Table of Contents
1. Introduction to Machine Learning
   1.1 Introduction to machine learning
   1.2 Feature and model types
   1.3 Modeling workflow in scikit-learn
   1.4 Bias-variance tradeoff
   1.5 Machine learning ethics
2. Classification Models
   2.1 k-nearest neighbors
   2.2 Logistic regression
   2.3 Gaussian naive Bayes
   2.4 Discriminant analysis
3. Regression Models
3.1 Linear regression
3.2 Elastic net regression
3.3 k-nearest neighbors for regression
4. Model Evaluation
   4.1 Loss functions for classification
   4.2 Classification metrics
   4.3 Loss functions for regression
   4.4 Regression metrics
   4.5 Evaluating models with plots
5. Model Validation
   5.1 Cross-validation methods
   5.2 Cross-validation for model selection
   5.3 Cross-validation for model tuning
6. Model Improvement
   6.1 Data pre-processing
   6.2 Feature transformations
   6.3 Imputation techniques
7. Support Vector Machines
   7.1 Support vector classifiers
   7.2 Nonlinear support vector machines
   7.3 Support vector machines for regression
8. Decision Trees
  8.1 Decision trees for classification
 8.2 Decision tree algorithms
 8.3 Decision trees for regression
9. Ensemble Models
   9.1 Bootstrapping
   9.2 Bagging
   9.3 Random forests
   9.4 Boosting
10. Deep Learning
   10.1 Neural networks
   10.2 Training neural networks
   10.3 Gradient descent
   10.4 Deep Learning with Keras
   10.5 Advanced neural networks
11. Clustering
   11.1 k-means clustering
   11.2 Hierarchical clustering
   11.3 Other clustering algorithms
12. Dimensionality Reduction
   12.1 Feature selection
   12.2 Feature extraction using linear techniques
   12.3 Feature extraction using non-linear techniques
13. Appendix
   13.1 Datasets
   13.2 Symbols and Notation
   13.3 Vectors
   13.4 Matrices
   13.5 Derivatives
   13.6 Probability
Teach Machine Learning using the only interactive solution with integrated Jupyter Notebooks
Machine Learning is the first complete, interactive introduction to the foundational algorithms and techniques for machine learning using Python.
- Students can write and edit live code, create data visualizations, and output images right in the zyBook
- Includes data preprocessing, supervised and unsupervised learning algorithms, decision trees, neural networks, ensemble methods, and model evaluation techniques
- Jupyter Notebooks is embedded in the zyBook, so students work with real-world, professional tools
- Continuously updated with the latest advances in machine learning
Data science is interactive; it requires coding and live investigations of data sets. To do all that within a digital zyBook is really powerful.”
–Â Co-author Dr. Aimee Schwab-McCoy
Dr. Schwab-McCoy explains the benefits of zyBooks for data science instructors and students:
What is a zyBook?
Machine Learning is a web-native, interactive zyBook that helps students visualize concepts to learn faster and more effectively than with a traditional textbook.
Since 2012, over 1,200 universities and colleges across the country have adopted web-native zyBooks to transform their STEM education.
zyBooks benefit students and instructors:
- Instructor benefits
- Customize your course by reorganizing existing content, or adding your own
- Continuous publication model updates your course with the latest content and technologies
- Gain insight into students’ progress, reading and participation with robust reporting
- Save time with auto-graded labs and challenge activities that seamlessly integrate with your LMS gradebook
- Build quizzes and exams with over 300 included test questions
- Student benefits
- Learning questions and other content serve as an interactive form of reading
- Instant feedback on labs and homework
- Concepts come to life through extensive animations embedded into the interactive content
- Save chapters as PDFs to reference the material at any time
- Gain real-life, professional experience working with industry standard Jupyter Notebooks
Embedded Jupyter Notebooks
The Machine Learning zyBook is fully integrated with the industry standard Jupyter Notebooks web-based computing platform. So students will gain real-life experience writing and editing live code, creating data visualizations, and experimenting by changing models to evaluate their performance with a professional application.
Jupyter Notebooks can also be downloaded for offline use.
In this video, Dr. Schwab-McCoy demonstrates the power of zyBooks’ embedded Jupyter Notebooks:
Author
Aimee Schwab-McCoy
Manager, Content Development, Data Science and Statistics / Ph.D. in Statistics / University of Nebraska–Lincoln
Key Contributors
Chris Chan
Director, Content Development / M.A. in Mathematics / San Francisco State University
Pamela Fellers
Content Developer, Statistics / Ph.D. in Statistics / University of Nebraska–Lincoln
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