What I learned about AI / machine learning using Python (1)

Introduction

I'm studying with this book How to make AI / machine learning / deep learning apps with Python

1-1 What can you do with machine learning?

--Classification ... Classify given data Examine and classify data characteristics

--Regression ... Predict future values from past performance Learn past data to make future numerical predictions

--Clustering ... Classify data into a set of similar things What is different from the classification is not to divide into predetermined items, but to classify similar items.

--Recommendation ... Derivation of relevant information on the data We recommend online shopping that suits your tastes

--Dimensionality reduction ... Reducing data features Identify characteristic data from large dimensions, reduce dimensions, and analyze data efficiently

What can it be applied to?

--Image analysis Judging the object of the image --Voice analysis: Convert voice to text and determine what kind of sound it is --Text analysis: Sentence categorization, extraction of specific expressions, parsing

Types of machine learning

--Supervised Learning --The correct answer is given along with the data --Make predictions for unknown data --Unsupervised Learning --No correct answer is given --Find regularity from unknown data --Reinforcement Learning --The action gives a partial correct answer --Find the right buy from the data

1-2 What kind of scenario is machine learning performed?

  1. Goal decision
  2. Data collection
  3. Data shaping and processing
  4. Learn data
  5. Select a machine learning method
  6. Parameter adjustment
  7. Learn the data to build the model
  8. Evaluate the model
  9. Return to 4. if not accurate enough
  10. Utilized in business

1-3 How to create data used in machine learning

A versatile data format useful for machine learning

--Comma separated data CSV format --ini file format

In addition, Numpy save format, pickle that can write Python objects as they are, etc.

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