Regression in scikit-learn
We will compare several regression methods by using the same dataset. We will try to predict the price of a house as a function of its attributes. In [6]: import numpy…
k-Nearest Neighbors(kNN)
k-Nearest Neighbors(kNN) Pros: High accuracy, insensitive to outliers, no assumptions about data Cons: Computationally expensive, requires a lot of memory Works with: Numeric values, nominal values We have an existing…
Linear Classification method with ScikitLearn
This blog is from the book and aimed to be as a learning material for myself only.Linear Classification method implements regularized linear models with stochastic gradient descent (SGD) learning. Each sample estimates…
Kaggle Tutorial using Kobe Bryant Dataset
This part is a kaggle tutorial using Kobe Bryant Dataset - Part 1. You can get the data from https://www.kaggle.com/c/kobe-bryant-shot-selection . What excited me was that this dataset is excellent…
Machine Learning applications
Detailed below are some of the recent machine learning applications. Agriculture and Farming About 8000 B.C. ago, at the dawn of agriculture, the total number of living human beings on…
A history of Machine Learning
Machine learning inspired systems are now being used in wide range of fields to solve a whole variety of problems. Machine learning facilitates the use of information to work smarter…
First Proven Comprehensive Overview of Machine Learning
Machine learning detects patterns in data, focuses on growth and adjusts when new data exposes a computer programme. It is closely related to computational statistics and links to mathematical optimisation.…