WEKA - Data Mining with Open Source Machine Learning Tool
- Life Time Access
- Certificate on Completion
- Access on Android and iOS App
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
- Basic Mathematics is enough
- Students can learn WEKA tool for data pre-processing, classification, regression, clustering, association rules, and visualization
Waikato Environment for Knowledge Analysis (WEKA)
Analysis & Prediction using WEKA Machine Learning Toolkit
Python Libraries for Data Science
Introduction to Data Science
- 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 output
a b c
1 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 output
y ~ x : y=10x
Example 3: Here "x" is input and "y" is output
here we can observe linear regression
y ~ x : y=mx+c here m is slope and c is constant
From 0 to 1: Hive for Processing Big DataLoony Corn
CompTIA Network+ Cert.; N10-006. The Total CourseMike Meyers, Total Seminars