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 🙂