Just like the dots in the scatter plot shown before, most filled elements no longer have a black edge by default, making the graphics less cluttered:
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by Aldrin Yim, Claire Chung, Allen Yu, Pratap Dangeti
Numerical Computing with Python
- Title Page
- Copyright
- Contributors
- About Packt
- Preface
- Journey from Statistics to Machine Learning
- Tree-Based Machine Learning Models
- Introducing decision tree classifiers
- Comparison between logistic regression and decision trees
- Comparison of error components across various styles of models
- Remedial actions to push the model towards the ideal region
- HR attrition data example
- Decision tree classifier
- Tuning class weights in decision tree classifier
- Bagging classifier
- Random forest classifier
- Random forest classifier - grid search
- AdaBoost classifier
- Gradient boosting classifier
- Comparison between AdaBoosting versus gradient boosting
- Extreme gradient boosting - XGBoost classifier
- Ensemble of ensembles - model stacking
- Ensemble of ensembles with different types of classifiers
- Ensemble of ensembles with bootstrap samples using a single type of classifier
- Summary
- K-Nearest Neighbors and Naive Bayes
- K-nearest neighbors
- KNN classifier with breast cancer Wisconsin data example
- Tuning of k-value in KNN classifier
- Naive Bayes
- Probability fundamentals
- Understanding Bayes theorem with conditional probability
- Naive Bayes classification
- Laplace estimator
- Naive Bayes SMS spam classification example
- Summary
- Unsupervised Learning
- Reinforcement Learning
- Reinforcement learning basics
- Markov decision processes and Bellman equations
- Dynamic programming
- Grid world example using value and policy iteration algorithms with basic Python
- Monte Carlo methods
- Temporal difference learning
- SARSA on-policy TD control
- Q-learning - off-policy TD control
- Cliff walking example of on-policy and off-policy of TD control
- Further reading
- Summary
- Hello Plotting World!
- Hello Matplotlib!
- Plotting our first graph
- Summary
- Visualizing Online Data
- Visualizing Multivariate Data
- Adding Interactivity and Animating Plots
- Selecting Subsets of Data
- Boolean Indexing
- Calculating boolean statistics
- Constructing multiple boolean conditions
- Filtering with boolean indexing
- Replicating boolean indexing with index selection
- Selecting with unique and sorted indexes
- Gaining perspective on stock prices
- Translating SQL WHERE clauses
- Determining the normality of stock market returns
- Improving readability of boolean indexing with the query method
- Preserving Series with the where method
- Masking DataFrame rows
- Selecting with booleans, integer location, and labels
- Index Alignment
- Grouping for Aggregation, Filtration, and Transformation
- Defining an aggregation
- Grouping and aggregating with multiple columns and functions
- Removing the MultiIndex after grouping
- Customizing an aggregation function
- Customizing aggregating functions with *args and **kwargs
- Examining the groupby object
- Filtering for states with a minority majority
- Transforming through a weight loss bet
- Calculating weighted mean SAT scores per state with apply
- Grouping by continuous variables
- Counting the total number of flights between cities
- Finding the longest streak of on-time flights
- Restructuring Data into a Tidy Form
- Tidying variable values as column names with stack
- Tidying variable values as column names with melt
- Stacking multiple groups of variables simultaneously
- Inverting stacked data
- Unstacking after a groupby aggregation
- Replicating pivot_table with a groupby aggregation
- Renaming axis levels for easy reshaping
- Tidying when multiple variables are stored as column names
- Tidying when multiple variables are stored as column values
- Tidying when two or more values are stored in the same cell
- Tidying when variables are stored in column names and values
- Tidying when multiple observational units are stored in the same table
- Combining Pandas Objects
- Other Books You May Enjoy
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