The past week, on Monday and Tuesday I was in Berlin, for the DevOpsCon.

**On Tuesday** was the first day of the conference. I was present on a few DevOps talks and in general, I managed to understand what is being told in every single one of them. If I have to summarize the key messages I have remembered from these talks they are the following:

- Big companies are probably more flexible and better adaptive to change than the small ones (although you would never bet on this initially);
- If you have a startup idea and you are very sure in yourself that the world needs it, you really do not need any other proves;
- It’s never too late to become a high-end PHP developer, if you are dedicated enough and know how to learn from your colleagues;
- You should know the environment around you pretty well in any aspect, when you are proposing a solution/change/plan.

**On Monday**, I was on the **Machine learning using Python **workshop, where the lecturers have demonstrated the basics of machine learning with python. In general, mainly linear regression and some random forest examples. With a quick and simple example, here is how to make a linear regression with Python, using the built-in dataset from the sklearn package in Anaconda:

The code for the example is here:

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import matplotlib.pyplot as plt import numpy as np from sklearn import datasets, linear_model # Load and return the boston house-prices dataset (regression). boston = datasets.load_boston() # Use only one feature boston_X = boston.data[:, np.newaxis, 2] # Split the data into training/testing sets boston_X_train = boston_X[:-40] boston_X_test = boston_X[-40:] # Split the targets into training/testing sets boston_y_train = boston.target[:-40] boston_y_test = boston.target[-40:] # Create the linear regression object regr = linear_model.LinearRegression() # Train the model using the training sets regr.fit(boston_X_train, boston_y_train) # The mean squared error print("Mean squared error: %.2f" % np.mean((regr.predict(boston_X_test) - boston_y_test) ** 2)) # The coefficients print('Coefficients: \n', regr.coef_) # Explained variance score: 1 is perfect prediction print('Variance score: %.2f' % regr.score(boston_X_test, boston_y_test)) # Plot outputs plt.scatter(boston_X_test, boston_y_test, color='black') plt.plot(boston_X_test, regr.predict(boston_X_test), color='red', linewidth=3) plt.xticks(()) plt.yticks(()) plt.show() |

Pretty much that’s it! In general man can become a DevOps in 2 days only if he was *DevOps – 2 days* before these days 🙂