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Introduction to Web Scraping with Python
Web-scraping is an vital strategy, as often as possible utilised in a part of distinctive settings, particularly information science and information mining. Python is to a great extent considered the go-to dialect for web-scraping, the reason being the batteries-included nature of Python. With Python, you’ll be able make a basic scratching script in approximately 15 minutes and in beneath 100 lines of code. So regardless of utilisation, web-scraping could be a expertise that every Python software engineer must have beneath his belt.
Before we begin getting hands-on, we ought to step back and consider what is web-scraping, when ought to we utilize it, and when to maintain a strategic distance from utilising it.
As you as of now know, web-scraping could be a procedure utilized to naturally extricate information from websites. What’s important to get it is, web-scraping may be a to some degree unrefined strategy to extract information from different sources – regularly web pages. In the event that the engineers of web site are liberal sufficient to supply an API to extract data, that would be a parcel more steady and vigorous way to do get to the data. So, as a run the show of thumb, on the off chance that web site gives an API to programmatically recover their information, utilize that. In case an API isn’t accessible, as it were at that point utilize web-scraping.
Be beyond any doubt to moreover comply with any rules or confinements with respect to web scratching for each site you utilize, as a few don’t permit it. With that being clear, let’s hop right into the instructional exercise.
Relationship between bias, variance, and test set MSE
In all three cases, the variance increases and the bias decreases as the method’s flexibility increases. However, the flexibility level corresponding to the optimal test MSE differs considerably among the three data sets, because the squared bias and variance change at different rates in each of the data sets.
Continue reading…Difference Between Classification and Regression in Machine Learning
Classification vs Regression
Classification predictive modeling problems are different from regression predictive modeling problems.
- Classification is the task of predicting a discrete class label.
- Regression is the task of predicting a continuous quantity.
What are “ensemble methods”?
An ensemble methods are technique which uses multiple independent similar or different models and then combine them to produce improved results. Ensemble methods usually produces more accurate solutions than a single model would. The motivation for using ensemble models is to reduce bias or variance.
For e.g. A random forest is an ensemble of multiple decision trees.
Continue reading…Entropy, Information gain, and Gini Index: Decision Tree
The decision tree algorithm is one of the widely used methods for inductive inference. It approximates discrete-valued target functions while being robust to noisy data and learns complex patterns in the data.
The family of decision tree learning algorithms includes algorithms like ID3, CART, ASSISTANT, etc. They are supervised learning algorithms used for both, classification and regression tasks. They classify the instances by sorting down the tree from root to a leaf node that provides the classification of the instance. Each node in the tree represents a test of an attribute of the instance and a branch descending from that node indicates one of the possible values for that attribute. So, classification of an instance starts at a root node of the tree, tests an attribute at this node, then moves down the tree branch corresponding to the value of the attribute. This process is then repeated for the subtree rooted at the new node.
What is bias and variance in machine learning?
- Some models are too simplistic and ignore important relationships in the training data, which could have improved their predictions. Such models are said to have high bias. When a model has high bias, its predictions are consistently off, at least for certain regions of the data if not the whole range. For example, if you try to fit a line to a scatter plot where the data appears to follow a curve-linear pattern, then you can imagine that we won’t have a good fit. Some parts of the plot, the line will fall below the curve and other parts it will be above it, awkwardly trying to follow the trajectory of a curve. Since the line traces out the model’s predictions, then we can see that when the line falls below the curve, the predictions are consistently lower than the ground truth, and vice versa. So when you think of the word bias, think of predictions being consistently off. High-bias models are said to underfit [to the training data], and as such the prediction error is high both on the training data and test data.
Basics of Python Programming!
Python is a popular programming language that is known for its simplicity, readability, and versatility. In this post, we’ll cover the fundamental concepts of Python programming that will help you get started with building your own programs.
