WEKA - Data Mining with Open Source Machine Learning Tool

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Description

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    Course

  • Methodology

    Online

  • Start date

    Different dates available

Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this.Weka is open source software issued under the GNU General Public License.We have put together several free online courses that teach machine learning and data mining using R Programming, Python Programming, Weka Toolkit and SQL.Yes, it is possible to apply Weka to process big data and perform deep learning!Who this course is for:Graduates or Pursuing BTech Students

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Location

Start date

Online

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Different dates availableEnrolment now open

About this course

Students can learn WEKA tool for data pre-processing, classification, regression, clustering, association rules, and visualization

Graduates or Pursuing BTech Students

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This centre's achievements

2021

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The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 4 years

Subjects

  • Programming
  • Systems
  • Data Mining
  • Artificial Intelligence

Course programme

WEKA - Data Mining with Open Source Machine Learning Tool 5 lectures 03:28:58 Waikato Environment for Knowledge Analysis (WEKA) Analysis & Prediction using WEKA Machine Learning Toolkit Python Libraries for Data Science Introduction to Data Science Introduction to Machine Learning Machine Learning:
  • It is similar like Human Learning
  • Machine learning is the sub-field of computer science that, according to Arthur Samuel, gives "computers the ability to learn without being explicitly programmed."
  • Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "machine learning" in 1959 while at IBM.
  • Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.
Traditional Programming vs Machine Learning:
  • In traditional programming, if we give inputs + programs to the computer, then computer gives the output.
  • In machine learning, if we give inputs + outputs to the computer, then computer gives the program (Predictive Model).
Example 1: Here "a" and "b" are inputs and "c" is outputa b c1 2 3 2 3 5 3 4 7 4 5 9 9 10 ? What is the output of c? Example 2: Here "x" is input and "y" is outputx y1 10 2 20 3 30 4 40 5 ? 500 ? y ~ x : y=10x Example 3: Here "x" is input and "y" is outputx y1 14 2 18 3 22 4 26 5 ? 500 ? here we can observe linear regression y ~ x : y=mx+c here m is slope and c is constant y=4x+10 WEKA - Data Mining with Open Source Machine Learning Tool 5 lectures 03:28:58 Waikato Environment for Knowledge Analysis (WEKA) Analysis & Prediction using WEKA Machine Learning Toolkit Python Libraries for Data Science Introduction to Data Science Introduction to Machine Learning Machine Learning:
  • It is similar like Human Learning
  • Machine learning is the sub-field of computer science that, according to Arthur Samuel, gives "computers the ability to learn without being explicitly programmed."
  • Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "machine learning" in 1959 while at IBM.
  • Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.
Traditional Programming vs Machine Learning:
  • In traditional programming, if we give inputs + programs to the computer, then computer gives the output.
  • In machine learning, if we give inputs + outputs to the computer, then computer gives the program (Predictive Model).
Example 1: Here "a" and "b" are inputs and "c" is outputa b c1 2 3 2 3 5 3 4 7 4 5 9 9 10 ? What is the output of c? Example 2: Here "x" is input and "y" is outputx y1 10 2 20 3 30 4 40 5 ? 500 ? y ~ x : y=10x Example 3: Here "x" is input and "y" is outputx y1 14 2 18 3 22 4 26 5 ? 500 ? here we can observe linear regression y ~ x : y=mx+c here m is slope and c is constant y=4x+10 Waikato Environment for Knowledge Analysis (WEKA) Waikato Environment for Knowledge Analysis (WEKA) Waikato Environment for Knowledge Analysis (WEKA) Waikato Environment for Knowledge Analysis (WEKA) Analysis & Prediction using WEKA Machine Learning Toolkit Analysis & Prediction using WEKA Machine Learning Toolkit Analysis & Prediction using WEKA Machine Learning Toolkit Analysis & Prediction using WEKA Machine Learning Toolkit Python Libraries for Data Science Python Libraries for Data Science Python Libraries for Data Science Python Libraries for Data Science Introduction to Data Science Introduction to Data Science Introduction to Data Science Introduction to Data Science Introduction to Machine Learning Machine Learning:
  • It is similar like Human Learning
  • Machine learning is the sub-field of computer science that, according to Arthur Samuel, gives "computers the ability to learn without being explicitly programmed."
  • Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "machine learning" in 1959 while at IBM.
  • Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.
Traditional Programming vs Machine Learning:
  • In traditional programming, if we give inputs + programs to the computer, then computer gives the output.
  • In machine learning, if we give inputs + outputs to the computer, then computer gives the program (Predictive Model).
Example 1: Here "a" and "b" are inputs and "c" is outputa b c1 2 3 2 3 5 3 4 7 4 5 9 9 10 ? What is the output of c? Example 2: Here "x" is input and "y" is outputx y1 10 2 20 3 30 4 40 5 ? 500 ? y ~ x : y=10x Example 3: Here "x" is input and "y" is outputx y1 14 2 18 3 22 4 26 5 ? 500 ? here we can observe linear regression y ~ x : y=mx+c here m is slope and c is constant y=4x+10 Introduction to Machine Learning Machine Learning:
