Machine Learning with Knime for Managers without programming

Machine Learning with Knime for Managers without programming, available at Free, has an average rating of 4.3, with 15 lectures, based on 126 reviews, and has 3965 subscribers.

You will learn about Machine learning Data visualization Create a workflow of machine learning algorithm Linear regression This course is ideal for individuals who are decision makers, data analist, students, and people interested in learn to use machine learning It is particularly useful for decision makers, data analist, students, and people interested in learn to use machine learning.

Enroll now: Machine Learning with Knime for Managers without programming

Summary

Title: Machine Learning with Knime for Managers without programming

Price: Free

Average Rating: 4.3

Number of Lectures: 15

Number of Published Lectures: 15

Number of Curriculum Items: 15

Number of Published Curriculum Objects: 15

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Machine learning
Data visualization
Create a workflow of machine learning algorithm
Linear regression

Who Should Attend

decision makers, data analist, students, and people interested in learn to use machine learning

Target Audiences

decision makers, data analist, students, and people interested in learn to use machine learning

In last couple of years, machine learning has become a fundamental tool for decision-making in the corporate world, as well as widely used in different areas, ranging from business to the purest science, allowing the computer to perform the most difficult tasks finding patterns and correlations between variables that allow through inferential statistics to make predictions based on data, which allows creating competitive advantages in various fields, taking advantage of the inputs of historical information of the company or business to find patterns and correlations that allow us to identify possible future outcomes. Under the supervised learning paradigm we will apply the concept of linear regression to project, through the equation of the line, the possible result values in relation to predictor variables and dependent variables, in which Machine Learning is able to identify the weights of importance for each one of the participating variables, its relationship with the variable of interest to be predicted and even determine if any system variable can be discarded, in order to create a model with a high level of statistical reliability that contributes to the decision-making process management, finally one of the objectives of the course is to also understand the importance of data visualization to achieve a greater understanding of the results of the model and thus identify how far or close to the actual results selected to evaluate the model we are

Course Curriculum

Chapter 1: Thank you for your support

Lecture 1: Let me know if you want more Machine Learning examples

Chapter 2: Introduction

Lecture 1: Speed showcase of the Knime tool

Lecture 2: Download and Install KNIME

Lecture 3: KNIME Interface

Chapter 3: Creating a workspace group and a workflow project

Lecture 1: Creating a workspace group and a workflow project

Chapter 4: Load a table data

Lecture 1: Load a table data in Excel file format

Chapter 5: Scatter matrix Graph

Lecture 1: Scatter matrix Graph

Chapter 6: Partitioning data to train and test our model

Lecture 1: Partitioning data to train and test our model

Chapter 7: Machine learning for linear regression

Lecture 1: Linear regressión learner

Chapter 8: Machine Learner Predictor

Lecture 1: Make a prediction over the remaining Test Data

Chapter 9: Mesure the effectiveness of the model with Numeric Scorer

Lecture 1: Mesure the effectiveness of the model with Numeric Scorer

Chapter 10: Visualization of the result

Lecture 1: Visualization of the result for better understanding

Chapter 11: Predict over new data (Working model)

Lecture 1: Predict over new dataset – Week 7 Budget

Chapter 12: Export results to an excel file

Lecture 1: Export results to an excel file

Chapter 13: Extra class

Lecture 1: Extra class

Instructors

Kenneth Alvarenga
Ing. en Computación, Msc. Admón Industrial, Tec. Mercadeo

Rating Distribution

1 stars: 1 votes
2 stars: 4 votes
3 stars: 19 votes
4 stars: 41 votes
5 stars: 61 votes

Frequently Asked Questions

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