Linear Algebra and Feature Selection in Python
What you'll learn
- Understand the math behind machine learning models
- Become familiar with basic and advanced linear algebra notions
- Be able to solve linear equations
- Determine independency of a set of vectors
- Calculate eigenvalues and eigenvectors
- Perform Linear Discriminant Analysis
- Perform Dimensionality Reduction in Python
- Carry out Principal Components Analysis
- Compare the performance of PCA and LDA for classification with SVMs
- Suitable for beginners. Some understanding of Python basics and math would be an advantage.
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.
Who this course is for:
- Ideal for beginner data science and machine learning students
- Aspiring data analysts
- Aspiring data scientists
- Aspiring machine learning engineers
- People who want to level-up their career and add value to their company
- Anyone who wants to start a career in data science or machine learning
365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings.
Currently, 365 focuses on the following topics on Udemy: 1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA
2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy
3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing
4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook
5) Blockchain for Business
All of our courses are:
- Real-life tested
By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.
If you want to become a financial analyst, a data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur
365 Careers’ courses are the perfect place to start.