Machine Learning Mastery With Python

Machine Learning Mastery With Python

Discover The Fastest Growing Platform For Professional Machine Learning
With Step-By-Step Tutorials and End-To-End Projects


Machine Learning Mastery With Python

 

$37 USD

The Python ecosystem with scikit-learn and pandas is required for operational machine learning.

Python is the rising platform for professional machine learning because you can use the same code to explore different models in R&D then deploy it directly to production.

In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, learn exactly how to get started and apply machine learning using the Python ecosystem. You get:

  • 178 Page PDF Ebook.
  • 74 Python Recipes using scikit-learn and Pandas.
  • 16 Step-by-Step Lessons.
  • 3 End-to-End Projects.

Start Python Machine Learning Today

Convinced?
Click to jump straight to the packages.

I really have to say that this is the first time I see a clear explanation of how to implement a machine learning project END TO END, and I start now knowing how to put together all the things that I have learn so far.

Machine Learning Mastery with Python is for Developers

….with a little Background in Machine Learning
…and LOTS of Interest in Making Accurate Predictions and Delivering Results

I have carefully designed this Ebook for developers that already know a little background in machine learning and who are interested in discovering how to make accurate predictions and deliver results with machine learning on the Python ecosystem.

Introducing your guide to applied machine learning with Python.

You will discover the step-by-step process that you can use to get started and become good at machine learning for predictive modeling with the Python ecosystem including:

  • Python 2.7
  • SciPy
  • NumPy
  • Matplotlib
  • Pandas
  • Scikit-Learn

This book will lead you from being a developer who is interested in machine learning with Python to a developer who has the resources and capability to work through a new dataset end-to-end using Python and develop accurate predictive models.

After reading this ebook you will know…

  • How to deliver a model that can make accurate predictions on new unseen data.
  • How to complete all subtasks of a predictive modeling problem with Python.
  • How to learn new and different techniques in Python and SciPy.
  • How to work through a small to medium sized dataset end-to-end.
  • How to get help with Python machine learning.

You will know which Python modules, classes and functions to use for common machine learning tasks.

From here you can start to dive into the specifics of the functions, techniques and algorithms used with the goal of learning how to use them better in order to deliver more accurate predictive models, more reliably in less time.

Harness The Rising Power of Python for Machine Learning

The Python ecosystem is growing and may become the dominant platform for machine learning.

The reason is because Python is a general purpose programming language (unlike R or Matlab). This means that you can use the same code for research and development to figure out what model to run as you can in production.

The cost and maintenance efficiencies and benefits of this fact cannot be understated.

Everything You Need To Know to Apply Machine Learning in Python

You Will Get:
16 Lessons on Python Best Practices for Machine Learning Tasks and
3 Project Tutorials that Tie it All Together

This Ebook was written around two themes designed to get you started and using Python for applied machine learning effectively and quickly.

These two parts are Lessons and Projects:

  1. Lessons: Learn how the sub-tasks of machine learning projects map onto Python and the best practice way of working through each task.
  2. Projects: Tie together all of the knowledge from the lessons by working through case study predictive modeling problems.

1. Lessons

Here is an overview of the 16 step-by-step lessons you will complete:

  • Lesson 1: Python Ecosystem for Machine Learning.
  • Lesson 2: Python and SciPy Crash Course.
  • Lesson 3: Load Datasets from CSV.
  • Lesson 4: Understand Data With Descriptive Statistics.
  • Lesson 5: Understand Data With Visualization.
  • Lesson 6: Pre-Process Data.
  • Lesson 7: Feature Selection.
  • Lesson 8: Resampling Methods.
  • Lesson 9: Algorithm Evaluation Metrics.
  • Lesson 10: Spot-Check Classification Algorithms.
  • Lesson 11: Spot-Check Regression Algorithms.
  • Lesson 12: Model Selection.
  • Lesson 13: Pipelines.
  • Lesson 14: Ensemble Methods.
  • Lesson 16: Model Finalization.

Each lesson was designed to be completed in about 30 minutes by the average developer.

2. Projects

Here is an overview of the 3 end-to-end projects you will complete:

  • Project 1: Hello World Project (Iris flowers dataset).
  • Project 2: Regression (Boston House Price dataset).
  • Project 3: Binary Classification (Sonar dataset).

Each project was designed to be completed in about 60 minutes by the average developer.

Master Machine Learning with Python Table of Contents

Master Machine Learning with Python Table of Contents

Here’s Everything You’ll Get…
in Machine Learning Mastery With Python

Hands-On Tutorials

A digital download that contains everything you need, including:

  • Clear descriptions that help you to understand the Python ecosystem for machine learning.
  • Step-by-step Python tutorials to show you exactly how to apply each technique and algorithm.
  • End-to-end Python projects that show you exactly how to tie the pieces together and get a result.
  • Python source code recipes for every example in the book so that you can run the tutorial and project code in seconds.
  • Digital Ebook in PDF format so that you can have the book open side-by-side with the code and see exactly how each example works.

