Master Machine Learning Algorithms
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.
No Fancy Math and Nowhere for Details to Hide
Click to jump straight to the packages.
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.
1. Algorithm Descriptions
Here is an overview of the linear, nonlinear and ensemble algorithm descriptions:
- Algorithm 1: Gradient Descent.
- Algorithm 2: Linear Regression.
- Algorithm 3: Logistic Regression.
- Algorithm 4: Linear Discriminant Analysis.
- Algorithm 5: Classification and Regression Trees.
- Algorithm 6: Naive Bayes.
- Algorithm 7: K-Nearest Neighbors.
- Algorithm 8: Learning Vector Quantization.
- Algorithm 9: Support Vector Machines.
- Algorithm 10: Bagged Decision Trees and Random Forest.
- Algorithm 11: Boosting and AdaBoost.
2. Algorithm Tutorials
Here is an overview of the step-by-step algorithm tutorials:
- Tutorial 1: Simple Linear Regression using Statistics.
- Tutorial 2: Simple Linear Regression with Gradient Descent.
- Tutorial 3: Logistic Regression with Gradient Descent.
- Tutorial 4: Linear Discriminant Analysis using Statistics.
- Tutorial 5: Classification and Regression Trees with Gini.
- Tutorial 6: Naive Bayes for Categorical Data.
- Tutorial 7: Gaussian Naive Bayes for Real-Valued Data.
- Tutorial 8: K-Nearest Neighbors for Classification.
- Tutorial 9: Learning Vector Quantization for Classification.
- Tutorial 10: Support Vector Machines with Gradient Descent.
- Tutorial 11: Bagged Classification and Regression Trees.
- Tutorial 12: AdaBoost for Classification.
Each tutorial was designed to be completed in about 30 minutes by the average developer.
Here’s Everything You’ll Get In…
Master Machine Learning Algorithms
Algorithm Tutorials and Spreadsheets
A digital download that contains everything you need, including:
- Clear algorithm descriptions that help you to understand the principles that underlie each technique.
- The step-by-step algorithm tutorials show you exactly how each model learns.
- Spreadsheets showing all the examples and calculations from the book, giving you working models to use, learn from and extend.
- Real worked examples so that you can see exactly the numbers in and the numbers out, there’s nowhere for the details to hide.
- Digital Ebook in PDF format so that you can have the book open side-by-side with the spreadsheets and see exactly how each model works.
- The grounding needed to understand algorithm behavior so that you can choose which algorithm to use and diagnose issues.
Important foundation principles for all machine learning algorithms, including:
- The statistical and computer science terms used to describe data, and what they all mean (with pictures).
- The fundamental problem that all machine learning algorithms solve and why it’s important.
- The breakdown of algorithms as parametric and nonparametric and when to use each.
- The important distinction between supervised and unsupervised techniques, and why you should just focus on one.
- The modeling error introduced by bias and variance and how to balance them.
- The poor algorithm performance that caused by overfitting and underfitting, and the techniques to identify and mitigate both.
Resources you need to go deeper, when you need to, including:
- Top machine learning textbooks to deepen your foundation of machine learning algorithms, if you crave more.
- The best forums and question-and-answer websites, places where you can ask your challenging questions and actually get a response.
Linear and Nonlinear Algorithms
Get the most from linear algorithms, the starting point for most projects, including:
- The important functions in Excel, so that there is nothing holding you back from understanding how machine learning algorithms work.
- Tips to get the most out of gradient descent, the core of many algorithms.
- A clever shortcut you can use to greatly simplify linear regression.
- The application of gradient descent to linear and logistic regression for fast and robust learning, and the specific numbers calculated at each step in the process.
- The linear algorithm to use for classifications with more than two classes when logistic regression just won’t do.
Get better performance with more advanced nonlinear algorithms including:
- The procedure for building up a decision tree, and carefully explained cost function you need to know to make it work.
- The Bayes Theorem and the clever simplification that lets you harness the power of probability for predictive modeling.
- The simple little technique that lets you use Bayesian probability on your real-valued data.
- The simple but powerful nearest neighbor method and the problem that can trip you up when you have a lot of data features.
- A clever simplification of nearest neighbors that uses learning rather than “a big dumb database of observations”.
- The simple principle behind the wildly used Support Vector Machines method and how it translates into a real predictive modeling algorithm.
Combine the predictions from many models with ensemble algorithms, including:
- The interesting bootstrap method for estimating quantities and how it can be easily applied as the basis for the Random Forest algorithm, perhaps the most popular machine learning algorithm used today.
- The idea of creating models to fix the mistakes of other models and how this can be scaled up to achieve impressive results.
What More Do You Need?
Take a Sneak Peek Inside The Ebook
Below are some snapshots of select pages from the Ebook. Click to enlarge.
BONUS: Machine Learning Algorithm Spreadsheets
…you also get 16 fully working spreadsheets
Each machine learning algorithm tutorial pressented in the book is standalone, meaning that you can dive in anywhere and pickup where you left off anytime.
You get 16 Excel spreadsheets, one for each machine learning algorithm tutorial in the book.
This means that you can follow along and compare your answers to a known working implementation of each algorithm in the provided spreadsheets.
This helps a lot to speed up you progress when working through the detail of an algorithm.
About The Author
Hi, I'm Jason Brownlee.
I live in Australia with my wife and son and love to write and code.
I have a computer science background as well as a Masters and Ph.D. degree in Artificial Intelligence.
I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones. (yes I have written tons of code that runs operationally)
I get a lot of satisfaction helping developers get started and get really good at machine learning.
I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.
I'm here to help if you ever have any questions. I want you to be awesome at machine learning.
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(1) A Theoretical Textbook for $100+
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Frequently Asked Questions
What programming language is used? None. There is no code. All of the examples use arithmetic and the spreadsheets show you what numbers to expect and how everything works.
Do I need to be a good programmer? Not at all. This Ebook requires that you have a programmers mindset of thinking in procedures and learning by doing. You do not need to be an excellent programmer and you do not need to write one line of code to read this book and learn about machine learning algorithms.
How much math do I need to know? No background in statistics, probability or linear algebra is required. We do not derive any equations. All equations that are listed are tools that we apply by plugging in real numbers and making direct use of the output.
What spreadsheet program do I need? It does not matter at all. You can use Microsoft Excel, LibreOffice Calc, Numbers on the Mac or Google Sheets in Google Drive. If you wanted, you could follow along in all of the tutorials using your favorite programming language like Java, C# or Python.
Do you use macros or programming in the spreadsheets? No. None. All simple arithmetic.
Will I get updates? Yes. You will be notified about updates to the book and code that you can download for free.
Is there any digital rights management (DRM)? No, there is no DRM.
How long will the Ebook take to complete? I recommend reading one chapter per day. With 33 Chapters and moving fast through the intro and conclusions, you can finish in a month. On the other hand, if you are keen you could work through all of the material in a weekend.
What if I need help? The final chapter is titled “Getting More Help” and points to resources that you can use to get more help on machine learning algorithms. This also includes my personal email address, I’m here to help you as much as you need.
How much machine learning do I need to know? Little to none. You will be led through the background principles that underlie all machine learning algorithms. The book does not assume any prior machine learning experience but does expect you to be interested in the topic and eager to work through examples.
How many pages is it? The Ebook is 163 pages.
Is there a hard copy physical book? Not at this stage. Ebook only.
Are there any additional downloads? Yes. In addition to the download for the Ebook itself, you get downloads for spreadsheets that demonstrate every algorithm and every equation in the book. Nothing is hidden.