## Tic-Tac-AI: A Strong Tic-Tac-Toe AI Opponent using Forward Sampling

You want to build your own Tic-Tac-Toe opponent? Then you need to read further! In the Tic-Tac-AI series, I will present a couple of Artificial Intelligence algorithms implemented as Tic-Tac-Toe opponent. In this first article, I will introduce a method called Forward Sampling which is capable of not losing any game of Tic-Tac-Toe!

## Mastering Pandas

In this course, you will learn how to use the Python Pandas. After the course, you will be able to:

• Visualizing data using line plots, scatter plots and histograms
• Merging and storing data

The course also includes more advanced topics, such as data parallelization and aggregation.

You can see all course content under “Curriculum” on Data Blogger Courses and the first three lessons are free. The first free lesson can be found here.

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## Win with gambling: MAP estimation on a Bernoulli distribution

In this article, we will try to win a (fictive) lottery called Win-Win. In fact, you will learn how to do a MAP (maximum a posteriori) estimation on a Bernoulli distribution. Don’t worry if you don’t know all these words, everything will be explained. If you already know some of the terms, then you can skip these parts. (more…)

Suppose that we have a probability density function (PDF) $f(x)$ that is impossible to analyze analytically. How can we ever draw samples from this PDF? Luckily, there are many techniques out there and this time I will highlight rejection sampling. A simple to implement (but not always effective) sampling method.