Data science from scratch first principles with Python
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. With this updated second edition, you'll learn how many of the most fundamental data science tool...
| Autor principal: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, Calif. :
O'Reilly Media
2019.
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| Edición: | 2nd ed |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630478506719 |
Tabla de Contenidos:
- Introduction
- A crash course in Python
- Visualizing data
- Linear algebra
- Statistics
- Probability
- Hypothesis and inference
- Gradient descent
- Getting data
- Working with data
- Machine learning
- k-nearest neighbors
- Naive bayes
- Simple linear regression
- Multiple regression
- Logistic regression
- Decision trees
- Neural networks
- Deep learning
- Clustering
- Natural language processing
- Network analysis
- Recommender systems
- Databases and SQL
- MapReduce
- Data ethics
- Go forth and do data science.
