In this repository, I have includes all basic and fundamentals implementations of Python and it's alias in Machine Learning. They are summarized below:
- Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.
- Supervised Learning
- Supervised Learning and Neural Networks
- Unsupervised learning is a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision.
- Un-Supervised Learning
- In statistics, the term linear model is used in different ways according to the context. The most common occurrence is in connection with regression models and the term is often taken as synonymous with linear regression model.
- Linear Models and Optimization
- Here, I tried to answer only one question: Will performance of GBDT model drop dramatically if we remove the first tree?
- Gradient Boosting Decision Trees
- You can get insights about the selection of variours Hyper-parameters and it's tuning.
- KNeighbors Classifier
Model Evaluation and Selection
