Data Science Programming All-in-One For Dummies
4.690 kr.
Annað
- Höfundar: John Paul Mueller, Luca Massaron
- Útgáfa:1
- Útgáfudagur: 2019-12-09
- Hægt að prenta út 10 bls.
- Hægt að afrita 2 bls.
- Format:ePub
- ISBN 13: 9781119626145
- Print ISBN: 9781119626114
- ISBN 10: 1119626145
Efnisyfirlit
- Cover
- Introduction
- About This Book
- Foolish Assumptions
- Icons Used in This Book
- Beyond the Book
- Where to Go from Here
- Book 1: Defining Data Science
- Chapter 1: Considering the History and Uses of Data Science
- Considering the Elements of Data Science
- Defining the Role of Data in the World
- Creating the Data Science Pipeline
- Comparing Different Languages Used for Data Science
- Learning to Perform Data Science Tasks Fast
- Chapter 2: Placing Data Science within the Realm of AI
- Seeing the Data to Data Science Relationship
- Defining the Levels of AI
- Creating a Pipeline from Data to AI
- Chapter 3: Creating a Data Science Lab of Your Own
- Considering the Analysis Platform Options
- Choosing a Development Language
- Obtaining and Using Python
- Obtaining and Using R
- Presenting Frameworks
- Accessing the Downloadable Code
- Chapter 4: Considering Additional Packages and Libraries You Might Want
- Considering the Uses for Third-Party Code
- Obtaining Useful Python Packages
- Locating Useful R Libraries
- Chapter 5: Leveraging a Deep Learning Framework
- Understanding Deep Learning Framework Usage
- Working with Low-End Frameworks
- Understanding TensorFlow
- Chapter 1: Considering the History and Uses of Data Science
- Chapter 1: Manipulating Raw Data
- Defining the Data Sources
- Considering the Data Forms
- Understanding the Need for Data Reliability
- Chapter 2: Using Functional Programming Techniques
- Defining Functional Programming
- Understanding Pure and Impure Languages
- Comparing the Functional Paradigm
- Using Python for Functional Programming Needs
- Understanding How Functional Data Works
- Working with Lists and Strings
- Employing Pattern Matching
- Working with Recursion
- Performing Functional Data Manipulation
- Chapter 3: Working with Scalars, Vectors, and Matrices
- Considering the Data Forms
- Defining Data Type through Scalars
- Creating Organized Data with Vectors
- Creating and Using Matrices
- Extending Analysis to Tensors
- Using Vectorization Effectively
- Selecting and Shaping Data
- Working with Trees
- Representing Relations in a Graph
- Chapter 4: Accessing Data in Files
- Understanding Flat File Data Sources
- Working with Positional Data Files
- Accessing Data in CSV Files
- Moving On to XML Files
- Considering Other Flat-File Data Sources
- Working with Nontext Data
- Downloading Online Datasets
- Chapter 5: Working with a Relational DBMS
- Considering RDBMS Issues
- Accessing the RDBMS Data
- Creating a Dataset
- Mixing RDBMS Products
- Chapter 6: Working with a NoSQL DMBS
- Considering the Ramifications of Hierarchical Data
- Accessing the Data
- Interacting with Data from NoSQL Databases
- Working with Dictionaries
- Developing Datasets from Hierarchical Data
- Processing Hierarchical Data into Other Forms
- Chapter 1: Working with Linear Regression
- Considering the History of Linear Regression
- Combining Variables
- Manipulating Categorical Variables
- Using Linear Regression to Guess Numbers
- Learning One Example at a Time
- Chapter 2: Moving Forward with Logistic Regression
- Considering the History of Logistic Regression
- Differentiating between Linear and Logistic Regression
- Using Logistic Regression to Guess Classes
- Switching to Probabilities
- Working through Multiclass Regression
- Chapter 3: Predicting Outcomes Using Bayes
- Understanding Bayes' Theorem
- Using Naïve Bayes for Predictions
- Working with Networked Bayes
- Considering the Use of Bayesian Linear Regression
- Considering the Use of Bayesian Logistic Regression
- Chapter 4: Learning with K-Nearest Neighbors
- Considering the History of K-Nearest Neighbors
- Learning Lazily with K-Nearest Neighbors
- Leveraging the Correct k Parameter
- Implementing KNN Regression
- Implementing KNN Classification
- Chapter 1: Leveraging Ensembles of Learners
- Leveraging Decision Trees
- Working with Almost Random Guesses
- Meeting Again with Gradient Descent
- Averaging Different Predictors
- Chapter 2: Building Deep Learning Models
- Discovering the Incredible Perceptron
- Hitting Complexity with Neural Networks
- Understanding More about Neural Networks
- Looking Under the Hood of Neural Networks
- Explaining Deep Learning Differences with Other Forms of AI
- Chapter 3: Recognizing Images with CNNs
- Beginning with Simple Image Recognition
- Understanding CNN Image Basics
- Moving to CNNs with Character Recognition
- Explaining How Convolutions Work
- Detecting Edges and Shapes from Images
- Chapter 4: Processing Text and Other Sequences
- Introducing Natural Language Processing
- Understanding How Machines Read
- Understanding Semantics Using Word Embeddings
- Using Scoring and Classification
- Chapter 1: Making Recommendations
- Realizing the Recommendation Revolution
- Downloading Rating Data
- Leveraging SVD
- Chapter 2: Performing Complex Classifications
- Using Image Classification Challenges
- Distinguishing Traffic Signs
- Chapter 3: Identifying Objects
- Distinguishing Classification Tasks
- Perceiving Objects in Their Surroundings
- Overcoming Adversarial Attacks on Deep Learning Applications
- Chapter 4: Analyzing Music and Video
- Learning to Imitate Art and Life
- Mimicking an Artist
- Moving toward GANs
- Chapter 5: Considering Other Task Types
- Processing Language in Texts
- Processing Time Series
- Chapter 6: Developing Impressive Charts and Plots
- Starting a Graph, Chart, or Plot
- Setting the Axis, Ticks, and Grids
- Defining the Line Appearance
- Using Labels, Annotations, and Legends
- Creating Scatterplots
- Plotting Time Series
- Plotting Geographical Data
- Visualizing Graphs
- Chapter 1: Locating Errors in Your Data
- Considering the Types of Data Errors
- Obtaining the Required Data
- Validating Your Data
- Manicuring the Data
- Dealing with Dates in Your Data
- Chapter 2: Considering Outrageous Outcomes
- Deciding What Outrageous Means
- Considering the Five Mistruths in Data
- Considering Detection of Outliers
- Examining a Simple Univariate Method
- Developing a Multivariate Approach
- Chapter 3: Dealing with Model Overfitting and Underfitting
- Understanding the Causes
- Determining the Sources of Overfitting and Underfitting
- Guessing the Right Features
- Chapter 4: Obtaining the Correct Output Presentation
- Considering the Meaning of Correct
- Determining a Presentation Type
- Choosing the Right Graph
- Working with External Data
- Chapter 5: Developing Consistent Strategies
- Standardizing Data Collection Techniques
- Using Reliable Sources
- Verifying Dynamic Data Sources
- Looking for New Data Collection Trends
- Weeding Old Data
- Considering the Need for Randomness
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- Gerð : 208
- Höfundur : 10683
- Útgáfuár : 2019
- Leyfi : 379