Learning Data Mining with Python

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About the Reviewers Asad Ahamad is a data enthusiast and loves to work on data to solve challenging problems. He did his master's degree in industrial mathematics with computer application at Jamia Millia Islamia, New Delhi. He admires mathematics a lot and always tries to use it to gain maximum profit for businesses. He has good experience working in data mining, machine learning, and data science and has worked for various multinationals in India. He mainly uses R and Python to perform data wrangling and modeling. He is fond of using open source tools for data analysis. He is an active social media user. Feel free to connect with him on Twitter at @asadtaj88. P Ashwin is a Bangalore-based engineer who wears many different hats depending on the occasion. He graduated from IIIT, Hyderabad at in 2012 with an M Tech in computer science and engineering. He has a total of 5 years of experience in the software industry, where he has worked in different domains such as testing, data warehousing, replication, and automation. He is very well versed in DB concepts, SQL, and scripting with Bash and Python. He has earned professional certifications in products from Oracle, IBM, Informatica, and Teradata. He's also an ISTQB-certified tester. In his free time, he volunteers in different technical hackathons or social service activities. He was introduced to Raspberry Pi in one of the hackathons and he's been hooked on it ever since. He writes a lot of code in Python, C, C++, and Shell on his Raspberry Pi B+ cluster. He's currently working on creating his own Beowulf cluster of 64 Raspberry Pi 2s. www.allitebooks.com
Christophe Van Gysel is pursuing a doctorate degree in computer science at the University of Amsterdam under the supervision of Maarten de Rijke and Marcel Worring. He has interned at Google, where he worked on large-scale machine learning and automated speech recognition. During his internship in Facebook's security infrastructure team, he worked on information security and implemented measures against compression side-channel attacks. In the past, he was active as a security researcher. He discovered and reported security vulnerabilities in the web services of Google, Facebook, Dropbox, and PayPal, among others. Edward C. Delaporte V leads a software development group at the University of Illinois, and he has contributed to the documentation of the Kivy framework. He is thankful to all those whose contributions to the open source community made his career possible, and he hopes this book helps continue to attract enthusiasts to software development. www.allitebooks.com
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Page 11 and 12: Table of Contents Preprocessing usi
Page 13 and 14: Table of Contents Chapter 7: Discov
Page 15 and 16: Table of Contents GPU optimization
Page 18 and 19: Preface If you have ever wanted to
Page 20 and 21: What you need for this book It shou
Page 22 and 23: Preface Reader feedback Feedback fr
Page 24 and 25: Getting Started with Data Mining We
Page 26 and 27: Chapter 1 In the preceding dataset,
Page 28 and 29: After you have the above "Hello, wo
Page 30 and 31: Chapter 1 Windows users may need to
Page 32 and 33: Chapter 1 The dataset we are going
Page 34 and 35: Chapter 1 As an example, we will co
Page 36 and 37: We get the names of the features fo
Page 38 and 39: Chapter 1 Two rules are near the to
Page 40 and 41: Chapter 1 The scikit-learn library
Page 42 and 43: We then iterate over all the sample
Page 44 and 45: Chapter 1 Overfitting is the proble
Page 46: Chapter 1 Summary In this chapter,
Page 49 and 50: Classifying with scikit-learn Estim
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Classifying with scikit-learn Estim
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Predicting Sports Winners with Deci
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Recommending Movies Using Affinity
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Chapter 4 The classic algorithm for
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Chapter 4 When loading the file, we
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Chapter 4 We will sample our datase
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Chapter 4 Implementation On the fir
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Chapter 4 We want to break out the
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The process starts by creating dict
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movie_name_data.columns = ["MovieID
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To do this, we will compute the tes
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Chapter 4 - Train Confidence: 1.000
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Extracting Features with Transforme
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Chapter 5 Thought should always be
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Chapter 5 Other features describe a
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Chapter 5 Similarly, we can convert
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Chapter 5 [18, 19, 20], [21, 22, 23
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Chapter 5 Next, we create our trans
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Chapter 5 This returns a different
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Also, we want to set the final colu
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Chapter 5 The downside to transform
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Chapter 5 A transformer is akin to
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We can then create an instance of t
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Chapter 5 Putting it all together N
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Social Media Insight Using Naive Ba
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Discovering Accounts to Follow Usin
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Chapter 7 Next, we will need a list
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Chapter 7 Make sure the filename is
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Chapter 7 cursor = results['next_cu
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Chapter 7 Next, we are going to rem
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Chapter 7 Creating a graph Now, we
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Chapter 7 As you can see, it is ver
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Chapter 7 Next, we will only add th
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Chapter 7 The difference in this gr
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Chapter 7 We can graph the entire s
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Chapter 7 Optimizing criteria Our a
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Chapter 7 Next, we need to get the
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• method='nelder-mead': This is u
