最新章節(jié)

• Coursera
• Kaggle competitions
• More resources
• W.I.L.L
• Recommendation engine
• Pydoop

• Coursera 更新時(shí)間：2021-07-02 23:40:49
• Kaggle competitions
• More resources
• W.I.L.L
• Recommendation engine
• Pydoop
• Courses on Hadoop
• Working with Big Data
• Magenta
• Mahotas
• Classifying Objects in Images Using Deep Learning
• Real-time clusterings
• Temporal analysis
• Clustering Evaluation
• Clustering News Articles
• Local n-grams
• Blogs dataset
• Increasing the sample size
• Authorship Attribution
• Reinforcement learning
• Deeper networks
• Better (worse?) CAPTCHAs
• Beating CAPTCHAs with Neural Networks
• NetworkX
• More complex algorithms
• Discovering Accounts to Follow Using Graph Mining
• Natural language processing and part-of-speech tagging
• Spam detection
• Social Media Insight Using Naive Bayes
• word2vec
• Vowpal Wabbit
• Adding noise
• Extracting Features with Transformers
• Collaborative Filtering
• The Eclat algorithm
• New datasets
• Recommending Movies Using Affinity Analysis
• Research
• Dask
• More complex features
• Predicting Sports Winners with Decision Trees
• Automated Learning
• Comparing classifiers
• More complex pipelines
• Scalability with the nearest neighbor
• Classifying with scikit-learn Estimators
• More application ideas
• Other Evaluation Metrics
• More datasets
• Extending the Jupyter Notebook
• Scikit-learn tutorials
• Getting Started with Data Mining
• Next Steps...
• Summary
• Training on Amazon's EMR infrastructure
• Putting it all together
• Training Naive Bayes
• Extracting the blog posts
• The mrjob package
• Naive Bayes prediction
• Getting the data
• Applying MapReduce
• Hadoop MapReduce
• A word count example
• The intuition behind MapReduce
• MapReduce
• Applications of big data
• Big data
• Working with Big Data
• Summary
• Putting it all together
• Creating the neural network
• Getting the data
• Application
• Setting up the environment
• Running our code on a GPU
• When to use GPUs for computation
• GPU optimization
• Convolutional Neural Networks
• Using Keras
• An Introduction to TensorFlow
• Implementing deep neural networks
• Intuition
• Deep neural networks
• Application scenario
• Use cases
• Object classification
• Object Detection in Images using Deep Neural Networks
• Summary
• Implementation
• Online learning
• Implementation
• How it works
• Evidence accumulation
• Clustering ensembles
• Using clustering algorithms as transformers
• Extracting topic information from clusters
• Evaluating the results
• The k-means algorithm
• Grouping news articles
• Extracting the content
• Finding the stories in arbitrary websites
• Extracting text from arbitrary websites
• Getting the data
• Reddit as a data source
• Using a web API to get data
• Trending topic discovery
• Clustering News Articles
• Summary
• Evaluation
• Putting it all together
• Creating a dataset loader
• Accessing the Enron dataset
• The Enron dataset
• Extracting character n-grams
• Character n-grams
• Kernels
• Classifying with SVMs
• Support Vector Machines
• Classifying with function words
• Counting function words
• Using function words
• Getting the data
• Authorship attribution
• Applications and use cases
• Attributing documents to authors
• Authorship Attribution
• Summary
• Putting it all together
• Ranking mechanisms for word similarity
• Improving accuracy using a dictionary
• Predicting words
• Back-propagation
• Training and classifying
• Creating a training dataset
• Splitting the image into individual letters
• Drawing basic CAPTCHAs
• Creating the dataset
• An introduction to neural networks
• Artificial neural networks
• Beating CAPTCHAs with Neural Networks
• Summary
• Optimizing criteria
• Connected components
• Finding subgraphs
• Creating a similarity graph
• Creating a graph
• Building the network
• Getting follower information from Twitter
• Classifying with an existing model
• Loading the dataset
• Follow Recommendations Using Graph Mining
• Summary
• Getting useful features from models
• Evaluation using the F1-score
• Putting it all together
• Converting dictionaries to a matrix
• Extracting word counts
• Applying of Naive Bayes
• How it works
• Naive Bayes algorithm
• Understanding Bayes' theorem
• Naive Bayes
• Other text features
• n-gram features
• Bag-of-words models
• Text transformers
• Creating a replicable dataset from Twitter
• Loading and classifying the dataset
• Downloading data from a social network
• Disambiguation
• Social Media Insight using Naive Bayes
• Summary
• Putting it all together
• Unit testing
• Implementing a Transformer
• The transformer API
• Creating your own transformer
• Principal Component Analysis
• Feature creation
• Selecting the best individual features
• Feature selection
• Creating good features
• Common feature patterns
• Representing reality in models
• Feature extraction
• Features and scikit-learn Transformers
• Summary
• Evaluating the association rules
• Extracting association rules
• Implementing the Apriori algorithm
• Looking into the basics of the Apriori algorithm
• Understanding the Apriori algorithm and its implementation
• Sparse data formats
• Loading with pandas
• Obtaining the dataset
• Dealing with the movie recommendation problem
• Overall methodology
• Algorithms for affinity analysis
• Affinity analysis
• Recommending Movies Using Affinity Analysis
• Summary
• Engineering new features
• Applying random forests
• Setting parameters in Random Forests
• How do ensembles work?
