine learning algorithms.
Data Analyst
Data Analysts collect, clean, and analyze data to extract meaningful insights and make business recommendations. This course may be useful for individuals in this role as it provides a foundation in linear algebra, which is important for understanding data structures and transformations, and dimensionality reduction techniques, which can help identify patterns and relationships in complex datasets.
Database Administrator
Database Administrators manage and maintain database systems. This course may be useful for individuals in this role as it provides a foundation in linear algebra, which is used to optimize database structures and queries, and dimensionality reduction techniques, which can help reduce data complexity and improve system performance.
Financial Analyst
Financial Analysts analyze financial data and make recommendations for investment and other financial decisions. This course may be useful for individuals in this role as it provides a foundation in linear algebra, which is used to analyze financial models and identify risk factors, and dimensionality reduction techniques, which can help identify patterns and relationships in complex financial datasets.
Risk Analyst
Risk Analysts identify, assess, and manage risks in various industries. This course may be useful for individuals in this role as it provides a foundation in linear algebra, which is used to model and analyze risks, and dimensionality reduction techniques, which can help identify patterns and relationships in complex risk data.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course may be useful for individuals in this role as it provides a strong foundation in linear algebra, which is essential for understanding financial models, and dimensionality reduction techniques, which can help identify patterns and relationships in complex financial datasets.
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course may be useful for individuals in this role as it provides a foundation in linear algebra, which is used to model insurance and financial risks, and dimensionality reduction techniques, which can help identify patterns and relationships in complex risk data.
Credit Analyst
Credit Analysts assess the creditworthiness of individuals and businesses. This course may be useful for individuals in this role as it provides a foundation in linear algebra, which is used to analyze financial data and identify risk factors, and dimensionality reduction techniques, which can help identify patterns and relationships in complex financial datasets.
Software Engineer
Software Engineers design, develop, and maintain software applications. While this course is not directly related to software engineering, it may provide some foundational knowledge that can be beneficial in certain domains. For example, linear algebra is used in computer graphics and game development, while dimensionality reduction techniques are used in data compression and natural language processing.

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Linear Algebra and Feature Selection in Python.
Comprehensive introduction to statistical learning methods in Chinese. It covers topics such as supervised learning, unsupervised learning, and deep learning. It good choice for students who want to learn more about the theoretical and practical aspects of statistical learning methods in Chinese.
Comprehensive introduction to deep learning in Chinese. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It good choice for students who want to learn more about the theoretical and practical aspects of deep learning in Chinese.
Comprehensive introduction to machine learning in Chinese. It covers topics such as supervised learning, unsupervised learning, and deep learning. It good choice for students who want to learn more about the theoretical and practical aspects of machine learning in Chinese.
Provides a clear and concise introduction to linear algebra, covering the basics of vectors, matrices, and linear transformations. It valuable resource for students who want to learn the fundamentals of linear algebra and its applications.
Provides a comprehensive introduction to dimensionality reduction techniques for machine learning. It covers topics such as principal component analysis, singular value decomposition, and manifold learning. It good choice for students who want to learn more about the theoretical and practical aspects of dimensionality reduction.
Provides a comprehensive introduction to deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It good choice for students who want to learn more about the theoretical and practical aspects of deep learning.
Provides a practical introduction to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It good choice for students who want to learn how to apply machine learning techniques to real-world problems.
Practical introduction to machine learning in Chinese. It covers topics such as supervised learning, unsupervised learning, and deep learning. It good choice for students who want to learn more about the theoretical and practical aspects of machine learning in Chinese.
Comprehensive introduction to machine learning in Chinese. It covers topics such as supervised learning, unsupervised learning, and deep learning. It good choice for students who want to learn more about the theoretical and practical aspects of machine learning in Chinese.
More comprehensive introduction to linear algebra, covering a wider range of topics than the previous book. It good choice for students who want to learn more about linear algebra and its applications in other fields.
Provides a comprehensive introduction to matrix analysis and applied linear algebra, covering topics such as matrix norms, eigenvalues, and singular value decomposition. It good choice for students who want to learn more about the practical applications of linear algebra.

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