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MATLAB for Machine Learning

You're reading from   MATLAB for Machine Learning Unlock the power of deep learning for swift and enhanced results

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781835087695
Length 374 pages
Edition 2nd Edition
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Getting Started with Matlab
2. Chapter 1: Exploring MATLAB for Machine Learning FREE CHAPTER 3. Chapter 2: Working with Data in MATLAB 4. Part 2: Understanding Machine Learning Algorithms in MATLAB
5. Chapter 3: Prediction Using Classification and Regression 6. Chapter 4: Clustering Analysis and Dimensionality Reduction 7. Chapter 5: Introducing Artificial Neural Network Modeling 8. Chapter 6: Deep Learning and Convolutional Neural Networks 9. Part 3: Machine Learning in Practice
10. Chapter 7: Natural Language Processing Using MATLAB 11. Chapter 8: MATLAB for Image Processing and Computer Vision 12. Chapter 9: Time Series Analysis and Forecasting with MATLAB 13. Chapter 10: MATLAB Tools for Recommender Systems 14. Chapter 11: Anomaly Detection in MATLAB 15. Index 16. Other Books You May Enjoy

Discovering exploratory statistics

Exploratory statistics refers to the initial phase of data analysis where various statistical techniques are employed to understand the main characteristics of a dataset. There are many techniques available, but the most used one is the following.

EDA

EDA is an approach to analyzing data that focuses on understanding the main characteristics, patterns, and relationships within a dataset. It involves using statistical techniques and visualizations to summarize and explore the data to gain insights and formulate hypotheses. Here are some key steps and techniques involved in EDA:

  • Data summary: Start by examining the basic summary statistics of the dataset, such as the mean, median, standard deviation, minimum, maximum, and so on. This gives an initial understanding of the central tendency, spread, and distribution of the data.
  • Data distribution: Examine the distribution of individual variables to identify any skewness or non-normality...
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