Book contents
- A Hands-On Introduction to Data Science
- A Hands-On Introduction to Data Science
- Copyright page
- Contents
- Preface
- About the Author
- Part I Conceptual Introductions
- Part II Tools for Data Science
- Part III Machine Learning for Data Science
- Part IV Applications, Evaluations, and Methods
- Book part
- Index
- References
Part III - Machine Learning for Data Science
Published online by Cambridge University Press: 07 February 2026
Book contents
- A Hands-On Introduction to Data Science
- A Hands-On Introduction to Data Science
- Copyright page
- Contents
- Preface
- About the Author
- Part I Conceptual Introductions
- Part II Tools for Data Science
- Part III Machine Learning for Data Science
- Part IV Applications, Evaluations, and Methods
- Book part
- Index
- References
Information
- Type
- Chapter
- Information
- A Hands-On Introduction to Data Science with Python , pp. 181 - 300Publisher: Cambridge University PressPrint publication year: 2026
References
Further Reading and Resources
If you are interested in learning more about the topics discussed in this chapter, the following are a few links that might be useful:
2. https://www.kdnuggets.com/2017/04/simple-understand-gradient-descent-algorithm.html
3. https://machinelearningmastery.com/gradient-descent-for-machine-learning/
Further Reading and Resources
If you are interested in learning more about supervised learning or any of the topics discussed above, following are a few links that might be useful:
1. Advanced regression models, http://r-statistics.co/adv-regression-models.html
2. Further topics on logistic regression, https://onlinecourses.science.psu.edu/stat504/node/217/
3. Common pitfalls in statistical analysis: logistic regression, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543767/
4. Decision trees in machine learning, simplified https://blogs.oracle.com/bigdata/decision-trees-machine-learning
5. A practical explanation of a naive Bayes classifier, https://monkeylearn.com/blog/practical-explanation-naive-bayes-classifier/
6. Softmax regression, http://deeplearning.stanford.edu/tutorial/supervised/SoftmaxRegression/
7. 6 Easy steps to learn the naive Bayes algorithm, https://www.analyticsvidhya.com/blog/2015/09/naive-bayes-explained/
Further Reading and Resources
If you are interested in learning more about unsupervised learning methods, the following are a few links that might be useful:
1. Data mining cluster analysis: advanced concepts and algorithms: https://www-users.cs.umn.edu/~kumar001/dmbook/dmslides/chap9_advanced_cluster_analysis.pdf
2. Advanced clustering methods: http://www.cse.psu.edu/~rtc12/CSE586/lectures/meanshiftclustering.pdf
3. An advanced clustering algorithm (ACA) for clustering large data set to achieve high dimensionality: https://www.omicsonline.org/open-access/an-advanced-clustering-algorithm-aca-for-clustering-large-data-jcsb.1000115.pdf
4. Expectation-maximization algorithm for clustering multidimensional numerical data: https://engineering.purdue.edu/kak/Tutorials/ExpectationMaximization.pdf
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