Machine Learning for total Beginners

Machine Learning for total Beginners, available at $27.99, has an average rating of 4.1, with 22 lectures, based on 10 reviews, and has 101 subscribers.

You will learn about what is Machine Learning how to do Machine Learning how to get and present the result of Machine Learning This course is ideal for individuals who are People interested in data analysis It is particularly useful for People interested in data analysis.

Enroll now: Machine Learning for total Beginners

Summary

Title: Machine Learning for total Beginners

Price: $27.99

Average Rating: 4.1

Number of Lectures: 22

Number of Published Lectures: 22

Number of Curriculum Items: 22

Number of Published Curriculum Objects: 22

Original Price: $59.99

Quality Status: approved

Status: Live

What You Will Learn

what is Machine Learning
how to do Machine Learning
how to get and present the result of Machine Learning

Who Should Attend

People interested in data analysis

Target Audiences

People interested in data analysis

The easiest way to learn and do various machine learning in the world

Lectures that will definitely satisfy the beginners

Lectures that will surprise any skilled person

Lectures that make you become familiar with the machine through machine learning

Lectures that make you wait for the next lectures

You will learn how to conduct, compare, validate and present a variety of machine learning and their result.

Sample data for all lectures are given.

Free unlimited tools to try it out are given.

Course Curriculum

Chapter 1: Step 1 Introduction to Machine Learning

Lecture 1: Introduction to Machine Learning with Bayes(1)

Lecture 2: Introduction to Machine Learning with Bayes(2)

Lecture 3: ML with Neural Networks

Lecture 4: Confusion Matrix

Chapter 2: Step 2 Classification

Lecture 1: ML with kNN

Lecture 2: ML with SVM

Lecture 3: ML with DecisionTree(party)

Lecture 4: ML with DecisionTree(tree)

Lecture 5: ML with DecisionTree(rpart)

Lecture 6: ML with Random-Forest

Lecture 7: ML with Bagging

Chapter 3: Step 3 Numric Prediction

Lecture 1: ML with Gradient boosting

Lecture 2: ML with GLM

Lecture 3: ML with BMA

Lecture 4: ML with SVM

Lecture 5: Evaluation of numeric Model(Matrix scatter plot)

Lecture 6: Evaluation of numeric Model(Bland-Altman plot plot)

Chapter 4: Step 4 Event prediction & probabilty

Lecture 1: ML with Deep-NN

Lecture 2: ML with Logistic-Regression

Lecture 3: Validation and Calibration

Lecture 4: compare ROCs

Lecture 5: Nomogram for Logistic-Regression & Cox Regression

Instructors

김지형 M.D.
Orthopeadic Surgeon M.D. Statistical Editor Graphic Editor

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 0 votes
4 stars: 2 votes
5 stars: 6 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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