Scikit Learn Tutorial | Machine Learning with Python | Python for Data Science Training | Edureka

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(Python Certification Training for Data Science: https://www.edureka.co/python) This Edureka video on "Scikit-learn Tutorial" introduces you to machine learning in Python. It will also takes you through regression and clustering techniques along with a demo on SVM classification on the famous iris dataset. This video helps you to learn the below topics: 1. Machine learning Overview 2. Introduction to Scikit-learn 3. Installation of Scikit-learn 4. Regression and Classification 5. Demo Subscribe to our channel to get video updates. Hit the subscribe button and click the bell icon.

Technologie

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Topics Covered in Today’s Training
Machine Learning Overview
Introduction To Scikit-learn
Installation of Scikit-learn
Regression & Classification
Demo
1
2
3
4
5

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What is Machine Learning?

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It is a type of Artificial Intelligence that allows software applications to learn from the data and
become more accurate in predicting outcomes without human intervention.
What is Machine Learning?
Training
Data
Learn
Algorithm
Build Model Perform
Feedback

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Types of Machine Learning
03
Supervised
01
This is a process of an
algorithm learning from the
training dataset.
This is a process where a
model is trained using an
information which is
not labelled.
Reinforcement learning is
learning by interacting with
a space or an environment.
Unsupervised
02
Reinforcement
03

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What is Sckit-learn?

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Introduction to Scikit-learn
P
O
R
T
A
B
L
E
Open source library which is licensed
under BSD
Built on Numpy, Scipy & Matplotlib
Many tuning Parameters
Documentation & Support

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Installation of Scikit-learn
pip install scikit-learn
conda install scikit-learn
COMMANDS:
OR

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Scikit-learn
From sklearn.linear_model import LinearRegression
From sklearn.family import Model
Example:

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Regression & Clustering

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Regression & Classification
Classification
Classification is the problem identifying to which
set of categories a new observation belongs.
Classifier
Regression
Regression is the prediction of a numeric value
and often takes input as a continuous value.

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Dataset
➢ The data set consists of 50 samples from three species
of Iris - Iris Setosa, Virginica and versicolor
➢ Four features were measured from each sample: Length
and the width of the sepals and petals, in centimetres.
IRIS Dataset

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Supervised Learning:
Implement Logistic Regression

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Classification
Decision Tree
Random Forest
Naiive Bayes Classifier
Support Vector Machine
Algorithms

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Classification
Algorithms
Decision Tree
Random Forest
Naiive Bayes Classifier
Support Vector Machine

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Support Vector Machine (SVM)
➢ SVM is a supervised machine learning algorithm which can be used for both classification
or regression challenges
➢ It tries to define a hyperplane which can split the data in the most optimal way

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SVM Use-Case

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Support Vector Machine (SVM)
Use SVM on the iris datset n we will create a model which can classify the flowers
based on their features.
Problem
Statement

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K-nearest neighbors

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Session in a Minute
Machine Learning Scikit-Learn
Regression & Clustering
Installation
Demo

Scikit Learn Tutorial | Machine Learning with Python | Python for Data Science Training | Edureka

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