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

Bias and variance are important concepts in machine learning that help understand the sources of error in models and how to reduce them:

  • Bias refers to the error due to wrongly estimating the target function. A high bias model cannot capture the trends/patterns and always misses the correct hypothesis.

  • Variance refers to the error due to sensitivity of the model to variations in the training data. A high variance model overfits the noise in the training data and does not generalize well to new data.

  • The bias-variance tradeoff indicates there is an optimal level of complexity for a model – one that balances underfitting and overfitting.

  • High bias and low variance means the model is lacking expressiveness and is too simple to fit the patterns.

  • Low bias and high variance means the model is overly complex and overfitting to training data.

  • Increasing model complexity reduces bias but increases variance. Overly complex models overfit.

  • Reducing complexity reduces variance but increases bias as useful patterns are missed.

Understanding this helps choose the right model complexity, add regularization, get more training data etc. to achieve optimal performance on new data. Balancing bias and variance is crucial in machine learning.

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Muhammad Tufail 4 months ago