Python Machine Learning Programming Chapter 1 Gives Computers the Ability to Learn from Data Summary

Introduction

--Machine learning --Application and science of algorithms to understand the meaning of data --Exciting fields in computer science ――This chapter deals with the main concepts of machine learning and their types. --Contents to be handled --General concept --Three types of learning and basic terms --Components for system design --Python setup --Sample code - python-machine-learning-book/code/ch01/ch01.ipynb ――In the following summary, the code and formula are not described. I'm sorry.


1.1 "Intelligent machines" that turn data into knowledge

--Large amount of data --Structured data --Unstructured data --Examples of application in daily life --Email spam filter --Character / voice recognition software


1.2 3 types of machine learning

--Supervised learning --Unsupervised learning --Reinforcement learning


1.3 Future prediction by "supervised learning"


1.3.1 Classification for predicting class labels

1.3.2 Regression for predicting continuous values


1.4 Reinforcement learning to solve dialogue problems


1.5 Discovering hidden structures through "unsupervised learning"

--Unsupervised learning --Handling unlabeled data or data of unknown structure


1.5.1 Discovery of groups by clustering

--Clustering (unsupervised classification) --Exploratory data analysis that can structure a large amount of information as a meaningful group --Exploratory data analysis: Calculating data statistics and visualizing the distribution to exploratoryly derive knowledge about the data. --Example --Discovery of customer groups in marketing


1.5.2 Dimensionality reduction for data compression

-(Unsupervised) Dimensionality reduction --Compress data into lower dimensional subspaces while preserving most of the relevant information


1.6 Basic terms and notation


1.7 Roadmap for building a machine learning system

--General workflow when using machine learning for predictive modeling

  1. Pretreatment
  2. Learning
  3. Evaluation
  4. Forecast

1.8 Preprocessing: Data shaping


1.8.1 Predictive model training and selection

--Comparison of several algorithms is essential to train and select a good model --Indicator for measuring performance --Correct answer rate --Estimation of model generalization performance --Split training dataset for training and validation, cross-validation --Hyperparameter optimization

1.8.2 Model evaluation and unknown instance prediction

--Evaluation of generalization error --Apply the model to the test dataset and check how well it will perform against unknown data --The parameters of the above procedure such as feature scaling and dimensionality reduction are retrieved only in the training dataset.


1.9 Use Python for machine learning

1.9.1 Installation of Python package


Reference book

-Python Machine Learning Programming


Thank you very much.

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