Lean Six Sigma Green Belt Basics Gain Solid Understanding

We are so grateful to everyone for their support of this new course. We are thrilled to have more than 20,000 students from over 160 countries. File Size: 2.1GB

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Lean Six Sigma Green Belt Basics Gain Solid Understanding

Course most recently updated Nov 2018!

We are grateful for all the support and encouragement we have received regarding this course. We are thrilled to have more than 20,000 students from over 160 countries. It’s truly a blessing to see so many thoughtful and positive reviews. It’s a true honor to share this important topic in a clear, understandable manner with everyday people.

I am excited to announce that real closed captions have been created for all course material. They are great for ESL students . I’ve got you covered.

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But most importantly:

To take this course “real”, we’ve expanded. The course expanded from 41 lectures and 8 sections to 62 lectures, and 15 sections in November 2018. We hope you enjoy this new content!

Discover the secrets to understanding Machine Learning for Data Science!

This is the introductory course. “Backyard Data Scientist” It will take you through the jungle of Machine Learning for Data Science. This introductory course is accessible to all and explains Machine Learning. It also explains where it fits into the Data Science framework. “techno sphere around us”, why it’s important now, and how it will dramatically change our world today and for days to come.

This exotic journey will incorporate the core concepts:

Computer science as a train wreck and computer science as a way to make sense of it.

A data explanation that will make you see data everywhere you look

One of the “greatest lies” Ever sold about the future in computer science.

This is a real explanation of Big Data and how to avoid falling for the marketing hype.

Artificial intelligence: What does it mean? Can a computer think? What is the best way for computers to do tasks like playing games or navigate with a GPS?

What is Machine Learning? And if a computer can think – can it learn?

Data Science: How does it relate to magical unicorns?

Computer Science, Artificial Intelligence Machine Learning, Machine Learning, Big Data, and Data Science are interrelated.

We’ll then explore the past and the future while touching on the importance, impacts and examples of Machine Learning for Data Science:

This is how a perfect storm of data and Machine Learning algorithms has come together to make it so important.

We’ll actually make sense of how computer technology has changed over time while covering off a journey from 1956 to 2014. Are you a supercomputer in your house? It’s possible to be shocked at the truth.

We’ll discuss the kinds of problems Machine Learning solves, and visually explain regression, clustering and classification in a way that will intuitively make sense.

Most importantly we’ll show how this is changing our lives. Not just the lives of business leaders, but most importantly…you too!

To make sense of the Machine part of Machine Learning, we’ll explore the Machine Learning process:

How can you use Machine Learning to solve problems and what five steps are necessary for success?

Machine Learning can help you ask the right question.

Identifying, obtaining and preparing the right data … and dealing with dirty data!

How each mess looks “unique” But that neat data is like family!

Machine Learning algorithms with exotic names: How to identify them and apply them “Decision Trees”, “Neural Networks” “K’s Nearest Neighbors” And “Naive Bayesian Classifiers”

Here are the top pitfalls to avoid, and how to tune your Machine Learning models for Data Science success.

The final section of this course will prepare students to start their future journey into Machine Learning and Data Science. We’ll explore:

How to get started with Machine Learning without losing sight of your purpose.

Data Scientists use what equipment? The answer might surprise!

These are the top five data science tools, some of which may surprise you.

And for each of the top five tools – we’ll explain what they are, and how to get started using them.

And we’ll close off with some cautionary tales, so you can be the most successful you can be in applying Machine Learning to Data Science problems.

Bonus Course To learn more, “really real”, I’ve included a bonus course!

Most importantly in the bonus course I’ll include information at the end of every section titled “Further Magic to Explore” This will enable you to continue learning.

In this bonus course we’ll explore:

A real-life Machine Learning Example in Titanic proportions. That’s right – we are going to predict survivability onboard the Titanic!

Anaconda Jupyter can be used with python3.x

A crash course in Python – This course covers all the fundamental concepts of Python and helps you understand the code examples. Get the free cheat sheet!

Learn Python by doing it yourself! Interactively, with scripts and with Jupyter

Basics Learn how to use Jupyter notebooks

Reviewing and reinforcing core concepts of Machine Learning (that we’ll soon apply!)

Foundations for the essential Machine Learning and Data Science Modules:

NumPy – An Array Implementation

Pandas – The Python Data Analysis Library

Matplotlib – A plotting library which produces quality figures in a variety of formats

SciPy – The fundamental Package for scientific computing in Python

Scikit-Learn – Simple and efficient tools data mining, data analysis, and Machine Learning

In the titanic hands on example we’ll follow all the steps of the Machine Learning workflow throughout:

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1. Asking the right questions.

2. Recognizing, obtaining and preparing the correct data

3. How to identify and apply a Machine Learning algorithm

4. Evaluation of the performance of your model and adjustment

5. Presenting and using the model

We’ll also see a real world example of problems in Machine learning, including underfit and overfit.

The bonus course ends with a conclusion.

I invite you to join me, Backyard Data Scientist, on an amazing journey to unlock the secrets of Machine Learning for Data Science for everyday people… just like you!

Sign up right now, and we’ll see you – on the other side!
Who is this course for?

This course is necessary before you can load Python or R. This course introduces you to the Fundamentals that you will need before you can start learning. “Hands on”.
Anyone interested in learning more about Machine Learning and Data Science.
You!
These are the adventurous people who are willing to take on the world of Data Science & Machine Learning.

 

Here’s what you can expect in the new book Lean Six Sigma Green Belt Basics Gain Solid Understanding