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 Lasso (Least Absolute Shrinkage and Selection Operator) Regression:

  • Like Ridge, it is also a regularization technique used for linear regression models.

  • However, instead of penalizing the L2 norm of coefficients like Ridge, Lasso penalizes the L1 norm (absolute sum).

  • This induces sparsity by forcing some coefficients to become exactly zero, automatically performing variable selection.

  • It selects a parsimonious model with fewer predictors than Ridge by driving unnecessary coefficients to zero.

  • Only the most informative predictors remain, ignoring least important ones and improving interpretability.

  • The degree of sparsity is controlled by the regularization hyperparameter (lambda).

  • Converges faster than Ridge as the cost function is convex but not necessarily differentiable.

  • Commonly used when the true underlying model is sparse in nature i.e. has only a few influential predictors.

  • Tends to give higher prediction accuracy than Ridge when number of features is very large as it selects only relevant features.

So in summary, Lasso squeezes irrelevant coefficients to zero for simplified model interpretation while performing embedded feature selection.

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