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Beating CAPTCHAs with Neural Networ
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Chapter 8 The red lines indicate th
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Chapter 8 The combination of an app
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Chapter 8 Next we set the font of t
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Chapter 8 We can then extract the s
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Chapter 8 Our targets are integer v
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Chapter 8 Then we iterate over our
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Chapter 8 From these predictions, w
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Chapter 8 This code correctly predi
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The result is shown in the next gra
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Chapter 8 However, it isn't very go
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Chapter 8 Summary In this chapter,
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Authorship Attribution Attributing
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Authorship Attribution If we cannot
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Authorship Attribution After taking
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Authorship Attribution This dataset
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Authorship Attribution "instead", "
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Authorship Attribution Support vect
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Authorship Attribution Kernels When
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Authorship Attribution We can reuse
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Authorship Attribution With our dat
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Authorship Attribution We then reco
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Authorship Attribution If it doesn'
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Authorship Attribution Finally, we
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Clustering News Articles In most of
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Chapter 10 API Endpoints are the ac
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The token object is just a dictiona
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Chapter 10 We then create a list to
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Chapter 10 We are going to use MD5
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Chapter 10 Next, we develop the cod
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Chapter 10 We use clustering techni
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Chapter 10 The k-means algorithm is
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Chapter 10 We only fit the X matrix
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Chapter 10 We then print out the mo
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Chapter 10 Our function definition
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Chapter 10 The result from the prec
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Chapter 10 Implementation Putting a
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Chapter 10 Neural networks can also
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We then call the partial_fit functi
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Classifying Objects in Images Using
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Chapter 11 This dataset comes from
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You can change the image index to s
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Chapter 11 Each of these issues has
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Chapter 11 Using Theano, we can def
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Chapter 11 Building a neural networ
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Chapter 11 Finally, we create Thean
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Chapter 11 return [image,] return s
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Chapter 11 Next, we define how the
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Chapter 11 Getting your code to run
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Chapter 11 Setting up the environme
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This will unzip only one Coval.otf
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Chapter 11 First we create the laye
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Chapter 11 Finally, we set the verb
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Chapter 11 Summary In this chapter,
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Working with Big Data Big data What
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Working with Big Data Governments a
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Working with Big Data We start by c
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Working with Big Data The final ste
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Working with Big Data Getting the d
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Working with Big Data If we aren't
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Working with Big Data Before we sta
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Working with Big Data The first val
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Working with Big Data This gives us
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Working with Big Data Next, we crea
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Working with Big Data Then, make a
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Working with Big Data Left-click th
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Working with Big Data The result is
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Next Steps… Extending the IPython
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Next Steps… Chapter 3: Predicting
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Next Steps… Vowpal Wabbit http://
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Next Steps… Deeper networks These
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Next Steps… Real-time clusterings
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Next Steps… More resources Kaggle
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authorship, attributing 185-188 AWS
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feature extraction about 82 common
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NetworkX about 145 defining 303 URL
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scikit-learn package references 305
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Thank you for buying Learning Data
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Learning Python Data Visualization
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Learning Data Mining with Python

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