import pandas as pd %matplotlib inline import random import matplotlib.pyplot as plt import seaborn as sns
df = pd.DataFrame() df['x'] = random.sample(range(1, 100), 25) df['y'] = random.sample(range(1, 100), 25)
sns.lmplot('x', 'y', data=df, fit_reg=False)
<seaborn.axisgrid.FacetGrid at 0x114563b00>
<matplotlib.axes._subplots.AxesSubplot at 0x113ea2ef0>
<matplotlib.axes._subplots.AxesSubplot at 0x113d7fef0>
<matplotlib.axes._subplots.AxesSubplot at 0x114294160>
plt.hist(df.x, alpha=.3) sns.rugplot(df.x);
<matplotlib.axes._subplots.AxesSubplot at 0x1142b8b38>
<matplotlib.axes._subplots.AxesSubplot at 0x114444a58>
sns.heatmap([df.y, df.x], annot=True, fmt="d")
<matplotlib.axes._subplots.AxesSubplot at 0x114530c88>
<seaborn.matrix.ClusterGrid at 0x116f313c8>
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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