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We pick and click on EC2 (the second element on the leftmost column—this panel is shown as it was when this book was written; Amazon regularly makes minor changes, so you may see something slightly different). We now see the EC2 management console as shown in the following screenshot: Chapter 12 In the top-right corner, you can pick your region (see the Amazon regions information box). Note that you will only see information about the region that you have selected at the moment. Thus, if you mistakenly select the wrong region (or have machines running in multiple regions), they may not appear (from casual conversations with other programmers, this seems to be a common pitfall of using the EC2 web management console). [ 251 ]
Big(ger) Data In EC2 parlance, a running server is called an instance. So now, we want to select Launch Instance. Now, follow the classic wizard. Select the Amazon Linux option (if you are familiar with one of the other offered Linux distributions, such as Red Hat, SuSe, or Ubuntu, you can also select one of those, but the configurations will be slightly different). We will start with one instance of the T1.micro type. This is the smallest possible machine, and it is free. Accept all of the defaults until you come to the screen mentioning a key pair: We will pick the name awskeys for the key pair. Then, click on the Create & Download your Key Pair button to download the awskeys.pem file. Save that file somewhere safe! This is the Secure Shell (SSH) key that will enable you to log in to your cloud machine. Accept the remaining defaults and your instance will launch. You will now need to wait for about a minute for your instance to come up. Eventually, the instance will be shown in green with the status "running." Right-clicking on it and selecting the Connect option will bring up instructions on how to connect. The following will be a standard SSH command of the form: ssh -i awskeys.pem ec2-user@ec2-54-244-194-143.us-west-2.compute. amazonaws.com [ 252 ]
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Building Machine Learning Systems w
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Credits Authors Willi Richert Luis
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Luis Pedro Coelho is a Computationa
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Maurice HT Ling completed his PhD.
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Table of Contents Preface 1 Chapter
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Table of Contents Tuning the instan
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Table of Contents Improving classif
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Preface You could argue that it is
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Preface What you need for this book
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Downloading the example code You ca
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Getting Started with Python Machine
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Learning How to Classify with Real-
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Chapter 2 We are using Matplotlib;
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Chapter 2 The last few lines select
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Chapter 2 error = 0.0 for ei in ran
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Chapter 2 We can play around with t
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Chapter 2 Features and feature engi
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Chapter 2 In the preceding screensh
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Chapter 2 Binary and multiclass cla
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Clustering - Finding Related Posts
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Chapter 3 How to do it More robust
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Chapter 3 This means that the first
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Chapter 3 ... post = posts[i] ... i
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Chapter 3 If you have a clear pictu
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Chapter 3 Extending the vectorizer
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Chapter 3 0.0 >>> print(tfidf("b",
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Chapter 3 Flat clustering divides t
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Because the cluster centers are mov
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Chapter 3 'D:\\data\\379\\raw\\comp
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As we have learned previously, we w
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Chapter 3 Position Similarity Excer
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Chapter 3 But before you go there,
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Topic Modeling For those who are in
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Topic Modeling Sparsity means that
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Topic Modeling Although daunting at
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Topic Modeling … for tj,v in t:
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Topic Modeling Finally, we build th
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Topic Modeling Alternatively, we ca
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Topic Modeling Topic modeling was f
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Classification - Detecting Poor Ans
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Classification II - Sentiment Analy
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Chapter 6 Getting to know the Bayes
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Using Naive Bayes to classify Given
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Chapter 6 This denotation "" leads
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Chapter 6 Similarly, we do this for
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Chapter 6 A quick look at the previ
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Chapter 6 To keep our experimentati
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Chapter 6 Y = np.zeros(Y.shape[0])
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Chapter 6 ° ° Experiment with whe
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Chapter 6 We have to be patient whe
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Chapter 6 First, we define a range
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Chapter 6 Determining the word type
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Chapter 6 Successfully cheating usi
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Chapter 6 Our first estimator Now w
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Chapter 6 for d in documents: allca
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Regression - Recommendations You ha
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Chapter 7 The preceding graph shows
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Chapter 7 Root mean squared error a
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Penalized regression The important
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Chapter 7 P greater than N scenario
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Chapter 7 So, we can see that the d
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Chapter 7 Fortunately, scikit-learn
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[ 161 ] Chapter 7 The loading of th
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Chapter 7 Summary In this chapter,
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Regression - Recommendations Improv
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Weights Regression - Recommendation
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Classification III - Music Genre Cl
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Chapter 9 Matplotlib provides the c
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[ 185 ] Chapter 9
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Chapter 9 def create_fft(fn): sampl
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Chapter 9 ax.set_yticks(range(len(g
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Chapter 9 On the left-hand side gra
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[ 193 ] Chapter 9 Improving classif
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We get the following promising resu
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Chapter 9 Summary In this chapter,
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