4.37 out of 5
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Data Science : Master Machine Learning Without Coding

Learn Fundamentals Of Data Science & Machine Learning With Rapidminer (No Coding). Dataset & Solutions Included.
Instructor:
Ram Prasad
2,623 students enrolled
English [Auto-generated]
Build predictive models using machine learning algorithms without writing a line of software code

Learn To Master Data Science And Machine Learning Without Coding And Earn a 6-Figure Income 

 

Why Data Science and Machine Learning are the Hottest and Most In-Demand Technology Jobs.

 

Data Scientist was recently dubbed “The Sexiest Job of the 21st Century” by Harvard Business Review, and for good reason! 

 

If you’re looking for a fast and effective way to earn a 6-figure income without spending thousands of dollars in training, keep reading to learn about this revolutionary Udemy course. 

 

Glassdoor reports that Data Scientist was named the “Best Job in America for 2016,” which was based on the huge amount of career opportunities and 6-figure average salary.  Business media from Forbes to The New York Times also frequently report about the increasing demand for data scientists.

 

Why is this great news for you? 

The sudden increase in demand for Data Scientists has created an incredible skills gap in the job market.  According to a McKinsey Report, by the end of 2018 the demand for them is expected to be 60% higher than the available talent! 

Machine Learning is the Key to Your High-Earning Future

Leading companies understand that Machine Learning is the future, and are investing millions of dollars into Machine Learning Research. 

Machine Learning is the subset of Artificial Intelligence (AI) that enables computers to learn and perform tasks they haven’t been explicitly programmed to do.

 

Data Scientists and Machine Learning Engineers who are skilled in Machine Learning are even higher in demand across the entire employment spectrum.  Many diverse industries are searching for innovation in the field, and their need for Machine Learning experts and engineers is rapidly increasing.

 

Traditional Machine Learning requires students to know software programming, which enables them to write machine learning algorithms.  But in this groundbreaking Udemy course, you’ll learn Machine Learning without any coding whatsoever.  As a result, it’s much easier and faster to learn! 

 

There’s literally no other course on Udemy that teaches Machine Learning without the need for programming knowledge or coding, using free open source software!

 

A Rare Opportunity to Quickly Learn Data Science and Machine Learning at an Affordable Cost… No Previous Knowledge of Programming Required!

 

Happily, now you can shorten your learning curve and be on your way toward earning a 6-figure income with this groundbreaking Udemy training.

 

Master Machine Learning & Data Science Quickly!

One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast.

A Different & More Effective Approach To Learning Data Science

In this groundbreaking course, you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much easier to grasp the fundamental concepts.

We’ll Build Several Machine Learning Algorithms Together.

I’ll “hand-hold” you as we build from scratch several different types of machine learning algorithms used in the real world, across several industries and I will explain where and how they are used.

Learn Both The Theory & Application Of Machine:

The course will teach you those fundamental concepts of machine learning by implementing practical exercises which are based on real world examples. You will learn the theory, but get hands on practice building these machine learning algorithms.

You’ll also get access to:

·         The datasets used in all the exercises.

·         The solution files of the completed exercises.

·         Cheat sheets to help you remember the fundamental concepts.

 

Join the class now!

Introduction

1
What Will You Get From This Course

This lecture details what you will get out of this machine learning course

2
What This Course Is Not

This lecture details what you will NOT get out of this machine learning course

3
What is Machine Learning and Why is it important?

Learn how computer systems are different from humans and how machine learning is a way to teach computers to behave and learn like a human being.

4
Why Rapidminer?
5
What is the difference between Rapidminer Vs Python or R?

Install Rapidminer

1
Installing Rapidminer

Rapidminer Basics

1
Rapidminer Development Environment Introduction
2
Operators, Extensions, Repository, Parameters, Help
3
Let's Build Our First Basic Process - Introduction
4
Let's Build Our First Basic Process - Hands On
5
End of Rapidminer Basics

Predicting House Prices with Regression Algorithm

1
Before We Start
2
Downloading the Data for your First Regression Machine Learning Model
3
What Is This Dataset?
4
Importing Data
5
Partitioning the Data
6
Building Multiple Linear Regression Machine Learning Model
7
Output Results of Multiple Linear Regression Machine Learning Model
8
Validating Performance of Multiple Linear Regression Machine Learning Model
9
Saving your Work
10
Conclusion - First Simple Regression Machine Learning Model

Handling Data Issues for a Regression Model

1
Downloading the Second Dataset With Issues
2
What Is This Dataset?
3
Importing Data and Identifying Issues
4
Eliminating Fields That Are Not Useful
5
Identifying and Removing Outliers
6
Convert Nominal Data Fields to Numerical Data Fields
7
Building Multiple Linear Regression Machine Learning Model on Cleaned Data

Quiz - Linear Regression

1
Let's Test Your Knowledge

Identifying Prospective Customers Using Classification Algorithm

1
Downloading the Data for your First Classification Machine Learning Model
2
What Is This Data?
3
Importing The Data and Identifying The Issues
4
Convert Data From One Type To Another
5
Handling Missing Values In A Field
6
Set Label Role Using Operator
7
Eliminate Fields That Are Not Useful
8
Partition Data and Build Classification Machine Learning Model
9
Output Results of Classification Machine Learning Model
10
Validating Performance of Classification Machine Learning Model
11
Additional Reading
12
Optimizing the Performance of our Classification Machine Learning Model
13
Additional Reading
14
Conclusion - Classification Machine Learning Algorithm

Quiz - Classification

1
Let's Test Your Knowledge

Segmenting Patient Data Using K-Means Clustering

1
What is Clustering and How is it different from Regression or Classification?
2
Download Data for your Clustering Model Example
3
Story behind the data
4
What is K-Means Clustering?
5
Let's Build our Clustering Algorithm to segment patient data
6
Download Data for New Patients
7
Let's Build our Clustering Algorithm to segment patient data - Continued
8
Conclusion

Quiz - Clustering

1
Let's Test Your Knowledge

Predicting Boiler Failures using Anomaly Detection

1
What is an Anomaly?
2
Anomalies are not always bad
3
Download the Data
4
Story behind the Data
5
Exploring our Data
6
Detecting Anomalies Using Statistical Method
7
Detecting Anomalies Using Distance Based Method
8
Detecting Anomalies Using Density Based Method
9
Detecting Anomalies Using Local Outlier Factor Method
10
Conclusion

Quiz - Anomaly Detection

1
Let's Test Your Knowledge

Bonus - Solutions Files

1
Download the Solutions Files
2
How to Use The Solution Files

Section 10 - Thank You

1
Thank You
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3 hours on-demand video
13 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion