After completion of ML course from Stanford University I thought of exploring more ML algorithms and making models in python using inbuilt libraries. This repo consists of all the basic models of algorithms that I have encountered in my AI journey. Contents are as follows:
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REGRESSION
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Linear Regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression
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CLASSIFICATION
- Logistic Regression
- K Nearest Neighbors
- Support Vector Machine(Linear and Non-linear)
- Decision Trees
- Random Forest
- Naive Bayes
- XGBoost
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CLUSTERING
- Heirarchical
- K Means
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ASSOCIATION LEARNING
- Apriori
- Eclat
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REINFORCEMENT LEARNING
- Upper Confidence Bound
- Thompson Sampling
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NATURAL LANGUAGE PROCESSING
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DEEP LEARNING
- Artificial Neural Networks
- Convolutional Neural Networks
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DIMENSIONALITY REDUCTION
- Principal Component Analysis
- Linear Discriminant Analysis
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PYTHON LIBRARIES USED
