maykulkarni / Machine Learning Notebooks
Machine Learning notebooks for refreshing concepts.
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Machine Learning Notebooks
Helpful jupyter noteboks that I compiled while learning Machine Learning and Deep Learning from various sources on the Internet.
NumPy Basics:
Data Preprocessing:
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Feature Selection: Imputing missing values, Encoding, Binarizing.
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Feature Scaling: Min-Max Scaling, Normalizing, Standardizing.
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Feature Extraction: CountVectorizer, DictVectorizer, TfidfVectorizer.
Regression
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Linear & Multiple Regression
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c. Assumptions in Linear Regression: Assumptions in Linear Regression, Dummy Variable Trap
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d. Linear Regression using Scikit-learn: Simple and Multivariable Regression using Scikit-learn.
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Backward Elimination: Method of Backward Elimination, P-values.
Classification
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Logistic Regression
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Regularization
Clustering
Model Evalutaion
Associate Rule Mining
Reinforcement Learning
Natural Language Processing
Neural Networks
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