Mathematics for Machine Learning: PCA
Price:49 USD
Date: On request
Type: e-learning
Category: Data Science/Data Engineering
Language: English
City: Online
/en/mathematics-for-machine-learning-pca
Training Description:
This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.
At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you?re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. The lectures, examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms.
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WHAT YOU WILL LEARN
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Implement mathematical concepts using real-world data
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Derive PCA from a projection perspective
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Understand how orthogonal projections work
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Master PCA
Additional information
SKILLS YOU WILL GAIN
- Dimensionality Reduction
- Python Programming
- Linear Algebra
Speakers:
Instructor rating3.84/5?(170 Ratings)
Marc Peter Deisenroth
Lecturer in Statistical Machine Learning
Department of Computing
52,491?Learners
1?Course
Professional skills
machine
Participation
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Training Center Details
Type: LLC/OJSC/CJSC
Number of Employees: 500-1500