Data Mining with RapidMiner
Data Mining with RapidMiner, available at $24.99, has an average rating of 2.85, with 39 lectures, based on 37 reviews, and has 652 subscribers.
You will learn about Data Mining using RapidMIner This course is ideal for individuals who are Beginner Data Scientist or Analyst interested in RapidMiner It is particularly useful for Beginner Data Scientist or Analyst interested in RapidMiner.
Enroll now: Data Mining with RapidMiner
Summary
Title: Data Mining with RapidMiner
Price: $24.99
Average Rating: 2.85
Number of Lectures: 39
Number of Published Lectures: 39
Number of Curriculum Items: 39
Number of Published Curriculum Objects: 39
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Data Mining using RapidMIner
Who Should Attend
- Beginner Data Scientist or Analyst interested in RapidMiner
Target Audiences
- Beginner Data Scientist or Analyst interested in RapidMiner
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 Data Mining using RapidmIner. This course uses CRISP-DM data mining process.
You will learn RapidMiner to do data understanding, data preparation, modeling, and Evaluation. You will be able to train your own prediction models with Naive Bayes, decision tree, knn, neural network, and linear regression, and evaluate your models very soon after learning the course.
You can take the course as following and you can take an exam at EMHAcademy to get SVBook Advance Certificate in Data Science using DSTK, Excel, and RapidMiner:
– Introduction to Data and Text Mining using DSTK 3
– Data Mining with RapidMiner
– Learn Microsoft Excel Basics Fast
– Learn Data analysis using Microsoft Excel Basics Fast.
Content
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Getting Started
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Getting Started 2
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Data Mining Process
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Download Data Set
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Read CSV
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Data Understanding: Statistics
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Data Understanding: Scatterplot
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Data Understanding: Line
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Data Understanding: Bar
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Data Understanding: Histogram
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Data Understanding: BoxPLot
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Data Understanding: Pie
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Data Understanding: Scatterplot Matrix
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Data Preparation: Normalization
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Data Preparation: Replace Missing Values
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Data Preparation: Remove Duplicates
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Data Preparation: Detect Outlier
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Modeling: Simple Linear Regression
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Modeling: Simple Linear Regression using RapidMiner
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Modeling: KMeans CLustering
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Modeling: KMeans Clustering using RapidmIner
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Modeling: Agglomeration CLustering
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Modeling: Agglomeration Clustering using RapidmIner
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Modeling: Decision Tree ID3 Algorithm
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Modeling: Decision Tree ID3 Algorithm using RapdimIner
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Modeling: Decision Tree ID3 Algorithm using RapidMiner
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Evaluation: Decision Tree ID3 Algorithm using RapidmIner
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Modeling: KNN Classification
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Modeling: KNN CLassification using RapidmIner
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Evaluation: KNN Classification using RapidmIner
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Modeling Naive Bayes Classification
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Modeling: Naive Bayes Classification using RapidmIner
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Evaluation: Naive Bayes Classification using RapidMIner
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Modeling: Neural Network Classification
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Modeling: Neural Network Classification using RapidmIner
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Evaluation: Neural Network Classification using RapidmIner
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What Algorithm to USe?
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Model Evaluation
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k fold cross-validation using RapdimIner
Course Curriculum
Chapter 1: Content
Lecture 1: Getting Started
Lecture 2: Getting Started
Lecture 3: Data Mining Process
Lecture 4: Download Dataset
Lecture 5: Read CSV
Lecture 6: Data Understanding – Statistics
Lecture 7: Data Understanding – Scatterplot
Lecture 8: Data Understanding – Line
Lecture 9: Data Understanding – Bar
Lecture 10: Data Understanding – Histogram
Lecture 11: Data Understanding – Boxplot
Lecture 12: Data Understanding – Pie
Lecture 13: Data Understanding – Scatterplot Matrix
Lecture 14: Data Preparation – Normalization
Lecture 15: Data Preparation – Replace Missing Values
Lecture 16: Data Preparation – Remove Duplicates
Lecture 17: Data Preparation – Detect Outlier
Lecture 18: Modeling: Simple Linear Regressions
Lecture 19: Modeling: Simple Linear Regressions (RapidMIner)
Lecture 20: Modeling: KMeans Clustering
Lecture 21: Modeling: KMeans Clustering using RapidmIner
Lecture 22: MOdeling: Agglomeration CLustering
Lecture 23: MOdeling: Agglomeration CLustering using RapidMIner
Lecture 24: MOdeling: Decision Tree ID3 Algorithm
Lecture 25: MOdeling: Decision Tree ID3 Algorithm using RapidMIner
Lecture 26: MOdeling: Decison Tree ID3 Algorithm using RapidMiner
Lecture 27: Evaluation: Decison Tree ID3 Algorithm using Rapidminer
Lecture 28: Modeling: KNN Classification
Lecture 29: Modeling: KNN Classification using RapidmIner
Lecture 30: Evaluation: KNN Classification using RapidMIner
Lecture 31: Modeling: Naive Bayes Classification
Lecture 32: Modeling: Naive Bayes Classification using RapidmIner
Lecture 33: Evaluation: Naive Bayes Classification using RapidmIner
Lecture 34: MOdeling: Neural Network
Lecture 35: Modeling: Neural Network using Rapidminer
Lecture 36: Evaluation: Neural Network using RapidmIner
Lecture 37: What ALgorithm to Use?
Lecture 38: Model Evaluation
Lecture 39: Evaluation: K Fold Cross Validation
Instructors
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Goh Ming Hui
Offer affordable data science courses.
Rating Distribution
- 1 stars: 8 votes
- 2 stars: 8 votes
- 3 stars: 8 votes
- 4 stars: 9 votes
- 5 stars: 4 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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