This page contains some Tips and Tricks which are helpful to work with Bash scripts or Terminals. I have mentioned only those tips and tricks which I uses most in my daily routine, BTW you can refer to links which I have shared to know all the tips and tricks.
Introduction To Hypothesis Testing
Null V/S Alternative Hypothesis| Parameter | Null Hypothesis | Alternative Hypothesis |
|---|---|---|
| Definition | A null hypothesis is a statement in which there is no relation between the two variables. | An alternative hypothesis is a statement in which there is some statistical relationship between the two variables. |
| What is it? | Generally, researchers try to reject or disprove it. | Researchers try to accept or prove it. |
| Testing Process | Indirect and Implicit | Direct and Explicit |
| p-value | Null hypothesis is rejected if the p-value is less than the alpha-value; otherwise, it is accepted. | An alternative hypothesis is accepted if the p-value is less than the alpha-value otherwise, it is rejected. |
| Notation | \(H_0\) | \(H_1\) |
| Symbol | Used Equality Symbol (=, ≥, ≤) | Inequality Symbol (≠, <, >) |
Web scraping is a very essential tool for programmers to learn to gather data from websites. Specifically, for Data Scientists web scraping is goto tool to gather data from websites. We can use bs4.BeautifulSoup or selenium in Python to scrape any website.
You can see some of my projects where I scraped websites like 99acres.com, flipkart.com, housing.com and gather useful data for my Data Science projects like arv-anshul/campusx-real-estate.
I have learned Web Scraping from YouTube only.
Tree based models and Regression models are widely used Machine Learning models. So more you know about them is better for you. Also, many concepts from these models are borrowed by advance Machine Learning models like Gradient Boosting, XGBoost, etc.
These models are also great choice for :fontawesome-user-tie: interviewers so from these models they ask many interview questions. This blog mainly focuses on tree based models.
Docker is a platform designed to simplify the process of creating, deploying, and managing applications using containers. Containers enable developers to package an application with all its dependencies into a standardized unit for seamless deployment across different environments.
Components of Docker