Learn Machine Learning with Weka

Description

Why learn Data Analysis and Data Science?

According to SAS, the five reasons are

1. Gain problem solving skills

The ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life.

2. High demand

Data Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase.

3. Analytics is everywhere

Data is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It's a hugely exciting time to start a career in analytics.

4. It's only becoming more important

With the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities.

5. A range of related skills

The great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths.  Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise.

The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities.

This is the bite-size course to learn Weka and Machine Learning. You will learn Machine Learning which is the Model and Evaluation of the CRISP Data Mining Process. You will learn Linear Regression, Kmeans Clustering, Agglomeration Clustering, KNN, Naive Bayes, and Neural Network in this course. 

Content

1. Getting Started

2. Getting Started 2

3. Data Mining Process

4. Simple Linear Regression

5. Regression in Weka

6. KMeans Clustering

7. KMeans Clustering in Weka

8. Agglomeration Clustering

9. Agglomeration Clustering in Weka

10. Decision Tree: ID3 Algorithm

11. Decision Tree in Weka

12. KNN Classification

13. KNN in Weka

14. Naive Bayes

15. Naive Bayes in Weka

16. What Algorithm to use?

17. Model Evaluation

18. Weka Advanced Attribute Selection

19. Weka Advanced Data Visualizations

20. Weka Model Selection and Deployment

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