Introduction to information system Machine Learning with Documents… Gerhard Neumann School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science Bag-of-words • We need a way to represent textual information, (what is x)? • We usually speak of Documents and Terms • Think of a document as a Bag-Of-Words (bow) Document: • Texas Instruments said […]
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DS_BW05_Workshop_KNN Week 05: K-Nearest Neighbors (K-NN) and K-Means (1/2) Workshop Exercise Bowei Chen, bchen@lincoln.ac.uk School of Computer Science, University of Lincoln, UK CMP3036M/CMP9063M Data Science 2016 – 2017 Sememster B This workshop covers: The KNN classification algorithm Parsing and importing data from a text file Creating scatter plots with Matplotlib Normalizing numeric values Now you
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Microsoft Learning Experiences CMP3036M/CMP9063M Data Science 2016 – 2017 Semester B Week 06 Workshop In this workshop, you will use Azure Machine Learning Studio to build k-means clustering models. Task 1: K-Means for Adult Census Income Prepare the Data 1. Open a browser and browse to https://studio.azureml.net. Then sign in using the Microsoft account associated
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Introduction to information system Logistic Regression Deema Abdal Hafeth Bowei Chen CMP3036M/CMP9063M Data Science 2016 – 2017 Objectives Limitation of Linear Regression Basic Concepts Logit Function Odds Ratio Logistic Function Logistic Regressions Simple Logistic Regression Multiple Logistic Regression References • James, G., Witten, D., Hastie, T., and
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Introduction to information system Model Selection Bowei Chen, Deema Hafeth and Jingmin Huang School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science 2016 – 2017 Workshop Today’s Objective • Do the following Exercises 1-4. • There are several hints about using the relevant R packages and/or built-in R functions, please google them or read
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Introduction to information system Introduction to Data Science Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science Hello, I’m a Data Scientist! My research interest lies mostly in developing intelligent algorithms and data solutions to the following fields: • Computational advertising: programmatic guarantee • Internet economics and digital products: inventory pricing, information
sms_spamfilter_workshop Data Science Workshop Week 8 – SMS Spam Classification¶ This notebood is based on the “Data Science with Python” notebook from Radmin Rehurek, see http://radimrehurek.com/data_science_python/ The goal of this notebook is to build a working, executable prototype: an app to classify phone SMS messages in English (well, the “SMS kind” of English…) as either
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Introduction to information system Trees and Forests… Gerhard Neumann School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science Step back… Bigger Picture Supervised Learning: We want to learn the relation from the input to the output data • Regression: Continuous output labels – Linear regression, Polynomial Regression, kNN (?) • Classification: Discrete / Nominal
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Introduction to information system Introduction to Monte Carlo Simulation Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science 2016 – 2017 Week B10 Workshop Today’s Objective Do the following two exercises. They will give you a better understanding of how to implement and analyse a Monte Carlo Simulation Ex1: Implement Droprain Experiment
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workshop_topicmodels In [ ]: %matplotlib inline Data Science Workshop Week 10 – Topic Models¶ The goal of this workshop is to extract topic models out of a corpus of 20 newsgroups. The corpus contains over 11000 documents. We will test Non-negative Matrix Factorization and Latent Dirichlet Allocation on this corpus with different pre-processing steps. In order to
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