舉報 
會員
Python：Advanced Predictive Analytics
Ashish Kumar Joseph Babcock 著
更新時間：2021-07-02 20:09:52
開會員，本書免費讀 >
最新章節：
Index
Thisbookisdesignedforbusinessanalysts,BIanalysts,datascientists,orjuniorleveldataanalystswhoarereadytomoveonfromaconceptualunderstandingofadvancedanalyticsandbecomeanexpertindesigningandbuildingadvancedanalyticssolutionsusingPython.IfyouarefamiliarwithcodinginPython(orsomeotherprogramming/statistical/scriptinglanguage)buthaveneverusedorreadaboutpredictiveanalyticsalgorithms,thisbookwillalsohelpyou.
最新章節
- Index
- Bibliography
- Summary
- Guidelines for communication
- Iterating on models through A/B testing
- Checking the health of models with diagnostics
品牌：中圖公司
上架時間：2021-07-02 18:27:41
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- Index 更新時間：2021-07-02 20:09:52
- Bibliography
- Summary
- Guidelines for communication
- Iterating on models through A/B testing
- Checking the health of models with diagnostics
- Chapter 9. Reporting and Testing – Iterating on Analytic Systems
- Summary
- Case study – logistic regression service
- Persisting information with database systems
- Server – the web traffic controller
- Clients and making requests
- The architecture of a prediction service
- Chapter 8. Sharing Models with Prediction Services
- Summary
- The TensorFlow library and digit recognition
- Learning patterns with neural networks
- Chapter 7. Learning from the Bottom Up – Deep Networks and Unsupervised Features
- Summary
- Case Study: Training a Recommender System in PySpark
- Images
- Principal component analysis
- Working with textual data
- Chapter 6. Words and Pixels – Working with Unstructured Data
- Summary
- Case study: fitting classifier models in pyspark
- Comparing classification methods
- Separating Nonlinear boundaries with Support vector machines
- Evaluating classification models
- Fitting the model
- Logistic regression
- Chapter 5. Putting Data in its Place – Classification Methods and Analysis
- Summary
- Scaling out with PySpark – predicting year of song release
- Tree methods
- Linear regression
- Chapter 4. Connecting the Dots with Models – Regression Methods
- Summary
- Streaming clustering in Spark
- Agglomerative clustering
- k-medoids
- Affinity propagation – automatically choosing cluster numbers
- Similarity and distance metrics
- Chapter 3. Finding Patterns in the Noise – Clustering and Unsupervised Learning
- Summary
- Introduction to PySpark
- Working with geospatial data
- Time series analysis
- Exploring categorical and numerical data in IPython
- Chapter 2. Exploratory Data Analysis and Visualization in Python
- Summary
- Case study: targeted e-mail campaigns
- Case study: sentiment analysis of social media feeds
- Designing an advanced analytic solution
- Chapter 1. From Data to Decisions – Getting Started with Analytic Applications
- Part 2. Module 2
- Appendix A. A List of Links
- Summary
- Best practices for business contexts
- Best practices for statistics
- Best practices for algorithms
- Best practices for data handling
- Best practices for coding
- Chapter 9. Best Practices for Predictive Modelling
- Summary
- Understanding and implementing random forests
- Understanding and implementing regression trees
- Implementing a decision tree with scikit-learn
- Understanding the mathematics behind decision trees
- Introducing decision trees
- Chapter 8. Trees and Random Forests with Python
- Summary
- Fine-tuning the clustering
- Implementing clustering using Python
- Mathematics behind clustering
- Introduction to clustering – what why and how?
