Book Image

Python for Geeks

By : Muhammad Asif
Book Image

Python for Geeks

By: Muhammad Asif

Overview of this book

Python is a multipurpose language that can be used for multiple use cases. Python for Geeks will teach you how to advance in your career with the help of expert tips and tricks. You'll start by exploring the different ways of using Python optimally, both from the design and implementation point of view. Next, you'll understand the life cycle of a large-scale Python project. As you advance, you'll focus on different ways of creating an elegant design by modularizing a Python project and learn best practices and design patterns for using Python. You'll also discover how to scale out Python beyond a single thread and how to implement multiprocessing and multithreading in Python. In addition to this, you'll understand how you can not only use Python to deploy on a single machine but also use clusters in private as well as in public cloud computing environments. You'll then explore data processing techniques, focus on reusable, scalable data pipelines, and learn how to use these advanced techniques for network automation, serverless functions, and machine learning. Finally, you'll focus on strategizing web development design using the techniques and best practices covered in the book. By the end of this Python book, you'll be able to do some serious Python programming for large-scale complex projects.
Table of Contents (20 chapters)
1
Section 1: Python, beyond the Basics
5
Section 2: Advanced Programming Concepts
9
Section 3: Scaling beyond a Single Thread
13
Section 4: Using Python for Web, Cloud, and Network Use Cases

Answers

  1. In supervised learning, we provide the desired output with the training data. The desired output is not included as part of the training data for unsupervised learning.
  2. Cross-validation is a statistical technique that's used to measure the performance of an ML model. In k-fold cross-validation, we divide the data into k folds or slices. We train our model using the k-1 slices of the dataset and test the accuracy of the model using the kth slice. We repeat this process until each kth slice is used as testing data. The cross-validation accuracy of the model is computed by taking the average of the accuracy of all the models we built through k iterations.
  3. RandomizedSearchCV is a tool that's available with scikit-learn for applying cross-validation functionality to an ML model for randomly selected hyperparameters. GridSearchCV provides similar functionality to RandomizedSearchCV, except that it validates the model for all the combinations of hyperparameter...