Starting with installing and setting up scikit-learn, this book contains highly practical recipes on common supervised and unsupervised machine learning concepts. Acquire your data for analysis; select the necessary features for your model; and implement popular techniques such as linear models, classification, regression, clustering, and more in no time at all! The book also contains recipes on evaluating and fine-tuning the performance of your model. The recipes contain both the underlying motivations and theory for trying a technique, plus all the code in detail.
- Donald Knuth
scikit-learn and Python allow fast prototyping, which is in a sense the opposite of Donald Knuth's premature optimization. Personally, scikit-learn has allowed me to prototype what I once thought was impossible, including large-scale facial recognition systems and stock market trading simulations. You can gain instant insights and build prototypes with scikit-learn. Data science is, by definition, scientific and has many failed hypotheses. Thankfully, with scikit-learn you can see what works (and what does not) within the next few minutes.
Additionally, Jupyter (IPython) notebooks feature a nice interface that is very welcoming to beginners and experts alike and encourages a new scientific software engineering mindset. This welcoming nature is refreshing because, in innovation, we are all beginners.
In the last chapter of this book, you can make your own estimator and Python transitions from a scripting language to more of an object-oriented language. The Python data science ecosystem has the basic components for you to make your own unique style and contribute heavily to the data science team and artificial intelligence.
In analogous fashion, algorithms work as a team in the stacker. Diverse algorithms of different styles vote to make better predictions. Some make better choices than others, but as long as the algorithms are different, the choice in the end will be the best. Stackers and blenders came to prominence in the Netflix $1 million prize competition won by the team Pragmatic Chaos.
Welcome to the world of scikit-learn: a very powerful, simple, and expressive machine learning library. I am truly excited to see what you come up with.
