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

Elastic Net Regression is a regularization technique that combines the approaches of Lasso and Ridge regression. Here are some key points about Elastic Net:

  • Like Lasso, it performs automatic variable selection by driving some coefficients to zero.

  • But similar to Ridge, it also handles groups of correlated predictors by combining both L1 and L2 penalties.

  • The regularization term is a linear combination of L1 and L2 norms: (1-α)L2 + αL1

  • α tunes the relative contribution of L1 vs L2 penalty between 0-1.

  • α=1 recovers Lasso, α=0 recovers Ridge regression.

  • It overcomes limitations of Lasso by allowing groups of correlated features to be selected together.

  • Performs better than Lasso in situations with highly correlated features.

  • The grouping effect makes coefficients more stable and parameter estimation consistent even with numerous predictors.

  • Useful as a compromise between sparsity of Lasso and grouping effect of Ridge regularization.

  • Hyperparameters like α and lambda need to be tuned for best performance.

So in summary, Elastic Net achieves sparsity and grouping effect simultaneously, making it a flexible regression model for high-dimensional variable selection.

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Muhammad Tufail 4 months ago