Types of Machine Learning

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Batch and Online Learning

Batch Learning

Online learning

Instance-Based Versus Model-Based Learning

Instance based

Model-based learning

import os datapath = os.path.join("datasets", "lifesat", "") # Download the data import urllib.request DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml2/master/" os.makedirs(datapath, exist_ok=True) for filename in ("oecd_bli_2015.csv", "gdp_per_capita.csv"): print("Downloading", filename) url = DOWNLOAD_ROOT + "datasets/lifesat/" + filename urllib.request.urlretrieve(url, datapath + filename)

Downloading oecd_bli_2015.csv
Downloading gdp_per_capita.csv

def prepare_country_stats(oecd_bli, gdp_per_capita): oecd_bli = oecd_bli[oecd_bli["INEQUALITY"]=="TOT"] oecd_bli = oecd_bli.pivot(index="Country", columns="Indicator", values="Value") gdp_per_capita.rename(columns={"2015": "GDP per capita"}, inplace=True) gdp_per_capita.set_index("Country", inplace=True) full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita, left_index=True, right_index=True) full_country_stats.sort_values(by="GDP per capita", inplace=True) remove_indices = [0, 1, 6, 8, 33, 34, 35] keep_indices = list(set(range(36)) - set(remove_indices)) return full_country_stats[["GDP per capita", 'Life satisfaction']].iloc[keep_indices]

# To plot pretty figures directly within Jupyter %matplotlib inline import matplotlib as mpl mpl.rc('axes', labelsize=14) mpl.rc('xtick', labelsize=12) mpl.rc('ytick', labelsize=12)

gdp_per_capita.head()

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import matplotlib import matplotlib.pyplot as plt import numpy as np import pandas as pd import sklearn.linear_model # Load the data oecd_bli = pd.read_csv("/work/ch_1/datasets/lifesat/oecd_bli_2015.csv", thousands=',') # /work/ch_1/datasets/lifesat/oecd_bli_2015.csv gdp_per_capita = pd.read_csv("/work/ch_1/ch_1_note_s.ipynb",thousands=',',delimiter='\t', encoding='latin1', na_values="n/a") # Prepare the data country_stats = prepare_country_stats(oecd_bli, gdp_per_capita) X = np.c_[country_stats["GDP per capita"]] y = np.c_[country_stats["Life satisfaction"]] # Visualize the data country_stats.plot(kind='scatter', x="GDP per capita", y='Life satisfaction') plt.show() # Select a linear model model = sklearn.linear_model.LinearRegression() # Train the model model.fit(X, y) # Make a prediction for Cyprus X_new = [[22587]] # Cyprus' GDP per capita print(model.predict(X_new)) # outputs [[ 5.96242338]]

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filename

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