最新章節

• Summary
• Attribution errors
• Data pollution
• Auditing selection bias issues
• Selection bias
• Seasonal effects

• Summary 更新時間：2021-07-15 17:16:18
• Attribution errors
• Data pollution
• Auditing selection bias issues
• Selection bias
• Seasonal effects
• Novelty effects
• A/B test gotchas
• Determining how long to run an experiment for
• A/A testing
• Sample size increased seven-digits
• Sample size increased to six-digits
• Does the sample size make a difference?
• When there's no real difference between the two groups
• Running A/B test on some experimental data
• Measuring t-statistics and p-values using Python
• The p-value
• The t-statistic or t-test
• T-test and p-value
• Variance is your enemy
• How to attribute conversions
• Measuring conversion for A/B testing
• A/B tests
• A/B testing concepts
• Testing and Experimental Design
• Summary
• Implementing linear regression
• How Spark 2.0 MLlib works
• Using the Spark 2.0 DataFrame API for MLlib
• Running the algorithm
• Using the Wikipedia search engine algorithm
• Computing the TF-IDF score
• Creating and transforming a HashingTF object
• Creating the initial RDD
• Import statements
• Searching wikipedia with Spark MLlib
• Using TF- IDF
• TF-IDF in practice
• TF-IDF
• Running the code
• Within set sum of squared errors (WSSSE)
• K-Means Clustering in Spark
• Running the script
• Creating a test candidate and building our decision tree
• Importing and cleaning our data
• Creating the SparkContext
• Exploring decision trees code
• Decision Trees in Spark with MLlib
• Rating data type
• LabeledPoint data type
• The vector data type
• Special MLlib data types
• Some MLlib Capabilities
• Introducing MLlib
• Actions
• Using map()
• Transformations
• RDD operations
• More ways to create RDDs
• Loading an RDD from a text file
• Creating an RDD using a Python list
• Creating RDDs
• The SparkContext object
• Spark and Resilient Distributed Datasets (RDD)
• Python versus Scala for Spark
• Components of Spark
• It's not difficult
• It's young
• It's fast
• It's scalable
• Spark introduction
• Installing Spark
• Installing the Java Development Kit
• Installing Spark on other operating systems
• Installing Spark on Windows
• Installing Spark
• Apache Spark - Machine Learning on Big Data
• Summary
• Activity for outliers
• Dealing with outliers
• Detecting outliers
• Normalizing numerical data
• Activity for web log data
• Modification four - applying website-specific filters
• Filtering the activity of spiders/robots
• Modification three - checking the user agents
• Modification two - filtering post requests
• Modification one - filtering the request field
• Applying a regular expression on the web log
• Cleaning web log data
• Data cleaning and normalisation
• Example of k-fold cross-validation using scikit-learn
• K-fold cross-validation to avoid overfitting
• Bias/variance trade-off
• Dealing with Real-World Data
• Summary
• Dynamic programming
• Markov decision process
• Fancy words
• The better way
• The simple approach
• The exploration problem
• Q-learning
• Reinforcement learning
• ETL versus ELT
• Data warehousing overview
• Activity
• A PCA example with the Iris dataset
• Principal component analysis
• Dimensionality reduction
• Dimensionality reduction and principal component analysis
• Activity
• Using KNN to predict a rating for a movie
• K-nearest neighbors - concepts
• More Data Mining and Machine Learning Techniques
• Summary
• Improving the recommendation results
• Removing entries with the drop command
• Using the groupby command to combine rows
• Understanding movie recommendations with an example
• Making movie recommendations to people
• Improving the results of movie similarities
• The corrwith function
• Understanding the code
• Finding movie similarities
• Collaborative filtering using Python
• How item-based collaborative filtering works?
• Understanding item-based collaborative filtering
• Item-based collaborative filtering
• Limitations of user-based collaborative filtering
• User-based collaborative filtering
• What are recommender systems?