Resources you need to go deeper, when you need to, including:

  • The best sources of information on the Python ecosystem including the Python language, SciPy, NumPy, Matplotlib, Pandas and scikit-learn.
  • The best places online where you can ask your challenging questions and actually get a response.

Foundation tutorials for getting started and data preparation, including:

  • The installation of the Python ecosystem and a shortcut to speed things up.
  • The Python language syntax crash course and how to install the libraries you need.
  • The loading of data from CSV or URL and the important foundation this lays for loading your own data.
  • The calculation of descriptive statistics and the 7 techniques you need to use to understand your data.
  • The visualization of your data and the 5 plots you need to get insights into your predictive modeling problem.
  • The data preparation process and the 4 methods you must consider before modeling your problem.
  • The selection of features and the 4 main methods that you can use to cut down the size of your data.

Practical Projects

Lessons on applied machine learning with the Python platform, including:

  • The importance of estimating model performance on unseen data and 4 techniques you need to do so.
  • The metrics used to measure model performance and which to use for regression and classification problems.
  • The necessity of not assuming a solution, the spot checking method and the linear and nonlinear algorithm recipes you can use immediately.
  • The comparison and selection of trained models and the summarization of results and plotting technique to help you choose.
  • The organization of machine learning tasks into workflows and the 2 main types you need to know about.
  • The improvement of results with ensemble methods and the 3 main techniques you can use on your projects.
  • The tuning of machine learning algorithm hyperparameters and 2 different methods to apply.
  • The finalization of a trained model to save it to file and later load it to make new predictions on unseen data.

Projects that tie together the lessons into end-to-end sequence to deliver a result, including:

  • The project template that you can use to jump-start any predictive modeling problem in Python with scikit-learn.
  • The first machine learning project in Python for multi-class classification that provides a gentle guide to the template and how the lessons tie together.
  • The regression project to predict house prices that shows the improvements of data transforms, tuning and ensemble methods.
  • The binary classification problem that predicts the difference between rocks and mines from sonar data showing the judicious use of algorithm tuning and ensemble algorithms.
  • The selection of extra predictive modeling projects that can be used for ongoing practice to build up a portfolio of work.

What More Do You Need?

Take a Seek Peek Inside The Ebook

Master Machine Learning with Python Page2

Master Machine Learning with Python - Page1

Master Machine Learning with Python - Page3

BONUS: Python Machine Learning Script Library
…you get 74 Fully Working Python Scripts

Each recipe presented in the book is standalone meaning that you can copy and paste it into your project and use it immediately.

You get one Python script (.py) for each example provided in the book.

Your Python Script Library is organized by chapter coving topics such as:

  • Analyze Data
    • Data Loading
    • Data Summary
    • Data Visualization
  • Prepare Data
    • Data Preparation
    • Data Feature Selection
  • Algorithms
    • Classification
    • Regression
  • Evaluate Algorithms
    • Algorithm Comparison
    • Algorithm Metrics
    • Algorithm Pipeline
    • Algorithm Resampling
  • Improve Results
    • Algorithm Tuning
    • Ensembles
  • Finalize Model

This means that you can follow along and compare your answers to a known working implementation of each algorithm in the provided Python files.

This helps a lot to speed up your progress when working through the details of a specific task.

Python Machine Learning Sample Code

Python Machine Learning Sample Code

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.

Get Your Sample Chapter

Download PDFWant to take a look at the Ebook? Download a free sample chapter PDF.

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Check Out What Customers Are Saying:

Extremely Helpful for actually immediately implementing ML to any applications you may have. This book actually provides examples and recipes that you can study and learn. This is a book for implentation, it does not necessarily explain the code in depth as far as how it does what it does, but it explains exactly how to use it.

Machine Learning Mastery by Jason Brownlee is an excellent introduction to a highly important and modern topic. The strongest aspect of the book is the “Yes I Can Do This” feeling you will get while going through the text and examples. I feel that the book may be of even more value with some more explanation on what the different sorts of algorithms do, but would nevertheless recommend it to anyone without a technical background who wants to get started.

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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:
"YES!"
...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+ 
...it's boring, math-heavy and you'll probably never finish it.

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

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

OR...

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,
...how 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 2.7.

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 178 pages.

How many example Python scripts are included? A catalog of 74 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 16 lessons and 3 projects and moving fast through the intro and conclusions, you can finish in 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 project from end-to-end. One lesson on each step and 3 example projects that tie it all together.

Are there any additional downloads? Yes. In addition to the download for the Ebook itself, you will have access to a catalog of Python recipes covering each step of the applied machine learning process.