Machine Learning Algorithms From Scratch: With Python

Machine Learning Algorithms From Scratch

Discover How to Code Machine Algorithms
From First Principles With Pure Python and
Use them on Real-World Datasets

Machine Learning Algorithms From Scratch


$37 USD

You must understand algorithms to get good at machine learning.

The problem is that they are only ever explained using Math. No longer.

In this mega Ebook 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.

Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch.

  • 234 Page PDF Ebook.
  • 12 Top Algorithms.
  • 66 Python Recipes.
  • 18 Step-by-Step Tutorials.

No Math. No Libraries. No Hidden Details.

Click to jump straight to the packages.

You Learn Best By Implementing Algorithms From Scratch
…But You Need Help With The First Step

Developers Learn Best By Trying Things Out…

If you’re like me, you don’t really understand something until you can implement it from scratch.

You need to understand each piece before you can understand the whole thing.

The same applies to machine learning algorithms.

You won’t feel like you really understand machine learning algorithms until you can put them together yourself.

Without the tools and fancy libraries.

Math Can Really Slow You Down…

The problem is, machine learning algorithms are only ever described using math.

It’s all Greek letters, it’s a pain to read and understand and this can really sap your motivation.

You simply don’t have the time to study 4 years of advanced math just to implement some algorithms.

You don’t need a degree in computer science to implement bubble sort and understand how it works. You just read a simple explanation and implement it in a few lines of code.

Why can’t implementing machine learning algorithms be the same?
(it can!)

You Need Clear Step-By-Step Tutorials…

What is missing is a set of step-by-step tutorials that lay it all out.

You don’t need to know the mathematical reason why machine learning algorithms work. You need a clear explanation of how they work so you can turn that into code.

You need each step of the learning and prediction spelled out super simple so you can write a small function that does the same thing and write a little test to confirm it works.

It’s the same way we learn anything when programming. Piece-by-piece.

Put a few of these pieces together and you have a world-class machine learning algorithm.

Introducing: “Machine Learning Algorithms From Scratch

This is the book that I wish I had when starting out.

It is designed for exactly the way developers like you learn.

The book works through how to write small functions to load data and prepare it for learning.

There are tutorials on how to evaluate predictions and evaluate the performance of machine learning models.

Then there’s a suite of tutorials on how to implement linear, nonlinear and even ensemble machine learning algorithms from scratch.

  • Each tutorial is written in Python. This is the growing and soon to be the dominant programming language for applied machine learning and data science.
  • Each tutorial uses the standard library. There is no NumPy, no SciPy, no Scikit-Learn and no other advanced libraries to hide the details.
  • Each tutorial is standalone. Everything you need to understand and run an algorithm is right there, no flipping back and forth through the book to piece it together yourself.
  • Each tutorial is super simple. Code does not use fancy Python tricks that are hard to read and even harder to understand. I’ve opted for simple loops and the simplest data structures to help even Python novices see exactly what is going on.
  • Each tutorial actually works. Each function is added one at a time with full explanation and spot testing. Nothing is sprung on you and all the code works with sample output for you to compare to.

Machine Learning Algorithms From Scratch‘ is for Python Programmers
…with NO background in Math
…and BIG enthusiasm for machine learning

Machine Learning Algorithms From Scratch was designed for you.

This is the book that you have been looking for.

The book that finally unlocks how machine learning algorithms work.

  • You don’t need the math. Everything is explained in simple words, and we work in the language you do know: code.
  • You don’t need to be a Python master. All the code examples are clear and simple and no confusing Python tricks and clever shortcuts are used.
  • You don’t need to know machine learning. That is why you need this book, to make your start and finally discover how machine learning algorithms actually work.
  • You don’t need a ton of time. Each tutorial was designed for you to complete in 30 minutes to 60 minutes, and you could easily work through the book in 2 weeks at nights and weekend (or one power weekend as some like to do).
  • You don’t need to break the bank. Put back the $100+ machine learning textbooks and get started with a book designed for you in your language of tutorials, explanations and working code.

Let’s take a closer look at the breakdown of what you will discover inside this EBook.

Everything You Need To Know to Code
Machine Learning Algorithms From Scratch With Python

The tutorials were designed to cover the topics needed for applied machine learning projects.

They are presented in 4 main sections:

1. Data Preparation

  • Load Data: How to load and manipulate data from the CSV standard file format.
  • Data Scaling: How to prepare numerical data for learning algorithms.
  • Algorithm Evaluation: Techniques for estimating the performance of algorithms on unseen data.
  • Evaluation Metrics: Scoring methods to evaluate the skill of predictions made on new data.
  • Baseline Models: Techniques that can establish the best worst case from which to improve on a problem.

2. Linear Algorithms

  • Algorithm Test Harness: Drawing together the elements from the previous section to consistently and objectively evaluate different techniques on the same problem.
  • Simple Linear Regression: For predicting numerical values when there is only a single input.
  • Multivariate Linear Regression: For predicting numerical values with more than one input
    (trained using StochasticGradient Descent).
  • Logistic Regression: For predicting a class value on 2 class problems
    (trained using Stochastic Gradient Descent).
  • Perceptron: The simplest type of neural network for classification problems
    (trained using StochasticGradient Descent).

