Before you can plot anything, you need to specify which backend Matplotlib should use. The simplest option is to use Jupyter’s magic command %matplotlib inline. This tells Jupyter to set up Matplotlib so it uses Jupyter’s own backend. Scatter Plot ho…
In the former article "Data Preparation by Pandas and Scikit-Learn", we discussed about a series of steps in data preparation. Scikit-Learn provides the Pipeline class to help with such sequences of transformations. The Pipeline constructor take…
In this article, we dicuss some main steps in data preparation. Drop Labels Firstly, we drop labels for train set. Here we use drop() method in Pandas library. housing = strat_train_set.drop("median_house_value", axis=1) # drop labels for traini…
Getting started with machine learning in Python Machine learning is a field that uses algorithms to learn from data and make predictions. Practically, this means that we can feed data into an algorithm, and use it to make predictions about what might…
In machine learning, is more data always better than better algorithms? No. There are times when more data helps, there are times when it doesn't. Probably one of the most famous quotes defending the power of data is that of Google's Research Directo…
Here I list some useful functions in Python to get familiar with your data. As an example, we load a dataset named housing which is a DataFrame object. Usually, the first thing to do is get top five rows the dataset by head() function: housing = load…
The Dataset was acquired from https://www.kaggle.com/c/titanic For data preprocessing, I firstly defined three transformers: DataFrameSelector: Select features to handle. CombinedAttributesAdder: Add a categorical feature Age_cat which divided all pa…
Using Pandas Library The simplest way is to read data from .csv files and store it as a data frame object: import pandas as pd df = pd.read_csv('olympics.csv', index_col=0, skiprows=1) You can also read .xsl files and directly select the rows and col…
import numpy as np import matplotlib.pyplot as plt def is_outlier(points, threshold=3.5): if len(points.shape) == 1: points = points[:, None] # Find the median number of points median = np.median(points, axis=0) diff = np.sum((points - median)**2, ax…
Machine and Deep Learning with Python Education Tutorials and courses Supervised learning superstitions cheat sheet Introduction to Deep Learning with Python How to implement a neural network How to build and run your first deep learning network Neur…
https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644 How Can I Learn X? Learning Machine Learning Learning About Computer Science Educational Resources Advice Artificial Intelligence How-to Question Learning New Things Lea…
转自:机器学习(Machine Learning)&深度学习(Deep Learning)资料 <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最…
原文地址:http://www.demnag.com/b/java-machine-learning-tools-libraries-cm570/?ref=dzone This is a list of 25 Java Machine learning tools & libraries. Weka has a collection of machine learning algorithms for data mining tasks. The algorithms can either be…
We should think in below four questions: the decription of machine learning key tasks in machine learning why you need to learn about machine learning why python is so great for machine learning 1.The author talked some examples about machine learnin…