Data types and Variables
In Python, there are several data types that you can work with, including integers, floating-point numbers, strings, and lists. Variables are used to store data values in a program. Here are some examples:
# Integer variable
age = 25
# Floating-point variable
price = 12.50
# String variable
name = "John"
# List variable
my_list = [1, 2, 3, 4, 5]
Control Flow
Control flow is used to determine the order in which statements are executed in a program. In Python, you can use if/else statements and loops to control the flow of your program. Here are some examples:
# If statement
if age >= 18:
print("You are an adult")
else:
print("You are a minor")
# While loop
i = 1
while i <= 5:
print(i)
i += 1
# For loop
for item in my_list:
print(item)
Functions
Functions are used to group together a set of statements that perform a specific task. In Python, you can define your own functions and call them later in your program. Here’s an example:
# Define a function
def greet(name):
print("Hello, " + name)
# Call the function
greet("John")
File Handling
File handling is used to read and write data from files in a program. In Python, you can use the open() function to open a file, and the read() and write() methods to read and write data to the file. Here’s an example:
# Open a file
file = open("data.txt", "w")
# Write data to the file
file.write("Hello, world!")
# Close the file
file.close()
Modules and Packages
Modules and packages are used to extend the functionality of Python by providing additional features and tools. In Python, you can use the import statement to import modules and packages into your program. Here’s an example:
# Import the math module
import math
# Use the sqrt function from the math module
x = math.sqrt(25)
print(x)
Conclusion
These are the fundamental concepts of Python programming that you need to know to get started with building your own programs. With these concepts, you can start building more complex programs and take advantage of the vast library of modules and packages available for Python. Happy coding!
Unlocking the Power of Python: A Beginner’s Guide to the World’s Most Versatile Language
Welcome to the world of Python!
Python is a high-level, interpreted programming language that has become a popular choice for developers all around the world. It is known for its simplicity, readability, and versatility, which makes it a great choice for beginners and professionals alike.
Why learn Python?
There are many reasons why you should consider learning Python:
- Easy to learn: Python has a simple syntax and is easy to understand, making it a great language for beginners.
- Versatile: Python can be used for a variety of applications, including web development, data analysis, machine learning, and more.
- High demand: Python is one of the most in-demand programming languages in the world, which means that there are many job opportunities for those who know it.
- Community support: Python has a large and active community of developers who contribute to its development and provide support for those who are learning it.
Getting started with Python
To get started with Python, you’ll need to install it on your computer. You can download the latest version of Python from the official website: https://www.python.org/downloads/
Once you’ve installed Python, you can start writing your first Python program. Here’s an example:
print("Hello, world!")This program simply prints the message “Hello, world!” to the console. To run the program, save it with the .py extension and run it from the command line using the following command:
python filename.py
This will execute the program and print the message to the console.
Python features
Python has many features that make it a great language to learn and use. Some of these features include:
- Simple syntax: Python has a simple and easy-to-learn syntax, which makes it a great language for beginners.
- Interpreted: Python is an interpreted language, which means that you don’t need to compile your code before you can run it.
- Object-oriented: Python supports object-oriented programming, which allows you to write modular and reusable code.
- Extensive libraries: Python has a large number of libraries and modules that make it easy to perform tasks such as data analysis, web development, and more.
- Cross-platform: Python can run on a variety of platforms, including Windows, Mac, and Linux.
Python resources
There are many resources available to help you learn Python, including:
- Python.org: The official website for Python, which provides documentation, tutorials, and downloads.
- Codecademy: An online learning platform that offers interactive Python courses.
- Coursera: An online learning platform that offers Python courses from top universities.
- YouTube: There are many YouTube channels that offer tutorials and guides on Python programming.
- Python documentation: The official documentation for Python, which provides detailed information on the language and its features.
Conclusion
Python is a versatile and easy-to-learn programming language that is becoming increasingly popular among developers. With its simple syntax, extensive libraries, and cross-platform capabilities, it is a great choice for anyone who wants to learn how to code. So, what are you waiting for? Start learning Python today!
