Summary  
Using a Python library to fetch historical data from an API and load it into a DataFrame.

General domain of usage  
Financial data analysis

Loading stock exchange data using the `yfinance` library in Python is demonstrated. `yfinance` provides easy access to historical stock data, which is essential for time series forecasting tasks such as stock price prediction.

- **What is** `yfinance`: it's a Python library that provides an easy way to download historical stock data from Yahoo Finance. It supports various data points such as stock prices, volumes, and dividends, and can fetch data for individual stocks or market indices;
- **Installing `yfinance`**: before using `yfinance`, you need to install the library. You can install it using pip:
  ```python
  ! pip install yfinance
  ```
- **Loading the data**: 
  ```python
  ticker = "GOOGL"  # Example: Google stock ticker
  start_date = "2013-01-01"
  end_date = "2024-12-31"
  stock_data = yf.download(ticker, 
  start=start_date, end=end_date)
  ```

Which of the following columns is NOT included in the data fetched from yfinance?

Dive into advanced deep learning techniques for time series analysis. Learn to structure sequential data, design and implement RNN architectures as LSTM and GRU, and build ensemble models to forecast complex temporal patterns. This course focuses on leveraging computational intelligence to capture intricate dependencies and dynamics in time series data.

Explore advanced deep learning methods for time series forecasting, focusing on LSTM architectures, sequence modeling, and ensemble techniques.

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