The document outlines a data science workflow and introduces various algorithms, focusing on the key steps of data projects including business understanding, data understanding, preparation, model creation, evaluation, and deployment. It discusses different types of machine learning, including supervised and unsupervised approaches, and provides examples of common algorithms such as linear regression, decision trees, and clustering methods. Additionally, it emphasizes the importance of choosing the right algorithm based on the nature of the target variable.

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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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