• Recommender Systems
• Summary
• Activity
• Using SVM to cluster people by using scikit-learn
• Support vector machine overview
• Ensemble learning
• Activity
• Ensemble learning – Using a random forest
• Decision trees - Predicting hiring decisions using Python
• Random forests technique
• Walking through a decision tree
• Decision tree example
• Decision trees - Concepts
• Measuring entropy
• Activity
• Clustering people based on income and age
• Limitations to k-means clustering
• K-Means clustering
• Activity
• Implementing a spam classifier with Na?ve Bayes
• Bayesian methods - Concepts
• Activity
• Using train/test to prevent overfitting of a polynomial regression
• K-fold cross validation
• Evaluating supervised learning
• Supervised learning
• Unsupervised learning
• Machine learning and train/test
• Machine Learning with Python
• Summary
• Multi-level models
• Activity for multivariate regression
• Multivariate regression using Python
• Multivariate regression and predicting car prices
• Activity for polynomial regression
• Computing the r-squared error
• Implementing polynomial regression using NumPy
• Polynomial regression
• Activity for linear regression
• Computing linear regression and r-squared using Python
• Interpreting r-squared
• Computing r-squared
• The co-efficient of determination or r-squared
• The gradient descent technique
• The ordinary least squares technique
• Linear regression
• Predictive Models
• Summary
• Bayes' theorem
• My assignment solution
• Conditional probability assignment
• Conditional probability exercises in Python
• Conditional probability
• Correlation activity
• Computing correlation – The NumPy way
• Computing correlation – The hard way
• Computing covariance and correlation in Python
• Correlation
• Measuring covariance
• Defining the concepts
• Covariance and correlation
• Try it yourself
• Generating box-and-whisker plots
• Generating histograms
• Generating scatter plots
• Generating bar charts
• Generating pie charts
• A fun example
• Labeling axes and adding a legend
• Changing line types and colors
• Adding a grid
• Adjusting the axes
• Saving graphs as images
• Generating multiple plots on one graph
• A crash course in Matplotlib
• Matplotlib and Advanced Probability Concepts
• Summary
• Computing moments in Python
• Moments
• Computing percentiles in Python
• Quartiles
• Percentiles
• Percentiles and moments
• Poisson probability mass function
• Binomial probability mass function
• The exponential probability distribution or Power law
• Normal or Gaussian distribution
• Uniform distribution
• Types of data distributions
• Probability mass functions
• Probability density functions
• The probability density function and probability mass functions
• Probability density function and probability mass function
• Try it yourself
• Using Python to compute standard deviation and variance
• Analyzing standard deviation and variance on a histogram
• The Mathematical explanation
• Population variance versus sample variance
• Identifying outliers with standard deviation
• Standard deviation
• Measuring variance
• Variance
• Standard deviation and variance
• Some exercises
• Calculating mode using the SciPy package
• Analyzing the effect of outliers
• Calculating median using the NumPy package
• Visualizing data using matplotlib
• Calculating mean using the NumPy package
• Using mean median and mode in Python
• Mode
• The factor of outliers
• Median
• Mean
• Mean median and mode
• Ordinal data
• Categorical data
• Continuous data
• Discrete data
• Numerical data
• Types of data
• Statistics and Probability Refresher and Python Practice
• Summary
• Using the Canopy IDE
• Running Python scripts in command prompt
• More options than just the IPython/Jupyter Notebook
• Running Python scripts
• Exploring activity
• The while loop
• Looping
• The if-else loop
• The if statement
• Understanding boolean expressions
• Lambda functions - functional programming
• Functions in Python
• Python basics - Part 2
• Iterating through entries
• Dictionaries
• List of tuples
• Dereferencing an element
• Tuples
• Reverse sort
• The sort function
• Dereferencing a single element
• Complex data structures
• The append function
• Adding list to list
• Negative syntax
• Post colon
• Pre colon
• Experimenting with lists
• Data structures
• Importing modules
• Understanding Python code
• Python basics - Part 1
• Using and understanding IPython (Jupyter) Notebooks
• If you occasionally get problems opening your IPNYB files
• Giving the installation a test run
• Installing Enthought Canopy
• Getting Started
• Questions
• Piracy
• Errata
• Downloading the color images of this book
• Downloading the example code
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• Preface
• Customer Feedback
• Why subscribe?
