Data Analysis with Machine Learning

Ctrlk

Book Content
Chapter 1. Introduction to Data Analysis and Python Libraries
Chapter 2. Working with NumPy and Pandas
Chapter 3. Statistics and Probability
Chapter 4. Data Visualization
Chapter 5. Data Preprocessing and Cleaning
Chapter 6. Feature Engineering
Chapter 7. Understanding EDA Frameworks
Chapter 8. Linear Algebra for Machine Learning
Chapter 9. Linear Regression
Chapter 10. Model Evaluation and Polynomial Regression
Chapter 11. Logistic Regression
Chapter 12. Classification Techniques
Chapter 13. Unsupervised Learning
Chapter 14. Clustering Techniques

Powered by GitBook

On this page

Chapter 9. Linear Regression

PreviousChapter 8. Linear Algebra for Machine Learning NextChapter 10. Model Evaluation and Polynomial Regression