Machine Learning For Dummies. John Paul Mueller

Processing Revising the State of the Art in NLP Understanding How Machines Read Using Scoring and Classification Chapter 19: Recommending Products and Movies Realizing the Revolution of E-Commerce Downloading Rating Data Catching the Limits of Behavioral Data Integrating Text and Behaviors Leveraging SVD

      10  Part 6: The Part of Tens Chapter 20: Ten Ways to Improve Your Machine Learning Models Studying Learning Curves Using Cross-Validation Correctly Choosing the Right Error or Score Metric Searching for the Best Hyper-Parameters Testing Multiple Models Averaging Models Stacking Models Applying Feature Engineering Selecting Features and Examples Looking for More Data Chapter 21: Ten Guidelines for Ethical Data Usage Obtaining Permission Using Sanitization Techniques Avoiding Data Inference Using Generalizations Correctly Shunning Discriminatory Practices Detecting Black Swans in Code Understanding the Process Considering the Consequences of an Action Balancing Decision Making Verifying a Data Source Chapter 22: Ten Machine Learning Packages to Master Gensim imbalanced-learn OpenCV SciPy SHAP Statsmodels Modin PyTorch Poetry Snorkel

      11  Index

      12  About the Authors

      13  Advertisement Page

      14  Connect with Dummies

      15  End User License Agreement

      List of Tables

      1 Chapter 1TABLE 1-1: Comparing Machine Learning to Statistics

      2 Chapter 5TABLE 5-1 Python Numeric Data TypesTABLE 5-2 Python Assignment OperatorsTABLE 5-3 Python Arithmetic, Unary, and Bitwise OperatorsTABLE 5-4 Python Relational and Logical OperatorsTABLE 5-5 Python Membership and Identity OperatorsTABLE 5-6 Python Operator Precedence

      List of Illustrations

      1 Chapter 2FIGURE 2-1: The five tribes will combine their efforts toward the master algori...

      2 Chapter 4FIGURE 4-1: Tell the wizard how to install Anaconda on your system.FIGURE 4-2: Configure the advanced installation options.FIGURE 4-3: Anaconda Navigator provides centralized access to every development...FIGURE 4-4: Jupyter Notebook provides an easy method to create machine learning...FIGURE 4-5: New folders will appear with a name of Untitled Folder.FIGURE 4-6: A notebook contains cells that you use to hold code.FIGURE 4-7: Notebook uses cells to store your code.FIGURE 4-8: The files that you want to add to the repository appear as part of ...FIGURE 4-9: The read_df object contains the loaded dataset as a dataframe.

      3 Chapter 6FIGURE 6-1: Using Colab commands makes configuring your Notebook easy.FIGURE 6-2: The Settings dialog box helps you configure the Colab IDE.FIGURE 6-3: Customize shortcut keys for speed of access to commands.FIGURE 6-4: Colab lets you compare two files to see how they differ.FIGURE 6-5: Follow the prompts to create your Google account.FIGURE 6-6: The sign-in page gives you access to all the general features, incl...FIGURE 6-7: Create a new Python 3 Notebook using the same techniques as normal.FIGURE 6-8: Use this dialog box to open existing notebooks.FIGURE 6-9: When using GitHub, you must provide the location of the source code...FIGURE 6-10: Your output may differ from the book's output when using Colab.FIGURE 6-11: Colab maintains a history of the revisions for your project.FIGURE 6-12: Using GitHub means storing your data in a repository.FIGURE 6-13: Use Gists to store individual files or other resources.FIGURE 6-14: Colab code cells contain a few extras not found in Notebook.FIGURE 6-15: Use Cell panes to keep key cells easily available as needed.FIGURE 6-16: Colab code cells contain a few extras not found in Notebook.FIGURE 6-17: Use the GUI to make formatting your text easier.FIGURE 6-18: The table of contents makes Notebook information more accessible.FIGURE 6-19: Hardware acceleration speeds code execution.FIGURE 6-20: The notebook information includes both size and settings.FIGURE 6-21: Colab tracks which code you execute and in what order.FIGURE 6-22: Send a