• Random forests
• Putting it all together
• Sports outcome prediction
• Using decision trees
• Parameters in decision trees
• Decision trees
• Extracting new features
• Cleaning up the dataset
• Using pandas to load the dataset
• Collecting the data
• Loading the dataset
• Predicting Sports Winners with Decision Trees
• Summary
• Pipelines
• Putting it all together
• Standard pre-processing
• Preprocessing
• Setting parameters
• Running the algorithm
• Moving towards a standard workflow
• Loading the dataset
• Distance metrics
• Nearest neighbors
• scikit-learn estimators
• Classifying with scikit-learn Estimators
• Summary
• Testing the algorithm
• Implementing the OneR algorithm
• Loading and preparing the dataset
• What is classification?
• A simple classification example
• Ranking to find the best rules
• Implementing a simple ranking of rules
• Downloading the example code
• Loading the dataset with NumPy
• Product recommendations
• What is affinity analysis?
• A simple affinity analysis example
• Installing scikit-learn
• Installing Jupyter Notebook
• Installing Python
• Using Python and the Jupyter Notebook
• Introducing data mining
• Getting Started with Data Mining
• Questions
• Piracy
• Errata
• Downloading the example code
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Customer Feedback
• www.PacktPub.com
• About the Reviewer
• About the Author
• Credits
• 版權(quán)信息
• 封面

• 封面
• 版權(quán)信息
• Credits
• About the Author
• About the Reviewer
• www.PacktPub.com
• Customer Feedback
• Preface
• What this book covers
• What you need for this book
• Who this book is for
• Conventions
• Reader feedback
• Customer support
• Downloading the example code
• Errata
• Piracy
• Questions
• Getting Started with Data Mining
• Introducing data mining
• Using Python and the Jupyter Notebook
• Installing Python
• Installing Jupyter Notebook
• Installing scikit-learn
• A simple affinity analysis example
• What is affinity analysis?
• Product recommendations
• Loading the dataset with NumPy
• Downloading the example code
• Implementing a simple ranking of rules
• Ranking to find the best rules
• A simple classification example
• What is classification?
• Loading and preparing the dataset
• Implementing the OneR algorithm
• Testing the algorithm
• Summary
• Classifying with scikit-learn Estimators
• scikit-learn estimators
• Nearest neighbors
• Distance metrics
• Loading the dataset
• Moving towards a standard workflow
• Running the algorithm
• Setting parameters
• Preprocessing
• Standard pre-processing
• Putting it all together
• Pipelines
• Summary
• Predicting Sports Winners with Decision Trees
• Loading the dataset
• Collecting the data
• Using pandas to load the dataset
• Cleaning up the dataset
• Extracting new features
• Decision trees
• Parameters in decision trees
• Using decision trees
• Sports outcome prediction
• Putting it all together
• Random forests
• How do ensembles work?
