A.2 Least squares and maximum likelihood estimation.A.1 Informal review on hypothesis testing.6.4 Prediction and confidence intervals.6.3 Kernel regression with mixed multivariate data.5.3.2 Confidence intervals for the coefficients.5.3.1 Distributions of the fitted coefficients.5.1 Case study: The Challenger disaster.4.3.1 Model formulation and least squares.4 Linear models III: shrinkage, multivariate response, and big data.3.6.1 Review on principal component analysis.3.5.6 Outliers and high-leverage points.3.4.1 Transformations in the simple linear model.3.1 Case study: Housing values in Boston.3 Linear models II: model selection, extensions, and diagnostics.2.4.2 Confidence intervals for the coefficients.2.4.1 Distributions of the fitted coefficients.2.2 Model formulation and least squares.2 Linear models I: multiple linear model.Here is how we can learn about the query function of Pandas library. We just need to add ? at the end of the function name. In such cases, we can learn about the signature and docstring of a function inside the Jupyter notebook. We sometimes can’t remember what a function does exactly or its syntax. Those libraries typically have a lot of functions and methods. Python has a rich selection of third party libraries, which simplify tasks and speed up development processes. If only one cell is selected, it is merged with the cell below. ESC + SHIFT + M merges selected cells.ESC + DOWN or ESC + J selects the cell below.ESC + UP or ESC + K selects the cell above.CTRL + D (⌘ + D in Mac) deletes what is written in the current line.There are several shortcuts that you can use in Jupyter notebooks. Here are some examples that will help you get more productive.
Magic commands start with the “%” syntax element. They are quite useful for performing a variety of tasks. Magic commands are built-in for the IPython kernel. If a cell is not needed anymore, you can delete it with “ESC + D + D”. We can hide a cell output with “ESC + O” and unhide by pressing these keys again. However, some outputs take too much space and make the overall content hard to follow. This is very useful because you do not have to execute a cell each time you want to check its output or results. One of the great features of Jupyter notebooks is that they maintain the state of execution of each cell.
#LOGISTIC REGRESSION FLATICON FULL#
You can run the code cell-by-cell which expedites the debugging process as well as understanding other people’s code.Īlthough we quite often use Jupyter notebooks in our work, we do not make the most out of them and fail to discover its full potential.In-line outputs including data visualizations are highly useful for exploratory data analysis.It supports Markdown cells which are great for write-ups, preparing reports, and documenting your work.Being able to see the code and the output together makes it easier to learn and practice.There are several reasons why the Jupyter notebook is a highly popular tool. They are great for learning, practicing, and experimenting. Most of us start our learning journeys in Jupyter notebooks. The Jupyter Notebook is a web-based interactive computing platform, and it is usually the first tool we learn about in data science.
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