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Sebastian Raschka - Python: Real-World Data Science

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Sebastian Raschka Python: Real-World Data Science

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Appendix A. Reflect and Test Yourself! Answers
Module 2: Data Analysis
Chapter 1: Introducing Data Analysis and Libraries

Q1

Q2

Q3

Chapter 2: Object-oriented Design

Q1

Q2

Q3

Q4

Chapter 3: Data Analysis with pandas

Q1

Q2

Q3

Q4

Q5

Chapter 4: Data Visualization

Q1

Q2

Q3

Q4

Chapter 5: Time Series

Q1

Q2

Q3

Q4

Chapter 6: Interacting with Databases

Q1

Q2

Q3

Chapter 7: Data Analysis Application Examples

Q1

Q2

Module 3: Data Mining
Chapter 1: Getting Started with Data Mining

Q1

Q2

Q3

Chapter 2: Classifying with scikit-learn Estimators

Q1

Q2

Q3

Q4

Chapter 3: Predicting Sports Winners with Decision Trees

Q1

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Chapter 4: Recommending Movies Using Affinity Analysis

Q1

Chapter 5: Extracting Features with Transformers

Q1

Q2

Q3

Chapter 6: Social Media Insight Using Naive Bayes

Q1

Q2

Chapter 7: Discovering Accounts to Follow Using Graph Mining

Q1

Chapter 8: Beating CAPTCHAs with Neural Networks

Q1

Chapter 9: Authorship Attribution

Q1

Q2

Chapter 10: Clustering News Articles

Q1

Q2

Q3

Chapter 11: Classifying Objects in Images Using Deep Learning

Q1

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Chapter 12: Working with Big Data

Q1

Q2

Q3

Module 4: Machine Learning
Chapter 1: Giving Computers the Ability to Learn from Data

Q1

Q2

Chapter 2: Training Machine Learning

Q1

Chapter 3: A Tour of Machine Learning Classifiers Using scikit-learn

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Chapter 4: Building Good Training Sets Data Preprocessing

Q1

Chapter 5: Compressing Data via Dimensionality Reduction

Q1

Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning

Q1

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Chapter 7: Combining Different Models for Ensemble Learning

Q1

Chapter 8: Predicting Continuous Target Variables with Regression Analysis

Q1

Appendix B. Bibliography

This course is a blend of text and quizzes, all packaged up keeping your journey in mind. It includes content from the following Packt products:

  • Python 3 Object-oriented Programming , Second Edition , Dusty Phillips
  • Learning Python , Fabrizio Romano
  • Getting Started with Python Data Analysis , Phuong Vo.T.H and Martin Czygan
  • Learning Data Mining with Python , Robert Layton
  • Python Machine Learning , Sebastian Raschka
Index
A
  • absolute imports
    • about /
  • abstract base classes (ABCs)
    • about /
    • using /
    • creating /
    • @classmethod /
  • abstract factory pattern
    • about /
  • abstraction
    • about /
    • examples /
    • defining /
  • abstract methods
    • about /
  • access control
    • about /
  • access keys
    • about /
  • accuracy
    • improving, dictionary used /
  • accuracy (ACC) /
  • activation function
    • about /
  • AdaBoost /
  • adaline
    • about /
  • adapter pattern
    • about /
    • Interface1 /
    • Interface2 /
    • Adapter class /
  • adaptive boosting
    • weak learners, leveraging via /
  • ADAptive LInear NEuron (Adaline) /
  • ADAptive LInear NEuron (ADALINE) /
  • adaptive linear neurons
    • about /
    • cost functions, minimizing with gradient descent /
    • implementing, in Python /
    • large scale machine-learning /
    • stochastic gradient descent /
  • Adult dataset
    • URL /
  • advanced Pandas use cases
    • for data analysis /
    • hierarchical indexing /
    • panel data /
  • Advertisements dataset
    • URL /
  • affinity analysis
    • example /
    • defining /
    • product recommendations /
    • dataset, loading with NumPy /
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