• Setting parameters in Random Forests
• Applying random forests
• Engineering new features
• Summary
• Recommending Movies Using Affinity Analysis
• Affinity analysis
• Algorithms for affinity analysis
• Overall methodology
• Dealing with the movie recommendation problem
• Obtaining the dataset
• Loading with pandas
• Sparse data formats
• Understanding the Apriori algorithm and its implementation
• Looking into the basics of the Apriori algorithm
• Implementing the Apriori algorithm
• Extracting association rules
• Evaluating the association rules
• Summary
• Features and scikit-learn Transformers
• Feature extraction
• Representing reality in models
• Common feature patterns
• Creating good features
• Feature selection
• Selecting the best individual features
• Feature creation
• Principal Component Analysis
• Creating your own transformer
• The transformer API
• Implementing a Transformer
• Unit testing
• Putting it all together
• Summary
• Social Media Insight using Naive Bayes
• Disambiguation
• Downloading data from a social network
• Loading and classifying the dataset
• Creating a replicable dataset from Twitter
• Text transformers
• Bag-of-words models
• n-gram features
• Other text features
• Naive Bayes
• Understanding Bayes' theorem
• Naive Bayes algorithm
• How it works
• Applying of Naive Bayes
• Extracting word counts
• Converting dictionaries to a matrix
• Putting it all together
• Evaluation using the F1-score
• Getting useful features from models
• Summary
• Follow Recommendations Using Graph Mining
• Loading the dataset
• Classifying with an existing model
• Getting follower information from Twitter
• Building the network
• Creating a graph
• Creating a similarity graph
• Finding subgraphs
• Connected components
• Optimizing criteria
• Summary
• Beating CAPTCHAs with Neural Networks
• Artificial neural networks
• An introduction to neural networks
• Creating the dataset
• Drawing basic CAPTCHAs
• Splitting the image into individual letters
• Creating a training dataset
• Training and classifying
• Back-propagation
• Predicting words
• Improving accuracy using a dictionary
• Ranking mechanisms for word similarity
• Putting it all together
• Summary
• Authorship Attribution
• Attributing documents to authors
• Applications and use cases
• Authorship attribution
• Getting the data
• Using function words
• Counting function words
• Classifying with function words
• Support Vector Machines
• Classifying with SVMs
• Kernels
• Character n-grams
• Extracting character n-grams
• The Enron dataset
• Accessing the Enron dataset
• Creating a dataset loader
• Putting it all together
• Evaluation
• Summary
• Clustering News Articles
• Trending topic discovery
• Using a web API to get data
• Reddit as a data source
• Getting the data
• Extracting text from arbitrary websites
• Finding the stories in arbitrary websites
• Extracting the content
• Grouping news articles
• The k-means algorithm
• Evaluating the results
• Extracting topic information from clusters
• Using clustering algorithms as transformers
• Clustering ensembles
• Evidence accumulation
• How it works
• Implementation
• Online learning
• Implementation
• Summary
• Object Detection in Images using Deep Neural Networks
• Object classification
• Use cases
• Application scenario
• Deep neural networks
• Intuition
• Implementing deep neural networks
• An Introduction to TensorFlow
• Using Keras
• Convolutional Neural Networks
• GPU optimization
• When to use GPUs for computation
• Running our code on a GPU
• Setting up the environment
• Application
• Getting the data
• Creating the neural network
• Putting it all together
• Summary
• Working with Big Data
• Big data
• Applications of big data
• MapReduce
• The intuition behind MapReduce
• A word count example
• Hadoop MapReduce
• Applying MapReduce
• Getting the data
• Naive Bayes prediction
• The mrjob package
• Extracting the blog posts
• Training Naive Bayes
• Putting it all together
• Training on Amazon's EMR infrastructure
• Summary
• Next Steps...
• Getting Started with Data Mining
• Scikit-learn tutorials
• Extending the Jupyter Notebook
• More datasets
• Other Evaluation Metrics
• More application ideas
• Classifying with scikit-learn Estimators
• Scalability with the nearest neighbor
• More complex pipelines
• Comparing classifiers
• Automated Learning
• Predicting Sports Winners with Decision Trees
• More complex features
• Dask
• Research
• Recommending Movies Using Affinity Analysis
• New datasets
• The Eclat algorithm
• Collaborative Filtering
• Extracting Features with Transformers
• Adding noise
• Vowpal Wabbit
• word2vec
• Social Media Insight Using Naive Bayes
• Spam detection
• Natural language processing and part-of-speech tagging
• Discovering Accounts to Follow Using Graph Mining
• More complex algorithms
• NetworkX
• Beating CAPTCHAs with Neural Networks
• Better (worse?) CAPTCHAs
• Deeper networks
• Reinforcement learning
• Authorship Attribution
• Increasing the sample size
• Blogs dataset
• Local n-grams
• Clustering News Articles
• Clustering Evaluation
• Temporal analysis
• Real-time clusterings
• Classifying Objects in Images Using Deep Learning
• Mahotas
• Magenta
• Working with Big Data
• Courses on Hadoop
• Pydoop
• Recommendation engine
• W.I.L.L
• More resources
• Kaggle competitions
• Coursera 更新時(shí)間：2021-07-02 23:40:49