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 points about Linear Regression:

  • It is a simple and commonly used machine learning algorithm for predicting continuous or numeric outcomes.

  • It works by finding a linear relationship between two or more variables – a dependent variable and one or more independent variables.

  • The relationship is modeled using a straight line or linear equation of the form y = mx + c, where m is the slope and c is the y-intercept.

  • It assumes a linear relationship between dependent and independent variables and finds the coefficients that minimize the prediction errors using techniques like Gradient Descent.

  • The coefficients indicate the extent of influence of each independent variable on the dependent variable.

  • It can be used for both explanatory (understanding variables’ influence) and predictive (forecasting new outputs) analytics problems.

  • Common metrics to evaluate the fit of the linear model include R-squared, RMSE, MAE, MAPE etc.

  • Regularization techniques like Ridge and Lasso can help address overfitting due to many features.

  • Many variations exist like Logistic Regression, Multi-variat Linear Regression, Polynomial Regression etc.

So in summary, Linear Regression establishes a linear relationship between variables for prediction and explanation tasks.

Join the conversation
shafiq ahmed 6 months ago
asslaam u alekum maam please tell me how i find P-value method in which video?