3. Nonlinear Algorithms

  • Classification and Regression Trees: Decision trees, in this case applied to classification problems.
  • Naive Bayes: The very simple application of Bayes’ Theorem to classification problems.
  • k-Nearest Neighbors: For predicting numerical or categorical outcomes directly from training data.
  • Learning Vector Quantization: A type of neural network that is more efficient than k-Nearest Neighbors.
  • Backpropagation: The most widely used type of artificial neural network that underlies the broader field of deep learning.

4. Ensemble Algorithms

  • Bootstrap Aggregation: Also known as bagging that involves an ensemble of decision trees.
  • Random Forest: An extension of bagging that results in faster training and better performance.
  • Stacked Aggregation: An ensemble method also known as stacking or blending that learns how to best combine the perdictions from multiple models.

Below is a snapshot of the complete Table of Contents from the Ebook.

Table of Contents from Machine Learning Algorithms From Scratch

Table of Contents from Machine Learning Algorithms From Scratch


Each Algorithm is Demonstrated 2 Ways

1. Small Contrived Dataset

All algorithms are first developed and demonstrated on a small contrived dataset.

This is so that algorithms can be understood and demonstrated in isolation in a controlled environment.

2. Small Real-World Dataset

Each algorithm is then demonstrated on a small real world dataset from a range of different domains.

Problems were carefully selected from the UCI Machine Learning repository and all datasets are distributed with the book.

What More Do You Need?

Take a Sneak Peek Inside The Ebook

Click image to Enlarge.

Machine Learning Algorithms From Scratch - Sample 1

Machine Learning Algorithms From Scratch - Sample 2

Machine Learning Algorithms From Scratch - Sample 3

BONUS: Machine Learning Algorithm Code Recipes
…you also get 66 fully working machine learning algorithm scripts

You also get a copy of all the code used in the book.

This includes

  • Every small example as we develop machine learning algorithms.
  • Each end-of-tutorial complete working example applied to a real world dataset.

Each code example in the book is standalone.

This means that it will run, as is, with nothing additional required.

The datasets used in each tutorial are also provided with the code, so no hunting down data from the web.

This means:

  • You always have a version that works.
  • You can compare your tutorial code with the finished working version.
  • You can compare your results to the expected results as you work.
  • You have a basis for developing your own extensions to the algorithms.
  • You can adapt code and use them in your own projects immediately.

This is the beginning of your own Machine Learning Code Library, that you can develop further and leverage on your future projects.

In summary:

  • You get one Python code file (.py) for each example in the book.
  • You get the real world dataset (.csv) used in each example in the book.


Machine Learning Algorithms From Scratch Code Library

Machine Learning Algorithms From Scratch Code Library

About The Author

Jason BrownleeHi, 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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What Are Skills in Machine Learning Worth?

Your boss asks you:

Hey, can you build a predictive model for this?

Imagine you had the skills and confidence to say:
...and follow through.

I have been there. It feels great!

How much is that worth to you?

The industry is demanding skills in machine learning.
The market wants people that can deliver results, not write academic papers.

Business knows what these skills are worth and are paying sky-high starting salaries.

A Data Scientists Salary Begins at:
$100,000 to $150,000.
A Machine Learning Engineers Salary is Even Higher.

What Are Your Alternatives?

You made it this far.
You're ready to take action.

But, what are your alternatives? What options are there?

(1) A Theoretical Textbook for $100+'s boring, math-heavy and you'll probably never finish it.

(2) An On-site Boot Camp for $10,000+'s full of young kids, you must travel and it can take months.

(3) A Higher Degree for $100,000+'s expensive, takes years, and you'll be an academic.


For the Hands-On Skills You Get...
And the Speed of Results You See...
And the Low Price You Pay...

Machine Learning Mastery Ebooks are
Amazing Value!

And they work. That's why I offer the money-back guarantee.

You're A Professional

The field moves quickly, long can you wait?

You think you have all the time in the world, but...

  • New methods are devised and algorithms change.
  • New books get released and prices increase.
  • New graduates come along and jobs get filled.

Right Now is the Best Time to make your start.

Bottom-up is Slow and Frustrating,
...don't you want a faster way?

Can you really go on another day, week or month...

  • Scraping ideas and code from incomplete posts.
  • Skimming theory and insight from short videos.
  • Parsing Greek letters from academic textbooks.

Targeted Training is your Shortest Path to a result.

Professionals Use Training To Stay On Top Of Their Field
Get The Training You Need!

You don't want to fall behind or miss the opportunity.

Frequently Asked Questions

What programming language is used? All examples use the Python programming language version 2 or 3. It assumes you have a working Python environment.

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 to read 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.

How many pages it the Ebook? The Ebook is 234 pages.

How many example Python scripts are included? A total of 66 Python recipes are included.

Is there a hard copy physical book? Not at this stage. Ebook only.

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 18 tutorials and moving fast through the intro and conclusions, you can finish in 2-3 weeks. 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 with machine learning in Python.

How much machine learning do I need to know? Only a little. You will be lead step-by-step through the process of working a machine learning projects.

Are there any additional downloads? Yes. In addition to the download for the Ebook itself, you will have access to Python machine learning recipes.