Data Science

In this section we shall be covering basics and common algorithms in Data Science with in-depth explanations.

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• Training and Test Sets

• Encoding Categorical Variables in Python

• Creating dummy variables

• Standardisation and Normalisation

• Performance Metrics for Classification Problems

• Logistic Regression

• K-Fold Cross Validation

• Grid Search Explained

• K-Nearest Neighbours

• Bagging and Boosting

• Working of Decision Trees

• Random Forests Explained

• K - Means Clustering

• Hierarchical Clustering

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Python Code for Data Science:

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Data pre-processing

• Creating training and test sets in Python

• Encoding Categorical Variables in Python

• Creating dummy variables in Python

• Standardisation and Normalisation in Python

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Algorithms

• Grid Search in Python

• Logistic Regression in Python

• K-Nearest Neighbours in Python

• Decision Trees in Python

• Random Forests in Python

• K-Means Clustering in Python

• Hierarchical Clustering in Python

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More Articles Brewing Up!!