In the second part of our applied machine learning online course, you'll get an overview of the different steps in the data science workflow as well as a deep dive in 3 basic types of models: linear, tree-based and clustering.

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2nd Class: The data science workflow
& introduction to algorithms

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● September 27th at 12PM ET: The data science workflow, building a predictive model flow
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Curriculum

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The plan for today

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7 steps of a data projects

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Advanced version of the workflow
Dataset 1
Scored
dataset
Scored
dataset
Dataset 2
Dataset n

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Business understanding

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Data understanding

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Data preparation

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Model Creation

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Evaluation

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Deployment

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Most Advanced version of the workflow

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Machine Learning:
What are we talking about?

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copyright @Machine Learning Mastery

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Different types of Machine Learning
Data is labeled, algorithm predicts an
output feature from the input data
Data isn’t labeled, algorithm learns the
inherent structure of the data and makes a
prediction
Examples: Examples:

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Different types of Machine Learning
If your target is # Numerical (continuous) If your target is A Categorical (discrete)
Example: Examples:

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Different types of Prediction

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How can we chose an algorithm?

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Different types of Machine Learning
Goal: Goal:
Examples:
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Examples:
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Most common types of Machine Learning

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Linear Regressions

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Logistic Regressions

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Most common types of Tree Based Models

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The decision tree

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More complicated tree

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More trees!

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Random Forest

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Gradient Boosting Trees

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Isolation Forest

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K-means clustering
http://www.naftaliharris.com/blog/visualizing-k-means-clustering/

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Qu s o s?

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