M01 - Introduction and Data Science Theory

1. Introduction to Data Science

Doing Data Science means using data to make decisions that drives to action.

Data Science mainly involves:

Predictive analytics is about using past data to predict future values.

Prescriptive analytics is about using those predictions to drive decisions.

2. Data Science Process

Data Science process is iterative and involves these steps:

Historical approach to Data Science

These documents were written in different years in the past but have many things in common.

3. Introduction to Machine Learning

supervised learning - machine learning model is trained using a set of existing, known data values.

Terms to know: feature, label, over-fitted (works only with training data), under-fitted (too general)

unsupervised learning - analyzing data and looking for patterns

Occam's Razor: The best models are simple models that fit the data well.

4. Regression

Algorithms

Evaluation of algorithms

5. Classification

Algorithms

Terms to know: Loss function, Imbalanced data, TPR (True Positive Rate), FPR (False Positive Rate), ROC (Receiver Operator Characteristic)

6. Clustering

Algorithms

Terms to know: distance metric

7. Recommendation

Terms to know: Matrix Factorization

Posted with : Machine Learning

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