Скачать или смотреть ML Series #3: Why SkLearn Makes ML 10x Easier (+ Linear Regression Pitfalls)

ML Series #3: Why SkLearn Makes ML 10x Easier (+ Linear Regression Pitfalls)

In this video, we utilize sklearn to create the perceptron with fewer lines of code. However, the perceptron has limitations. To understand the limitations, we dive into its shortcoming: its inability to handle non-linear data distributions, as it relies exclusively on linear decision boundaries. To account for this, we discuss scenarios where linear classification fails and introduce logistic regression as an alternative. Logistic regression’s use of logarithmic operations enables curved decision boundaries, improving classification for more complex datasets.

0:00 Intro
1:06 Coding the Perceptron
1:45 Start Data
2:16 Import Data
3:21 Look At Data Set
5:32 Print Labels
5:52 Training And Testing Data
7:20 Standard Scaler
11:06 Percept
12:05 Set Prediction
13:44 Graph
16:01 Cons of the Perceptron
18:15 Outro

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