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Machine Learning with Python Lab

(ML-PYTHON.AP1.0L0)
Lab
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Skills You’ll Get

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1

Some Technical Background

  • Plotting a Probability Distribution Graph
  • Using the zip Function
  • Calculating the Sum of Squares
  • Plotting a Line Graph
  • Plotting a 3D Graph
  • Plotting a Polynomial Graph
  • Using the numpy.dot() Method
2

Predicting Categories: Getting Started with Classification

  • Displaying Histograms
3

Predicting Numerical Values: Getting Started with Regression

  • Defining an Outlier
  • Calculating the Median Value
  • Estimating the Multiple Regression Equation
4

Evaluating and Comparing Learners

  • Constructing a Swarm Plot
  • Using the describe() Method
  • Viewing Variance
5

Evaluating Classifiers

  • Creating a Confusion Matrix
  • Creating an ROC Curve
  • Recreating an ROC Curve
  • Creating a Trendline Graph
6

Evaluating Regressors

  • Viewing the Standard Deviation
  • Constructing a Scatterplot
  • Evaluating the Prediction Error Rates
7

More Classification Methods

  • Evaluating a Logistic Model
  • Creating a Covariance Matrix
  • Using the load_digits() Function
8

More Regression Methods

  • Illustrating a Less Consistent Relationship
  • Illustrating a Piecewise Constant Regression
9

Manual Feature Engineering: Manipulating Data for Fun and Profit

  • Manipulating the Target
  • Manipulating the Input Space
10

Combining Learners

  • Calculating the Mean Value
11

Models That Engineer Features for Us

  • Displaying a Correlation Matrix
  • Creating a Nonlinear Model
  • Performing a Principal Component Analysis
  • Using the Manifold Method
12

Feature Engineering for Domains: Domain-Specific Learning

  • Encoding Text
13

Connections, Extensions, and Further Directions

  • Building an Estimated Simple Linear Regression Equation

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