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

Sensitivity and specificity are important concepts in machine learning and medical testing.

Sensitivity (True Positive Rate):

  • Refers to the proportion of actual positives that are correctly identified as such.
  • Calculates the percentage of sick people who are correctly identified as having the condition.
  • Sensitivity = TP / (TP + FN)

Specificity (True Negative Rate):

  • Refers to the proportion of actual negatives that are correctly identified.
  • Calculates the percentage of healthy people who are correctly identified as not having the condition.
  • Specificity = TN / (TN + FP)

The key differences between them:

  • Sensitivity focuses on avoiding false negatives, while specificity focuses on avoiding false positives.

  • High sensitivity means fewer sick people will be missed. But it may misclassify some healthy people as sick.

  • High specificity means fewer healthy people will be misclassified. But some sick people may be missed.

  • There is usually a tradeoff – one cannot be increased without decreasing the other.

  • The optimal point depends on the application and cost of false positives vs false negatives.

So in summary, sensitivity measures model ability to correctly detect positives, while specificity measures ability to correctly detect negatives. Both are important metrics for classification tasks.

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