• www.PacktPub.com
• About the Author
• Credits
• Hands-On Data Science and Python Machine Learning
• Copyright
• Title Page
• cover

• cover
• Title Page
• Copyright
• Hands-On Data Science and Python Machine Learning
• Credits
• About the Author
• www.PacktPub.com
• Why subscribe?
• Customer Feedback
• Preface
• Who this book is for
• Conventions
• Reader feedback
• Customer support
• Downloading the example code
• Downloading the color images of this book
• Errata
• Piracy
• Questions
• Getting Started
• Installing Enthought Canopy
• Giving the installation a test run
• If you occasionally get problems opening your IPNYB files
• Using and understanding IPython (Jupyter) Notebooks
• Python basics - Part 1
• Understanding Python code
• Importing modules
• Data structures
• Experimenting with lists
• Pre colon
• Post colon
• Negative syntax
• Adding list to list
• The append function
• Complex data structures
• Dereferencing a single element
• The sort function
• Reverse sort
• Tuples
• Dereferencing an element
• List of tuples
• Dictionaries
• Iterating through entries
• Python basics - Part 2
• Functions in Python
• Lambda functions - functional programming
• Understanding boolean expressions
• The if statement
• The if-else loop
• Looping
• The while loop
• Exploring activity
• Running Python scripts
• More options than just the IPython/Jupyter Notebook
• Running Python scripts in command prompt
• Using the Canopy IDE
• Summary
• Statistics and Probability Refresher and Python Practice
• Types of data
• Numerical data
• Discrete data
• Continuous data
• Categorical data
• Ordinal data
• Mean median and mode
• Mean
• Median
• The factor of outliers
• Mode
• Using mean median and mode in Python
• Calculating mean using the NumPy package
• Visualizing data using matplotlib
• Calculating median using the NumPy package
• Analyzing the effect of outliers
• Calculating mode using the SciPy package
• Some exercises
• Standard deviation and variance
• Variance
• Measuring variance
• Standard deviation
• Identifying outliers with standard deviation
• Population variance versus sample variance
• The Mathematical explanation
• Analyzing standard deviation and variance on a histogram
• Using Python to compute standard deviation and variance
• Try it yourself
• Probability density function and probability mass function
• The probability density function and probability mass functions
• Probability density functions
• Probability mass functions
• Types of data distributions
• Uniform distribution
• Normal or Gaussian distribution
• The exponential probability distribution or Power law
• Binomial probability mass function
• Poisson probability mass function
• Percentiles and moments
• Percentiles
• Quartiles
• Computing percentiles in Python
• Moments
• Computing moments in Python
• Summary
• Matplotlib and Advanced Probability Concepts
• A crash course in Matplotlib
• Generating multiple plots on one graph
• Saving graphs as images
• Adjusting the axes
• Adding a grid
• Changing line types and colors
• Labeling axes and adding a legend
• A fun example
• Generating pie charts
• Generating bar charts
• Generating scatter plots
• Generating histograms
• Generating box-and-whisker plots
• Try it yourself
• Covariance and correlation
• Defining the concepts
• Measuring covariance
• Correlation
• Computing covariance and correlation in Python
• Computing correlation – The hard way
• Computing correlation – The NumPy way
• Correlation activity
• Conditional probability
• Conditional probability exercises in Python
• Conditional probability assignment
• My assignment solution
• Bayes' theorem
• Summary
• Predictive Models
• Linear regression
• The ordinary least squares technique
• The gradient descent technique
• The co-efficient of determination or r-squared
• Computing r-squared
• Interpreting r-squared
• Computing linear regression and r-squared using Python
• Activity for linear regression
• Polynomial regression
• Implementing polynomial regression using NumPy
• Computing the r-squared error
• Activity for polynomial regression
• Multivariate regression and predicting car prices
• Multivariate regression using Python
• Activity for multivariate regression
• Multi-level models
• Summary
• Machine Learning with Python
• Machine learning and train/test
• Unsupervised learning
• Supervised learning
• Evaluating supervised learning
• K-fold cross validation
• Using train/test to prevent overfitting of a polynomial regression
• Activity
• Bayesian methods - Concepts
• Implementing a spam classifier with Na?ve Bayes
• Activity
• K-Means clustering
• Limitations to k-means clustering
• Clustering people based on income and age
• Activity
• Measuring entropy
• Decision trees - Concepts
• Decision tree example
• Walking through a decision tree
• Random forests technique
• Decision trees - Predicting hiring decisions using Python
• Ensemble learning – Using a random forest
• Activity
• Ensemble learning
• Support vector machine overview
• Using SVM to cluster people by using scikit-learn
• Activity
• Summary
• Recommender Systems
• What are recommender systems?
