# Proclus Academy

## Machine Learning / Deep Learning

**Pandas DataFrame**.

First, I'll introduce the **two types of DataFrame indexes**. Then, I'll show you how to use the methods **iloc[]** and **loc[]** to access the **DataFrame** data.

**idxmin()**and

**idxmax()**to get the index of the minimum or maximum value.

These methods provide additional functionality for **DataFrames**. We'll cover that as well.

**HTML table data**from web pages using

**Pandas**and

**Python**.

You'll also learn to **handle a typical error** you may encounter when scraping web pages.

**SQL database**using

**Pandas**and

**Python**.

You'll learn to **write** a **DataFrame** to a database table and **load an entire table** or results of a **SQL query** into a **DataFrame**.

**Pandas**to interact with

**Excel**files.

I'll show you how to **read** spreadsheets, **load selected** columns and worksheets, and **write** DataFrames to Excel files.

**Pandas**for data analysis.

You'll learn how to **read, manipulate, sort, filter,** and **visualize datasets** using various **Pandas** methods.

**the distance between a typical value and the center**of a dataset?

Let's examine the three most common metrics to measure how values are spread out.

**one value that summarizes**and captures the essence of

**a given data set**?

Let's examine four measures that you can use to represent such a central value.

I’ll share the **topics you’ll need to learn**, the **best available resources**, and the order in which to study them.

**Normal Distribution**? How do you calculate

**probabilities**and

**percentiles**?

You'll learn to do all of that using **SciPy**. I'll also show you how to plot **histograms** and **density curves** for normally distributed data.