Data Science : Master Machine Learning Without Coding

Data Science : Master Machine Learning Without Coding, available at $49.99, has an average rating of 4.6, with 63 lectures, 4 quizzes, based on 997 reviews, and has 6635 subscribers.

You will learn about Build predictive models using machine learning algorithms without writing a line of software code This course is ideal for individuals who are Software Programmers or Data Analysts Trying To Switch To A Data Science Career or Business Analysts With No Programming Background Yet Want To Learn Machine Learning or Students Trying to Understand the Basics of Machine Learning or Anyone Who Wants To Understand the Fundamentals Behind Machine Learning It is particularly useful for Software Programmers or Data Analysts Trying To Switch To A Data Science Career or Business Analysts With No Programming Background Yet Want To Learn Machine Learning or Students Trying to Understand the Basics of Machine Learning or Anyone Who Wants To Understand the Fundamentals Behind Machine Learning.

Enroll now: Data Science : Master Machine Learning Without Coding

Summary

Title: Data Science : Master Machine Learning Without Coding

Price: $49.99

Average Rating: 4.6

Number of Lectures: 63

Number of Quizzes: 4

Number of Published Lectures: 63

Number of Published Quizzes: 4

Number of Curriculum Items: 67

Number of Published Curriculum Objects: 67

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Build predictive models using machine learning algorithms without writing a line of software code

Who Should Attend

Software Programmers or Data Analysts Trying To Switch To A Data Science Career
Business Analysts With No Programming Background Yet Want To Learn Machine Learning
Students Trying to Understand the Basics of Machine Learning
Anyone Who Wants To Understand the Fundamentals Behind Machine Learning

Target Audiences

Software Programmers or Data Analysts Trying To Switch To A Data Science Career
Business Analysts With No Programming Background Yet Want To Learn Machine Learning
Students Trying to Understand the Basics of Machine Learning
Anyone Who Wants To Understand the Fundamentals Behind Machine Learning

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

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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.

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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.

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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!�

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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.

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Join the class now!

Course Curriculum

Chapter 1: Introduction

Lecture 1: What Will You Get From This Course

Lecture 2: What This Course Is Not

Lecture 3: What is Machine Learning and Why is it important?

Lecture 4: Why Rapidminer?

Lecture 5: What is the difference between Rapidminer Vs Python or R?

Chapter 2: Install Rapidminer

Lecture 1: Installing Rapidminer

Chapter 3: Rapidminer Basics

Lecture 1: Rapidminer Development Environment Introduction

Lecture 2: Operators, Extensions, Repository, Parameters, Help

Lecture 3: Let's Build Our First Basic Process – Introduction

Lecture 4: Let's Build Our First Basic Process – Hands On

Lecture 5: End of Rapidminer Basics

Chapter 4: Predicting House Prices with Regression Algorithm

Lecture 1: Before We Start

Lecture 2: Downloading the Data for your First Regression Machine Learning Model

Lecture 3: What Is This Dataset?

Lecture 4: Importing Data

Lecture 5: Partitioning the Data

Lecture 6: Building Multiple Linear Regression Machine Learning Model

Lecture 7: Output Results of Multiple Linear Regression Machine Learning Model

Lecture 8: Validating Performance of Multiple Linear Regression Machine Learning Model

Lecture 9: Saving your Work

Lecture 10: Conclusion – First Simple Regression Machine Learning Model

Chapter 5: Handling Data Issues for a Regression Model

Lecture 1: Downloading the Second Dataset With Issues

Lecture 2: What Is This Dataset?

Lecture 3: Importing Data and Identifying Issues

Lecture 4: Eliminating Fields That Are Not Useful

Lecture 5: Identifying and Removing Outliers

Lecture 6: Convert Nominal Data Fields to Numerical Data Fields

Lecture 7: Building Multiple Linear Regression Machine Learning Model on Cleaned Data

Chapter 6: Quiz – Linear Regression

Chapter 7: Identifying Prospective Customers Using Classification Algorithm

Lecture 1: Downloading the Data for your First Classification Machine Learning Model

Lecture 2: What Is This Data?

Lecture 3: Importing The Data and Identifying The Issues

Lecture 4: Convert Data From One Type To Another

Lecture 5: Handling Missing Values In A Field

Lecture 6: Set Label Role Using Operator

Lecture 7: Eliminate Fields That Are Not Useful

Lecture 8: Partition Data and Build Classification Machine Learning Model

Lecture 9: Output Results of Classification Machine Learning Model

Lecture 10: Validating Performance of Classification Machine Learning Model

Lecture 11: Additional Reading

Lecture 12: Optimizing the Performance of our Classification Machine Learning Model

Lecture 13: Additional Reading

Lecture 14: Conclusion – Classification Machine Learning Algorithm

Chapter 8: Quiz – Classification

Chapter 9: Segmenting Patient Data Using K-Means Clustering

Lecture 1: What is Clustering and How is it different from Regression or Classification?

Lecture 2: Download Data for your Clustering Model Example

Lecture 3: Story behind the data

Lecture 4: What is K-Means Clustering?

Lecture 5: Let's Build our Clustering Algorithm to segment patient data

Lecture 6: Download Data for New Patients

Lecture 7: Let's Build our Clustering Algorithm to segment patient data – Continued

Lecture 8: Conclusion

Chapter 10: Quiz – Clustering

Chapter 11: Predicting Boiler Failures using Anomaly Detection

Lecture 1: What is an Anomaly?

Lecture 2: Anomalies are not always bad

Lecture 3: Download the Data

Lecture 4: Story behind the Data

Lecture 5: Exploring our Data

Lecture 6: Detecting Anomalies Using Statistical Method

Lecture 7: Detecting Anomalies Using Distance Based Method

Lecture 8: Detecting Anomalies Using Density Based Method

Lecture 9: Detecting Anomalies Using Local Outlier Factor Method

Lecture 10: Conclusion

Chapter 12: Quiz – Anomaly Detection

Chapter 13: Bonus – Solutions Files

Lecture 1: Download the Solutions Files

Lecture 2: How to Use The Solution Files

Chapter 14: Section 10 – Thank You

Lecture 1: Thank You

Instructors

Ram Prasad
Data Scientist & Certified RapidMiner Analyst

Rating Distribution

1 stars: 6 votes
2 stars: 20 votes
3 stars: 143 votes
4 stars: 382 votes
5 stars: 446 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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