- Chapter 7. Clustering with Python
- Summary
- Model validation
- Model validation and evaluation
- Implementing logistic regression with Python
- Understanding the math behind logistic regression
- Linear regression versus logistic regression
- Chapter 6. Logistic Regression with Python
- Summary
- Handling other issues in linear regression
- Model validation
- Implementing linear regression with Python
- Making sense of result parameters
- Understanding the maths behind linear regression
- Chapter 5. Linear Regression with Python
- Summary
- Correlation
- Chi-square tests
- Hypothesis testing
- Random sampling and the central limit theorem
- Chapter 4. Statistical Concepts for Predictive Modelling
- Summary
- Merging/joining datasets
- Concatenating and appending data
- Random sampling – splitting a dataset in training and testing datasets
- Grouping the data – aggregation filtering and transformation
- Generating random numbers and their usage
- Subsetting a dataset
- Chapter 3. Data Wrangling
- Summary
- Visualizing a dataset by basic plotting
- Creating dummy variables
- Handling missing values
- Basics – summary dimensions and structure
- Case 4 – miscellaneous cases
- Case 3 – reading data from a URL
- Case 2 – reading a dataset using the open method of Python
- Use cases of the read_csv method
- The read_csv method
- Various methods of importing data in Python
- Reading the data – variations and examples
- Chapter 2. Data Cleaning
- Summary
- IDEs for Python
- Python and its packages for predictive modelling
- Python and its packages – download and installation
- Applications and examples of predictive modelling
- Introducing predictive modelling
- Chapter 1. Getting Started with Predictive Modelling
- Part 1. Module 1
- Preface
- Credits
- 版權信息
- 封面
- 封面
- 版權信息
- Credits
- Preface
- Part 1. Module 1
- Chapter 1. Getting Started with Predictive Modelling
- Introducing predictive modelling
- Applications and examples of predictive modelling
- Python and its packages – download and installation
- Python and its packages for predictive modelling
- IDEs for Python
- Summary
- Chapter 2. Data Cleaning
- Reading the data – variations and examples
- Various methods of importing data in Python
- The read_csv method
- Use cases of the read_csv method
- Case 2 – reading a dataset using the open method of Python
- Case 3 – reading data from a URL
- Case 4 – miscellaneous cases
- Basics – summary dimensions and structure
- Handling missing values
- Creating dummy variables
- Visualizing a dataset by basic plotting
- Summary
- Chapter 3. Data Wrangling
- Subsetting a dataset
- Generating random numbers and their usage
- Grouping the data – aggregation filtering and transformation
- Random sampling – splitting a dataset in training and testing datasets
- Concatenating and appending data
- Merging/joining datasets
- Summary
- Chapter 4. Statistical Concepts for Predictive Modelling
- Random sampling and the central limit theorem
- Hypothesis testing
- Chi-square tests
- Correlation
- Summary
- Chapter 5. Linear Regression with Python
- Understanding the maths behind linear regression
- Making sense of result parameters
- Implementing linear regression with Python
- Model validation
- Handling other issues in linear regression
- Summary
- Chapter 6. Logistic Regression with Python
- Linear regression versus logistic regression
- Understanding the math behind logistic regression
- Implementing logistic regression with Python
- Model validation and evaluation
- Model validation
- Summary
- Chapter 7. Clustering with Python
- Introduction to clustering – what why and how?
- Mathematics behind clustering
- Implementing clustering using Python
- Fine-tuning the clustering
- Summary
- Chapter 8. Trees and Random Forests with Python
- Introducing decision trees
- Understanding the mathematics behind decision trees
- Implementing a decision tree with scikit-learn
- Understanding and implementing regression trees
- Understanding and implementing random forests
- Summary
- Chapter 9. Best Practices for Predictive Modelling
- Best practices for coding
- Best practices for data handling
- Best practices for algorithms
- Best practices for statistics
- Best practices for business contexts
- Summary
- Appendix A. A List of Links
- Part 2. Module 2
- Chapter 1. From Data to Decisions – Getting Started with Analytic Applications
- Designing an advanced analytic solution
- Case study: sentiment analysis of social media feeds
- Case study: targeted e-mail campaigns
- Summary
- Chapter 2. Exploratory Data Analysis and Visualization in Python
- Exploring categorical and numerical data in IPython
- Time series analysis
- Working with geospatial data
- Introduction to PySpark
- Summary
- Chapter 3. Finding Patterns in the Noise – Clustering and Unsupervised Learning
- Similarity and distance metrics
- Affinity propagation – automatically choosing cluster numbers
- k-medoids
- Agglomerative clustering
- Streaming clustering in Spark
- Summary
- Chapter 4. Connecting the Dots with Models – Regression Methods
- Linear regression
- Tree methods
- Scaling out with PySpark – predicting year of song release
- Summary
- Chapter 5. Putting Data in its Place – Classification Methods and Analysis
- Logistic regression
- Fitting the model
- Evaluating classification models
- Separating Nonlinear boundaries with Support vector machines
- Comparing classification methods
- Case study: fitting classifier models in pyspark
- Summary
- Chapter 6. Words and Pixels – Working with Unstructured Data
- Working with textual data
- Principal component analysis
- Images
- Case Study: Training a Recommender System in PySpark
- Summary
- Chapter 7. Learning from the Bottom Up – Deep Networks and Unsupervised Features
- Learning patterns with neural networks
- The TensorFlow library and digit recognition
- Summary
- Chapter 8. Sharing Models with Prediction Services
- The architecture of a prediction service
- Clients and making requests
- Server – the web traffic controller
- Persisting information with database systems
- Case study – logistic regression service
- Summary
- Chapter 9. Reporting and Testing – Iterating on Analytic Systems
- Checking the health of models with diagnostics
- Iterating on models through A/B testing
- Guidelines for communication
- Summary
- Bibliography
- Index 更新時間：2021-07-02 20:09:52
