Machine Learning.md

Machine Learning

• ML gives computer the ability to learn without being explicitly programmed.
• ML can extract structure | pattern from data.
• ML solve problems that are too difficullt for humans to solve.

Supervised Learning

• Supervised Learning is the most common form of ML.
• Supervised Learning model learns a relationship between feature matrix and target vector
• It can make predictions for unseen or future data.
• Regression : Predicts a continuous value.
• Classification : Predicts a categorical value.

Format data for Scikit Learn

• Before fitting | training a model with scikit learn, your data has to be in a numeric format.
• Scikit Learn expects feature matrix ( Independent features ) and target vector ( Dependent feature )

Import important libraries:

%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

Load dataset:

data = load_iris()

# Feature matrix:
df = pd.DataFrame(data.data, columns=data.feature_names) 

# Target vector:
df['species'] = data.target

Converting feature matrix into NumPy Array :

# Only comsider the feature matrix:
df.loc[:, ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']]

# Converting feature matrix into NumPy array:
X = df.loc[:, ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']].values
y = df.loc[:, 'species'].values

Scikit Learn expects data in this 👆🏻 format