Course Content
Day-2: How to use VScode (an IDE) for Python?
Day-3: Basics of Python Programming
This section will train you for Python programming language
Day-4: Data Visualization and Jupyter Notebooks
You will learn basics of Data Visualization and jupyter notebooks in this section.
Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
Day-10: Data Wrangling and Data Visualization
Data Wrangling and Visualization is an important part of Exploratory Data Analysis, and we are going to learn this.
Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
Day-12,13: Exploratory Data Analysis (EDA)
EDA stands for Exploratory Data Analysis. It refers to the initial investigation and analysis of data to understand the key properties and patterns within the dataset.
Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python
Day-26: How to use Conda Environments?
We are going to learn conda environments and their use in this section
Day-37: Time Series Analysis
In this Section we will learn doing Time Series Analysis in Python.
Day-38: NLP (Natural Language Processing)
In this section we learn basics of NLP
Day-39: git and github
We will learn about git and github
Day-40: Prompt Engineering (ChatGPT for Social Media Handling)
Social media per activae rehna hi sab kuch hy, is main ap ko wohi training milay ge.
Python ka Chilla for Data Science (40 Days of Python for Data Science)
About Lesson

Key things to know about Ridge Regression:

  • It is a regularization technique used to address the problem of multicollinearity in linear regression models.

  • Multicollinearity occurs when independent variables are highly correlated, which increases the variance of the coefficient estimates.

  • Ridge adds a degree of “bias” to the coefficient estimates by imposing a penalty on the size of coefficients.

  • It works by adding the L2 norm (square) of the coefficients to the loss function that is being minimized during regression.

  • This shrinks the large coefficients and distributes the weight more evenly among correlated variables, improving generalization.

  • The shrinkage is controlled by a hyperparameter alpha. Higher alpha means more shrinkage of coefficients towards zero.

  • It helps avoid overfitting and gives more stable and reliable estimates compared to ordinary least squares regression.

  • Coefficients never become exactly zero but are shrunken, so all variables are retained in the model unlike LASSO.

  • Commonly used when there are many correlated predictors to get a stable set of predictors with predictive power.

So in summary, Ridge applies L2 regularization to linear models by imposing a penalty on large coefficients to address multicollinearity.

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Muhammad Tufail 4 months ago
Soo nice