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
• Book 2: Interacting with Data Storage
  • 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
• Book 3: Manipulating Data Using Basic Algorithms
  • 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
• Book 4: Performing Advanced Data Manipulation
  • 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
• Book 5: Performing Data-Related Tasks
  • 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
• Book 6: Diagnosing and Fixing Errors
  • 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
• Index
• About the Authors
• Connect with Dummies
• End User License Agreement

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