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

Cross validation is an important technique used in machine learning model evaluation and selection. Here’s a brief overview:

  • It is used to evaluate how the results of a statistical model will generalize to an independent dataset.

  • The dataset is divided into k number of groups known as folds. Typically k=5 or 10.

  • One fold is used as the validation set to evaluate the model, while the remaining k-1 folds are used to train the model.

  • This process is repeated k times, each time using a different fold as the validation set.

  • The validation results are then averaged over all k trials to get an overall cross-validation estimate of how the model is expected to perform.

  • This helps address overfitting – models that perform well only due to a particular dataset split.

  • Common types include k-fold CV, leave-one-out CV, stratified CV etc. depending on the problem.

  • It provides an almost unbiased estimation of model performance on unseen data without a separate hold-out test set.

  • Popular in model selection to choose hyperparameters that generalize better to new examples.

So in summary, cross validation helps address overfitting and identify how well a model can classify or predict unknown examples. It is a standard evaluation technique in ML.

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