• User-based collaborative filtering
• Limitations of user-based collaborative filtering
• Item-based collaborative filtering
• Understanding item-based collaborative filtering
• How item-based collaborative filtering works?
• Collaborative filtering using Python
• Finding movie similarities
• Understanding the code
• The corrwith function
• Improving the results of movie similarities
• Making movie recommendations to people
• Understanding movie recommendations with an example
• Using the groupby command to combine rows
• Removing entries with the drop command
• Improving the recommendation results
• Summary
• More Data Mining and Machine Learning Techniques
• K-nearest neighbors - concepts
• Using KNN to predict a rating for a movie
• Activity
• Dimensionality reduction and principal component analysis
• Dimensionality reduction
• Principal component analysis
• A PCA example with the Iris dataset
• Activity
• Data warehousing overview
• ETL versus ELT
• Reinforcement learning
• Q-learning
• The exploration problem
• The simple approach
• The better way
• Fancy words
• Markov decision process
• Dynamic programming
• Summary
• Dealing with Real-World Data
• Bias/variance trade-off
• K-fold cross-validation to avoid overfitting
• Example of k-fold cross-validation using scikit-learn
• Data cleaning and normalisation
• Cleaning web log data
• Applying a regular expression on the web log
• Modification one - filtering the request field
• Modification two - filtering post requests
• Modification three - checking the user agents
• Filtering the activity of spiders/robots
• Modification four - applying website-specific filters
• Activity for web log data
• Normalizing numerical data
• Detecting outliers
• Dealing with outliers
• Activity for outliers
• Summary
• Apache Spark - Machine Learning on Big Data
• Installing Spark
• Installing Spark on Windows
• Installing Spark on other operating systems
• Installing the Java Development Kit
• Installing Spark
• Spark introduction
• It's scalable
• It's fast
• It's young
• It's not difficult
• Components of Spark
• Python versus Scala for Spark
• Spark and Resilient Distributed Datasets (RDD)
• The SparkContext object
• Creating RDDs
• Creating an RDD using a Python list
• Loading an RDD from a text file
• More ways to create RDDs
• RDD operations
• Transformations
• Using map()
• Actions
• Introducing MLlib
• Some MLlib Capabilities
• Special MLlib data types
• The vector data type
• LabeledPoint data type
• Rating data type
• Decision Trees in Spark with MLlib
• Exploring decision trees code
• Creating the SparkContext
• Importing and cleaning our data
• Creating a test candidate and building our decision tree
• Running the script
• K-Means Clustering in Spark
• Within set sum of squared errors (WSSSE)
• Running the code
• TF-IDF
• TF-IDF in practice
• Using TF- IDF
• Searching wikipedia with Spark MLlib
• Import statements
• Creating the initial RDD
• Creating and transforming a HashingTF object
• Computing the TF-IDF score
• Using the Wikipedia search engine algorithm
• Running the algorithm
• Using the Spark 2.0 DataFrame API for MLlib
• How Spark 2.0 MLlib works
• Implementing linear regression
• Summary
• Testing and Experimental Design
• A/B testing concepts
• A/B tests
• Measuring conversion for A/B testing
• How to attribute conversions
• Variance is your enemy
• T-test and p-value
• The t-statistic or t-test
• The p-value
• Measuring t-statistics and p-values using Python
• Running A/B test on some experimental data
• When there's no real difference between the two groups
• Does the sample size make a difference?
• Sample size increased to six-digits
• Sample size increased seven-digits
• A/A testing
• Determining how long to run an experiment for
• A/B test gotchas
• Novelty effects
• Seasonal effects
• Selection bias
• Auditing selection bias issues
• Data pollution
• Attribution errors
• Summary 更新時間：2021-07-15 17:16:18