Summary  
This chapter covers using a Python library to fetch historical time series data from an external API—demonstrating how to install the library, import it, and call its download function with ticker symbols and date ranges.  

General domain of usage  
Financial time series analysis (e.g., stock price forecasting)

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?

Master Recurrent neural networks and their advanced variants like LSTMs and GRUs using PyTorch. Gain hands-on experience processing sequential data for practical applications. Apply these powerful models to tackle real-world challenges in time series forecasting and various Natural language processing tasks.

Covers the limitations of traditional neural networks for sequential data and introduces the fundamentals of Recurrent Neural Networks. Explains RNN architecture, types, and step-by-step implementation through basic examples and a coding challenge.

Explores common training challenges such as vanishing and exploding gradients. Introduces advanced RNN variants including LSTM and GRU, highlighting their internal mechanisms and use cases, with practical implementation examples for each.

Focuses on processing and forecasting time series data using RNN-based models. Includes data loading, preprocessing techniques, model training, and performance evaluation, with emphasis on comparing LSTM and GRU architectures.

Demonstrates the application of RNNs to text classification tasks. Covers core NLP concepts, text encoding methods, data preparation steps, and construction of an LSTM-based model for sentiment prediction.

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