Handbook of Hidden Data Scientist (Python)
  • Introduction
  • Machine Learning
    • Supervised Learning
      • Features and Labels
      • Linear Decision Surface
      • Naive Bayes
      • Support Vector Machine
      • Decision Trees
      • Regressions
  • Python
  • CSV with pandas
    • Reading CSV
    • Math Operations on Column
    • Joining CSVs
    • Plot and Normalize CSV Data
  • NumPy
    • Using NumPy from pandas DataFrame
    • Create NDArray
    • Working with NDArray
    • Timing operations
  • Statistical Analysis
    • Global Statistics
    • Rolling Statistics
    • Daily Returns
    • Cumulative Returns
  • Incomplete Data
    • Pandas fillna()
  • Histograms and Scatter Plots
    • Histogram
    • Two Histograms
    • Scatter Plot
  • Visualization
    • pyplot
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  1. Machine Learning

Supervised Learning

In supervised learning, you have bunch of examples and you know the answers. Then the machine learning algorithms can mimic or learn what is expected. For example, if you drive a car which has ability to learn from you, the car will observe your driving style. When we would provide these data to supervise learning algorithms, the car could be taught your style of driving and could even drive instead of you (in case you like your style of driving and would like other people to drive like you).

There are two terms that should be understood before going deeper into algorithms. It is features and labels.

PreviousMachine LearningNextFeatures and Labels

Last updated 5 years ago

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