/* Load libraries */ from sklearn.datasets import make_blobs from sklearn.cluster import KMeans import pandas as pd
/* Make simulated feature matrix */ X, _ = make_blobs(n_samples = 50, n_features = 2, centers = 3, random_state = 1) /* Create DataFrame */ df = pd.DataFrame(X, columns=['feature_1','feature_2'])
/* Make k-means clusterer */ clusterer = KMeans(3, random_state=1) /* Fit clusterer */ clusterer.fit(X)
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300, n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto', random_state=1, tol=0.0001, verbose=0)
Create Feature Based On Predicted Cluster
/* Predict values */ df['group'] = clusterer.predict(X) /* First few observations */ df.head(5)
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes
Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!
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