This is the code repository for Machine Learning Quick Reference, published by Packt.
Quick and essential machine learning hacks for training smart data models
Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered.
This book covers the following exciting features:
- Get a quick rundown of model selection, statistical modeling, and cross-validation
- Choose the best machine learning algorithm to solve your problem
- Explore kernel learning, neural networks, and time-series analysis
- Train deep learning models and optimize them for maximum performance
- Briefly cover Bayesian techniques and sentiment analysis in your NLP solution
- Implement probabilistic graphical models and causal inferences
- Measure and optimize the performance of your machine learning models
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
#using "resample" function generate a bootstrap sample
boot_samp = resample(dataset, replace=True, n_samples=5, random_state=1)
Following is what you need for this book:
If you’re a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you’re an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You’ll need some exposure to machine learning to get the best out of this book..
With the following software and hardware list you can run all code files present in the book (Chapter 1-15).
| Chapter | Software required | OS required |
|---|---|---|
| 1-10 | ≥ Python 3.5 | Windows, Mac OS X, and Linux (Any) |
| 9-10 | Rstudio | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Rahul Kumar has got more than 10 years of experience in the space of Data Science and Artificial Intelligence. His expertise lies in the machine learning and deep learning arena. He is known to be a seasoned professional in the area of Business Consulting and Business Problem Solving, fuelled by his proficiency in machine learning and deep learning. He has been associated with organizations such as Mercedes-Benz Research and Development (India), Fidelity Investments, Royal Bank of Scotland among others. He has accumulated a diverse exposure through industries like BFSI, telecom and automobile. Rahul has also got papers published in IIM and IISc Journals.
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