Forecasting Markets with Machine Learning

Webinar: learn how you can use DataCamp’s Enterprise Dashboard to make sure your team stays ahead of the curve.  [DataCamp](  ISSUE #50 — JUNE 12, 2018  [DataCamp Weekly]( Hi - happy Tuesday. How can spreadsheet workflows be incorporated into more general data science flows in sustainable and healthy ways? Find out in the newest episode of DataFramed with Jenny Bryan: [Spreadsheets in Data Science!]( This week’s top posts range from forecasting markets using XGBoost, converting strings to dates as datetime objects using Pandas, intro to TensorFlow, 5 computer visions techniques and more! But first a special announcement. New webinar! Learn how you can use DataCamp’s Enterprise Dashboard to make sure your team stays ahead of the curve. DataCamp's Enterprise Product Manager will explore features that will help you personalize each team members’ learning experience and monitor how their data skills evolve over time. [Sign up now for our live webinar on Wednesday, June 13th at 10:00 AM EST!](  Community Top Posts  [1]( [Forecasting Markets using eXtreme Gradient Boosting (XGBoost)](  XGBOOST  |  Posted by nitint  [Upvotes]( 11 This post covers the basics of XGBoost machine learning model, along with a sample of XGBoost stock forecasting model using the 'xgboost' package in R programming.  [2]( [Converting Strings to Dates as datetime Objects](  PANDAS  |  Posted by tommyjee  [Upvotes]( 11 Learn how to convert strings to datetime objects in Python and why doing so has become standard practice for working data scientists today.  [3]( [Explaining the 68-95-99.7 rule for a Normal Distribution](  STATISTICS  |  Posted by michaelgalarnyk  [Upvotes]( 7 This post explains how the numbers in the 68-95-99.7 rule were derived in the hope that they can be more interpretable for your future endeavors. The code used to make everything (including the graphs) is available on GitHub.  [4]( [Introduction To Machine Learning K-Nearest Neighbors (KNN) Algorithm I](  MACHINE LEARNING  |  Posted by nitint  [Upvotes]( 6 Overview of one of the simplest algorithms used in machine learning the: K-Nearest Neighbors (KNN) algorithm. This post includes a step by step implementation of KNN algorithm in Python to create a trading strategy using data & classifying new data points based on a similarity measures.  [5]( [Trading Using Machine Learning In Python](  MACHINE LEARNING  |  Posted by nitint  [Upvotes]( 6 Machine Learning has many advantages. It is the hot topic right now. For a trader or a fund manager, the pertinent question is: How can I apply this new tool to generate more alpha? This post explores a model that answers this question in a series of blogs.  [6]( [Understand TensorFlow With a Simple Model](  TENSORFLOW  |  Posted by rizwankhan19  [Upvotes]( 6 TensorFlow is a software framework for building and deploying machine learning models. It provides the basic building blocks to design, train, and deploy machine learning models. This article provides an overview of TensorFlow and a very simple model.  [7]( [Tidy Sentiment Analysis in R](  R PROGRAMMING  |  Posted by bauassr  [Upvotes]( 6 Learn how to perform tidy sentiment analysis in R on Prince's songs, sentiment over time, song level sentiment, the impact of bigrams, and much more!  [8]( [Our Curriculum, Part 1: CS/ML](  DATA SCIENCE  |  Posted by mgelbart  [Upvotes]( 5 This is the first in what will hopefully become a series of posts on our curriculum for the Master of Data Science (MDS) program at UBC. Our program is structured as six four-week blocks, each containing four mini-courses, for a total of 24 mini-courses.  [9]( [The 5 Computer Vision Techniques That Will Change How You See The World](  DEEP LEARNING  |  Posted by khanhle1013  [Upvotes]( 5 Computer Vision is the hottest research field within Deep Learning at the moment. As Computer Vision represents a relative understanding of visual environments and contexts, many scientists believe that the field paves the way towards Artificial General Intelligence due to its cross-domain mastery.   [DataCamp Community]( BY THE COMMUNITY, FOR THE COMMUNITY [Discover](  Population Distribution in Canada   That's all for now. Have a great week!   [DataCamp](  DataCamp Inc. | 350 Fifth Avenue | Suite 7730 | New York, NY 10118  [Facebook]( [Twitter]( [LinkedIn]( [Instagram]( [YouTube](  [Download on the App Store]( [Get it on Google Play](  [Unsubscribe]( Â