Machine Learning Pipeline
Machine Learning Pipeline
Experience Gain
About the Book
By reading this book you will learn how to build a machine learning pipeline for a real-life projects, whatever stopped you before from mastering machine learning with python you can easily overcome it with this book, because of easy step-by-step, and example-oriented approach that will help you apply the most straightforward and effective tools to both demonstrative and real-world problems and datasets.
Note: This book is for free and and will always be, so get your copy and we will be glade if you supported us by either with your feedback or some donation.
This book will cover the following:
Part one: Introduction
- an introduction to what is data science tools and how to setup it.
- an introduction to data science pipelines and define it and how to scale it.
- an introduction to machine learning pipelines and how learning is done.
- building a small project to make sure that you are now understand the meaning of pipelines.
Part two: Data
- defining data, types of data and levels of data, because it will help us to understand the data.
- understand and cleaning data process, since it's a very important step in the pipeline
- resampling data to create train-set and test-set, and splitting techniques.
- feature engineering and selection, and that's because not all time the needed variable is visible to us.
Part three: supervised leaning
an introduction to machine learning algorithms, how it works, and it's evaluation. And this part will cover the following algorithms:
- Linear Regression.
- Logistic Regression.
- Decision Trees.
- Support Vector Machines.
License
About the Editor
Bundles that include this book
Table of Contents
- Part One: Introduction
-
Chapter 1. Introduction
- 1.1 Introduction to data science and python.
- 1.2 Installing python.
- 1.3 Introducing IPython & Jupyter.
- 1.4 Summary.
-
Chapter 2. A Nice Tour Through Data Science Pipeline
- 2.1 What is Data Science?
- 2.2 A bird's-eye view of the pipeline.
- 2.3 Summary.
-
Chapter 3. Machine Learning Pipeline
- 3.1 Data.
- 3.2 Goals.
- 3.3 Models.
- 3.4 Features.
- 3.5 Model Evaluation.
- 3.6 Summary.
- Part Two: Data
- Chapter 4. Defining Data
- 4.1 Defining Data.
- 4.2 Why you should read this chapter?
- 4.3 Structured, semi-structured and unstructured data.
- 4.4 Quantitative versus Qualitative data.
- 4.5 Example - Titanic.
- 4.6 Example - world alcohol consumption data.
- 4.7 Divide and Conquer.
- 4.8 Making a checkpoint.
- 4.9 The four levels of data.
- 4.10 The nominal level.
- 4.11 The ordinal level.
- 4.12 Quick recap and check.
- 4.13 The Interval Level.
- 4.14 The Ratio Level.
- 4.15 Summarizing All Levels.
- 4.16 Summary.
-
Chapter 5. Data Cleaning
- 5.1 The data science pipeline revisited
- 5.2 Data loading and preprocessing with pandas.
- 5.3 Missing Data.
- 5.4 Dealing with big datasets.
- 5.5 Accessing other data formats.
- 5.6 Data preprocessing.
- 5.7 Categorical and Text data.
- 5.8 Case Study - Titanic.
- 5.9 Summary.
-
Chapter 6. Data Resampling
- 6.1 Creating training and test sets.
- 6.2 Cross-Validation.
- 6.2.1 Validation set technique.
- 6.2.2 Leave-One-Out Cross-Validation (LOOCV).
- 6.2.3 K-Fold Cross-Validation.
- 6.3 Bootstrap (not added yet).
- 6.4 Summary.
-
Chapter 7. Feature Selection and Feature Engineering.
- 7.1 Scikit-learn datasets.
- 7.2 Feature selection and filtering.
- 7.3 Principal component analysis.
- 7.3.1 Non-negative matrix factorization.
- 7.3.1 Sparse PCA.
- 7.3.1 Kernel PCA.
- 7.4 Atom extraction and dictionary learning.
- 7.5 Summary.
- Part Three: Supervised Learning Algorithms
- Chapter 8. Introduction to Learn ability
-
Chapter 9. Linear Regression
- 9.1 Dataset we will use in the chapter.
- 9.2 Simple Linear Regression.
- 9.3 Example.
- 9.4 Estimating the Parameters.
- 9.4.1 Our Goal.
- 9.4.2 Least Square Criterion.
- 9.4.3 How Least Square Works?
- 9.5 Assessing the Accuracy of the Coefficient Estimates.
- 9.6 Assessing the Accuracy of the Model.
- 9.7 Multiple Linear Regression.
- 9.7.1 Estimating the Regression Coefficients.
- 9.8 Linear regression with scikit-learn.
- 9.8.1 Regressor analytic expression.
- 9.9 Ridge, Lasso, and ElasticNet.
- 9.10 Robust regression with random sample consensus.
- 9.11 Polynomial regression.
- 9.12 Isotonic regression.
- 9.13 Summary.
- Chapter 9. Logistic Regression
Other books by this author
The Leanpub 60 Day 100% Happiness Guarantee
Within 60 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.
Now, this is technically risky for us, since you'll have the book or course files either way. But we're so confident in our products and services, and in our authors and readers, that we're happy to offer a full money back guarantee for everything we sell.
You can only find out how good something is by trying it, and because of our 100% money back guarantee there's literally no risk to do so!
So, there's no reason not to click the Add to Cart button, is there?
See full terms...
Earn $8 on a $10 Purchase, and $16 on a $20 Purchase
We pay 80% royalties on purchases of $7.99 or more, and 80% royalties minus a 50 cent flat fee on purchases between $0.99 and $7.98. You earn $8 on a $10 sale, and $16 on a $20 sale. So, if we sell 5000 non-refunded copies of your book for $20, you'll earn $80,000.
(Yes, some authors have already earned much more than that on Leanpub.)
In fact, authors have earnedover $13 millionwriting, publishing and selling on Leanpub.
Learn more about writing on Leanpub
Free Updates. DRM Free.
If you buy a Leanpub book, you get free updates for as long as the author updates the book! Many authors use Leanpub to publish their books in-progress, while they are writing them. All readers get free updates, regardless of when they bought the book or how much they paid (including free).
Most Leanpub books are available in PDF (for computers) and EPUB (for phones, tablets and Kindle). The formats that a book includes are shown at the top right corner of this page.
Finally, Leanpub books don't have any DRM copy-protection nonsense, so you can easily read them on any supported device.
Learn more about Leanpub's ebook formats and where to read them
