Last week we saw the fundamentals of the matplotlib package and what we can do with it, now we’ll see how to use it with *pandas* and *numpy* to use it effectively.

Pandas dataframes are the most common data structure that we’ll use and we can obviously plot them with matplolib. When we introduced pandas we used a sample about baseball players with just ten rows because I have deleted all of them to simplify the learning. Now we can use the complete one that you can find here

But we have a problem: the names of the columns are…

We know about pandas and numpy and after matplotlib, we’ll know the *core* of machine learning; the prior knowledge needed to make actually a good project and how these three library matches so much together, so let’s dive in!

To get the maximum from matplotlib we can’t just import it, we have to add a special line first:

`%matplotlib inline`

will display the *plot*, or the *figure*(more later on), into our notebook, without we would have just a series of fancy numbers as output.

The principal function in matplotlib is *plot*, and is used to display a simple x and…

In this week we’ll see the final part of all the functions that we may need to use numpy in our machine learning project.

Let’s dive in!

In python, using numpy, as well as other languages, we’ll have a lot of ways to do the same thing; In python, we have the *sum* function and with numpy we have to *np.sum* function too.

As you can see both returns the same result, leaving a question:

When is a function better than the other?

Num is the official python function to sum python lists, while the np.sum function is the official…

Numpy is a powerful python *package* for three main reasons:

- python is slow and numpy is written mostly in C, one of the fastest languages ever created.
- Numpy is the backbone of other packages.
- It makes it easy to work with
*arrays*and*matrices*.

The core of machine learning is to find patterns through a lot of numbers that’s why numpy is *essential*.

examples were taken by the machine learning course by mrdbourke

In machine learning our main focus will be on how the shape of our data will lines up with other data and outputs and numpy arrays, technically…

Last week we saw the first steps on how to display data in pandas on jupyter notebook, but there is still some work to do.

If we are analyzing a dataFrame there is the chance that we do not always want to see all the data but to just compare two columns between them. We can do that with the *crosstab* function, which accepts as parameter 2 columns.

Recursion is a function that calls itself, and if this phrase can make it look simple, it can get complicated very quickly.

In this article we’ll see the difference between *recursion and iteration*, *when to use it*, *how to use it*, *how it actually works* and its *possible problems*.

It all begins with the *main* function that calls the recursive function, and for then the function calling itself.

The pseudocode for it will be similar to

After we did the set up of our environment we can now do the actual work, and to be able to do it we have to learn python and its library dedicated to the data analysis, pandas.

Learning python is very easy, and if you have any experience with a programming language will certainly learn python easily. I’ll not cover it by myself just because you can find all you need here. or, if you want to **overkill**, learn python the hard way

What we will use here are especially *lists* and *matrixes*, but not at a difficult level. This…

to be able to learn machine learning with python we have to set up the environment first.

To create machine learning environments for python we need Anaconda, a software that has all the packages that we need to actually begin. …

I have always been interested in artificial intelligence, thats why I have begun to acquire some knowledge about it.

In the last few days I have begun my path from a simple programmer to someone who knows at least a bit about about AIs for then focus my efforts on machine learning. in these few days, this is what I have learned.

Everything began with this youtube video. Besides being 3 years old is still a good starting point for a newbie in artificial intelligences. …

High school student at the 4th year of computer science,improving my knowledge every day