Course Content
Day-2: How to use VScode (an IDE) for Python?
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Day-3: Basics of Python Programming
This section will train you for Python programming language
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Day-4: Data Visualization and Jupyter Notebooks
You will learn basics of Data Visualization and jupyter notebooks in this section.
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Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
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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.
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Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
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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.
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Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python
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Day-26: How to use Conda Environments?
We are going to learn conda environments and their use in this section
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Day-37: Time Series Analysis
In this Section we will learn doing Time Series Analysis in Python.
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Day-38: NLP (Natural Language Processing)
In this section we learn basics of NLP
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Day-39: git and github
We will learn about git and github
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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.
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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
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