machine learning fundamental and application
Expert in machine learning and statistical decision theory, focusing on theory and mathematics.
In this book 'machine learning fundamental and application' by Shubham Kulkarni, a comprehensive exploration of machine learning theory and mathematics is provided, with a focus on Bayesian decision theory, Maximum Likelihood Estimation for Logistic Regression, Dimensionality Reduction using PCA, Linear Discriminant Functions, and Fisher's Analysis. With insights into tools such as Python, DALL-E, and browser-based applications, this book serves as a valuable resource for those interested in diving deep into the world of machine learning and statistical decision theory.
How to use
To make the most of this resource, follow these steps:
- Explore topics related to Bayesian decision theory in the context of Decision Theory.
- Implement Maximum Likelihood Estimation for Logistic Regression.
- Learn about the process of Dimensionality Reduction using PCA.
- Understand Linear Discriminant Functions and Fisher's Analysis.
Features
- Expert insights into machine learning theory and mathematics
- Practical guidance on implementing key machine learning algorithms
- In-depth exploration of Bayesian decision theory and mathematical concepts
- Coverage of popular tools such as Python, DALL-E, and browser-based applications
Updates
2023/12/19
Language
English (English)
Welcome message
Hello! Ready to dive into machine learning and statistical decision theory?
Prompt starters
- Explain Bayesian decision theory in Decision Theory.
- How do I implement Maximum Likelihood Estimation for Logistic Regression?
- Describe the process of Dimensionality Reduction using PCA.
- Explain Linear Discriminant Functions and Fisher's Analysis.
Tools
- python
- dalle
- browser
Tags
public
reportable