  • It is similar like Human Learning
  • Machine learning is the sub-field of computer science that, according to Arthur Samuel, gives "computers the ability to learn without being explicitly programmed."
  • Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "machine learning" in 1959 while at IBM.
  • Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.
Traditional Programming vs Machine Learning:
  • In traditional programming, if we give inputs + programs to the computer, then computer gives the output.
  • In machine learning, if we give inputs + outputs to the computer, then computer gives the program (Predictive Model).
Example 1: Here "a" and "b" are inputs and "c" is outputa b c1 2 3 2 3 5 3 4 7 4 5 9 9 10 ? What is the output of c? Example 2: Here "x" is input and "y" is outputx y1 10 2 20 3 30 4 40 5 ? 500 ? y ~ x : y=10x Example 3: Here "x" is input and "y" is outputx y1 14 2 18 3 22 4 26 5 ? 500 ? here we can observe linear regression y ~ x : y=mx+c here m is slope and c is constant y=4x+10 Introduction to Machine Learning Machine Learning:
  • It is similar like Human Learning
  • Machine learning is the sub-field of computer science that, according to Arthur Samuel, gives "computers the ability to learn without being explicitly programmed."
  • Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "machine learning" in 1959 while at IBM.
  • Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.
Traditional Programming vs Machine Learning:
  • In traditional programming, if we give inputs + programs to the computer, then computer gives the output.
  • In machine learning, if we give inputs + outputs to the computer, then computer gives the program (Predictive Model).
Example 1: Here "a" and "b" are inputs and "c" is outputa b c1 2 3 2 3 5 3 4 7 4 5 9 9 10 ? What is the output of c? Example 2: Here "x" is input and "y" is outputx y1 10 2 20 3 30 4 40 5 ? 500 ? y ~ x : y=10x Example 3: Here "x" is input and "y" is outputx y1 14 2 18 3 22 4 26 5 ? 500 ? here we can observe linear regression y ~ x : y=mx+c here m is slope and c is constant y=4x+10 Introduction to Machine Learning Machine Learning:
  • It is similar like Human Learning
  • Machine learning is the sub-field of computer science that, according to Arthur Samuel, gives "computers the ability to learn without being explicitly programmed."
  • Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "machine learning" in 1959 while at IBM.
  • Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.
Traditional Programming vs Machine Learning:
  • In traditional programming, if we give inputs + programs to the computer, then computer gives the output.
  • In machine learning, if we give inputs + outputs to the computer, then computer gives the program (Predictive Model).
Example 1: Here "a" and "b" are inputs and "c" is outputa b c1 2 3 2 3 5 3 4 7 4 5 9 9 10 ? What is the output of c? Example 2: Here "x" is input and "y" is outputx y1 10 2 20 3 30 4 40 5 ? 500 ? y ~ x : y=10x Example 3: Here "x" is input and "y" is outputx y1 14 2 18 3 22 4 26 5 ? 500 ? here we can observe linear regression y ~ x : y=mx+c here m is slope and c is constant y=4x+10 Machine Learning:
  • It is similar like Human Learning
  • Machine learning is the sub-field of computer science that, according to Arthur Samuel, gives "computers the ability to learn without being explicitly programmed."
  • Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "machine learning" in 1959 while at IBM.
  • Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.
Traditional Programming vs Machine Learning:
  • In traditional programming, if we give inputs + programs to the computer, then computer gives the output.
  • In machine learning, if we give inputs + outputs to the computer, then computer gives the program (Predictive Model).
Example 1: Here "a" and "b" are inputs and "c" is outputa b c1 2 3 2 3 5 3 4 7 4 5 9 9 10 ? What is the output of c? Example 2: Here "x" is input and "y" is outputx y1 10 2 20 3 30 4 40 5 ? 500 ? y ~ x : y=10x Example 3: Here "x" is input and "y" is outputx y1 14 2 18 3 22 4 26 5 ? 500 ? here we can observe linear regression y ~ x : y=mx+c here m is slope and c is constant y=4x+10 Machine Learning:
  • It is similar like Human Learning
  • Machine learning is the sub-field of computer science that, according to Arthur Samuel, gives "computers the ability to learn without being explicitly programmed."
  • Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "machine learning" in 1959 while at IBM.
  • Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.
Traditional Programming vs Machine Learning:
  • In traditional programming, if we give inputs + programs to the computer, then computer gives the output.
  • In machine learning, if we give inputs + outputs to the computer, then computer gives the program (Predictive Model).
Example 1: Here "a" and "b" are inputs and "c" is outputa b c1 2 3 2 3 5 3 4 7 4 5 9 9 10 ? What is the output of c? Example 2: Here "x" is input and "y" is outputx y1 10 2 20 3 30 4 40 5 ? 500 ? y ~ x : y=10x Example 3: Here "x" is input and "y" is outputx y1 14 2 18 3 22 4 26 5 ? 500 ? here we can observe linear regression y ~ x : y=mx+c here m is slope and c is constant y=4x+10

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WEKA - Data Mining with Open Source Machine Learning Tool

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