2016 | English | PDF | ISBN: N/A | 163 Page
Finally Pull Back The Curtain And See How They Work With
Clear Descriptions, Step-By-Step Tutorials and Working Examples in Spreadsheeds

You must understand the algorithms to get good (and be recognized as being good) at machine learning.

In this mega Ebook is written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step.

Clear Descriptions and Step-By-Step Tutorials
Ebook with 163 pages in PDF format.
10 top algorithms described with clear descriptions.
12 step-by-step tutorials with worked examples.
16 spreadsheets with working implementations.

You Learn Best By Implementing Algorithms From Scratch
…But You Need Help With The First Step: The Math
Developers Learn Fast By Trying Things Out…

I’m a developer and I feel like I don’t really understand something until I can implement it from scratch. I need to understand each piece of it in order to understand the whole. The same thing applies to machine learning algorithms.

If you are anything like me, you will not feel comfortable about machine learning algorithms until you can implement them from scratch, step-by-step.

The Math Can Really Slow You Down (…and Sap Your Motivation)

The problem is, machine learning algorithms are not like other algorithms you may have implemented like sorting. They are always described using complex mathematics with a mixture of probability, statistics and linear algebra.

You need to be able to get past the mathematical descriptions in order to implement the algorithms from scratch, but you don’t have the time to spend 3 years studying mathematics to get there.

You Really Need Clear Worked Examples (…step-by-step with real numbers)

Machine learning algorithms would be much easier to understand if someone simplified the math and gave clear worked examples showing how real numbers get plugged into the equations and what numbers to expect as outputs. With clear inputs and outputs we as developers can reproduce and understand the math.

Even better would be to have worked examples that actually perform all of the calculation required to learn a model from a small sample dataset, and all of the calculations required to make predictions from the learned model.

Master Machine Learning Algorithms is for Developers
….with NO Background in Math
…and LOTS of Interest in Machine Learning
Introducing the “Master Machine Learning Algorithms” Ebook. This Ebook was carefully designed to provide a gentle introduction of the procedures to learn models from data and make predictions from data 10 popular and useful supervised machine learning algorithms used for predictive modeling.

Each algorithm includes a one or more step-by-step tutorials explaining exactly how to plug in numbers into each equation and what numbers to expect as output. These tutorials will guide you step-by-step through the processes for creating models from training data and making predictions.

More than that, each tutorial is designed to be completed in a spreadsheet. Spreadsheets are the simplest way to automate calculations and anyone can use a spreadsheet, from beginners, to professional developers to hard core programmers.

If you can understand how a machine learning algorithm works in a spreadsheet then you really know how it works. You can then implement it in any programming language you wish or use your newfound knowledge and understanding to achieve better performance from the algorithms in practice.

Everything You Need To Know About 10 Top Machine Learning Algorithms
You Will Get:
6 Import Background Lessons
11 Clear Algorithm Descriptions
12 Step-By-Step Algorithm Tutorials
This ebook was written around two themes designed to help you understand machine learning algorithms as quickly as possible.

These two parts are Algorithm Descriptions and Algorithm Tutorials:

Algorithm Descriptions: Discover exactly what each algorithm is and generally how it works from a high-level.
Algorithm Tutorials: Climb inside each machine learning algorithm and work through a case study to see how it learns and makes predictions.