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Machine Learning With R | Machine Learning Algorithms | Data Science Training | Edureka - PowerPoint PPT Presentation


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( Data Science Training : https://www.edureka.co/data-science )
This "Machine Learning with R" tutorial by Edureka will help you to understand the core concepts of Machine Learning followed by a very interesting case study on Pokemon Dataset in R. This tutorial will comprise of these topics:

1. Understanding Machine Learning
2. Applications of Machine Learning
3. Types of Machine Learning Algorithms
4. Case Study on the "Pokemon Dataset" to implement Machine Learning Algorithms

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    Presentation Transcript
    machine learning with r

    Machine Learning with R

    Copyright © 2017, edureka and/or its affiliates. All rights reserved.

    agenda

    Agenda

    Understanding Machine Learning

    1

    2

    Applications of Machine Learning

    3

    Languages for Machine Learning

    Types of Machine Learning Algorithms

    4

    Implementing Machine Learning Algorithms

    with R

    5

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    understanding machine learning

    Understanding Machine Learning

    Copyright © 2017, edureka and/or its affiliates. All rights reserved.

    understanding machine learning 1

    Understanding Machine Learning

    What is

    this?

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    understanding machine learning 2

    Understanding Machine Learning

    How about

    this?

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    understanding machine learning 3

    Understanding Machine Learning

    And this?

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    understanding machine learning 4

    Understanding Machine Learning

    How do you

    know all of

    these are

    fish?

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    understanding machine learning 5

    Understanding Machine Learning

    Now, how will

    a computer

    identify it as

    a fish?

    Machine

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    understanding machine learning 6

    Understanding Machine Learning

    Fish

    Fish

    Fish

    Machine

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    understanding machine learning 7

    Understanding Machine Learning

    Fish

    New Data

    Machine

    Result

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    applications of machine learning

    Applications of Machine Learning

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    applications of machine learning 1

    Applications of Machine Learning

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    languages for machine learning

    Languages for Machine Learning

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    languages for machine learning 1

    Languages for Machine Learning

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    why r

    Why R?

    Functional

    Programming

    Turing Complete

    Open Source

    Object Oriented

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    steps in machine learning

    Steps in Machine Learning

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    steps in machine learning 1

    Steps in Machine Learning

    Step 1

    Ingesting Data

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    steps in machine learning 2

    Steps in Machine Learning

    Data Wrangling

    Step 1

    Step 2

    Ingesting Data

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    steps in machine learning 3

    Steps in Machine Learning

    Data Wrangling

    Step 3

    Step 1

    Step 2

    Ingesting Data

    Train Algorithm

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    steps in machine learning 4

    Steps in Machine Learning

    Test Algorithm

    Data Wrangling

    Step 3

    Step 4

    Step 1

    Step 2

    Ingesting Data

    Train Algorithm

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    steps in machine learning 5

    Steps in Machine Learning

    Test Algorithm

    Data Wrangling

    Step 3

    Step 4

    Step 1

    Step 5

    Step 2

    Ingesting Data

    Train Algorithm

    Evaluate Result

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    types of machine learning

    Types of Machine Learning

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    types of machine learning algorithms

    Types of Machine Learning Algorithms

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    supervised learning

    Supervised Learning

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    supervised learning 1

    Supervised Learning

    Supervised learning

    algorithm, learns

    from a known

    dataset(training set)

    to make predictions

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    supervised learning 2

    Supervised Learning

    I need

    Training to

    perform well

    in the test

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    classification

    Classification

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    classification 1

    Classification

    Man

    Woman

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    classification decision tree

    Classification – Decision Tree

    A path from root to

    leaf represents

    classification rules

    Root Node

    Branch Node

    Leaf Node

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    classification demo

    Classification Demo

    Car_Purchase Data-set

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    regression

    Regression

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    regression 1

    Regression

    Function: Y=F(X)

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    regression demo

    Regression Demo

    Diamonds Data-set

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    unsupervised learning

    Unsupervised Learning

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    unsupervised learning 1

    Unsupervised Learning

    Unsupervised

    learning algorithm

    draws inferences

    from data which does

    not have labels

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    unsupervised learning 2

    Unsupervised Learning

    No training

    required, we

    can learn by

    ourselves

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    clustering

    Clustering

    Cluster 1

    Cluster 2

    Data

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    clustering demo

    Clustering Demo

    Iris Data-set

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    reinforcement learning

    Reinforcement Learning

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    reinforcement learning 1

    Reinforcement Learning

    Reinforcement

    learning involves

    learning ideal

    behavior through a

    reward feedback

    system

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    reinforcement learning 2

    Reinforcement Learning

    Eat food, earn points!

    Hit a monster, lose

    life!

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    real world applications of reinforcement learning

    Real World Applications of Reinforcement Learning

    Manufacturing Robots

    Autonomous Flying

    Helicopter

    Google’s Self Driving Car

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    machine learning case study

    Machine Learning Case Study

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    case study on pok mon dataset

    Case Study on Pokémon Dataset

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    problem statement task 1

    Problem Statement –Task 1

    You are a Pokémon trainer who is

    entering the Jhoto League and have to

    select one fire, water and grass

    Pokémon for your fight

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    problem statement regression task 2

    Problem Statement(Regression)-Task 2

    Now, that you have won Jhoto League,

    you want to understand what are the

    factors influencing a Pokémon's attack!

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    problem statement classification task 3

    Problem Statement(Classification)-Task 3

    Team Rocket is on a mission to end the

    world and this time only the Legendary

    Pokemons can save the world. So, go

    ahead and catch all the Legendary

    Pokemon

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    tasks performed

    Tasks Performed

    Select fire,

    water and

    grass

    Pokémons

    Data Wrangling

    Find out

    how does a

    Pokemon’s

    defence

    influence it’s

    aattack

    Regression

    Classify if

    the

    Pokémon is

    legendary or

    not

    Classification

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    summary

    Summary

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    summary 1

    Summary

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