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Index
Cover Table of Contents 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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