Download Youtube Videos using youtube_dl module in Python
Download Youtube Videos using youtube_dl module in Python

You might want to store some video for future use where it will be required due to lack of internet or saving data or any other reason.

What if we tell you that you can do this exact very thing using Python. Let’s see how to download Youtube videos using youtube_dl module in Python.

youtube-dl is a command-line program to download videos from YouTube.com and a few more sites. It requires the Python interpreter, version 2.6, 2.7, or 3.2+, and it is not platform specific. It should work on your Unix box, on Windows or on macOS. …

In this article, I am going to show you how to create a simple memory game using Tkinter GUI in python.

Python provides various options for developing graphical user interfaces (GUIs). The most important are listed below.

Tkinter − Tkinter is the Python interface to the Tk GUI toolkit shipped with Python. We would look at this option in this article.

wxPython − This is an open-source Python interface for wxWindows http://wxpython.org.

JPython − JPython is a Python port for Java which gives Python scripts seamless access to Java class libraries on the local machine http://www.jython.org.

N.B: There are many other interfaces available, which you can find on the net.

Logistic regression is a generalized linear model that we can use to model or predict categorical outcome variables. For example, we might use logistic regression to predict whether a tumor is malignant or benign, but probably not to predict the price of someone’s house.

How are we going to do so? Well, In Logistic Regression, we’re essentially trying to find the weights(coefficients) that maximize the likelihood of producing our given data and use them to categorize the response variable

Since the likelihood maximization in logistic regression doesn’t have a closed-form solution, I’ll solve the optimization problem with gradient ascent. Gradient ascent is the same as gradient descent, except I’m maximizing instead of minimizing a function. …

In this article, I am going to implement the Gradient Descent Algorithm from scratch,

1- What is Linear Regression?
2- Cost Function or Loss Function
3- Gradient Descent Algorithm
4- Implementing the python code for Gradient Descent Algorithm

Without further ado, let’s dive in.

Linear Regression is a Supervised Learning Algorithm that predicts continuous values, for example: Predicting house prices?
It is a linear approach to modeling the relationship between the dependent variable (Y) and one(Simple Linear Regression) or more(Multiple Linear Regression) independent variables (X).

We will define a linear relationship between these two variables as follows:

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Linear Regression Formula

Cost Function or Loss Function:
difference between Loss function and Cost Function…

Gini Impurity, like Information Gain and Entropy, is just a metric used by Decision Tree Algorithms to measure the quality of a split.

You may like to watch this article as a video? Check it out!

Let’s consider the Dataset below,

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Question: We would like to build a decision tree from the Dataset, how can we choose the best feature to split the data and how can we measure our splitting?

This is where our metric “ Gini Impurity ” comes in, Gini Impurity measures the randomness in our data, how random our data is?

Gini Impurity Formula:

If we have C total classes and p(i) is the probability of picking a datapoint with class i, then the Gini Impurity is calculated…

What Entropy and Information Gain are? and how they are used to decide which attribute should be selected as the decision node?

In a Decision Tree Classification Algorithm, the classification is done by splitting the dataset into a bunch of branches based on decision nodes that determine where to go or what the result will be.

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Decision Tree Classification Flowchart

Once you see the image above, you may ask: “How does a Decision Tree Algorithm decide where to split?” or “How does it decide the best attribute(Decision Node) ?”

Well, to answer that question let’s take a simple example to work on.

suppose you have a dataset that contains 4 features(outlook, temp, humidity, and windy) and one label (play). …

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Idriss Jairi

Computer Science student and passionate

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