Visual Studio Code

Visual Studio Code is a lightweight but powerful source code editor which runs on your desktop and is available for Windows, macOS and Linux. It comes with built-in support for JavaScript, TypeScript and Node.js and has a rich ecosystem of extensions for other languages (such as C++, C#, Java, Python, PHP, Go) and runtimes (such as .NET and Unity). Begin your journey with VS Code with these introductory videos.

Website: https://code.visualstudio.com/

Getting Started with Python in VS Code >> https://code.visualstudio.com/docs/python/python-tutorial

Load Raw_Data using NumPy

The source code is for load the data from .csv file. However you need a NumPy. NumPy is the fundamental package for array computing with Python. The answer for source code is (768, 9).

import csv
import numpy
filename = 'pima-indians-diabetes.data.csv'
Raw_Data = open(filename, 'r')
reader = csv.reader(Raw_Data, delimiter=',', quoting=csv.QUOTE_NONE)
x = list(reader)
dataset = numpy.array(x).astype('float')
print(dataset.shape)

The details about the Pima Dataset can be found here >> https://www.kaggle.com/uciml/pima-indians-diabetes-database

or we can load the data form URL

# Load CSV from URL using NumPy
from numpy import loadtxt
import urllib.request
raw_data = urllib.request.urlopen('https://bit.ly/2GX9wC5')
dataset = loadtxt(raw_data, delimiter=",")
print(dataset.shape)

Thingspeak

ThingSpeak™ is a free web service that lets you collect and store sensor data in the cloud and develop Internet of Things applications. The ThingSpeak web service provides apps that let you analyze and visualize your data in MATLAB®, and then act on the data. Sensor data can be sent to ThingSpeak from Arduino®, Raspberry Pi™, BeagleBone Black, and other hardware.

https://thingspeak.com/

Top 10 Machine Learning Algorithms

According to a recent study, machine learning algorithms are expected to replace 25% of the jobs across the world, in the next 10 years. With the rapid growth of big data and availability of programming tools like Python and R –machine learning is gaining mainstream presence for data scientists. Machine learning applications are highly automated and self-modifying which continue to improve over time with minimal human intervention as they learn with more data.

Read More >> https://www.dezyre.com/article/top-10-machine-learning-algorithms/202

Ensemble Methods in Machine Learning

A good paper to read >> http://web.engr.oregonstate.edu/~tgd/publications/mcs-ensembles.pdf

Abstract – Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting. This paper reviews these methods and explains why ensembles can often perform better than any single classifier. Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.