In this module, we will dive deep into the different aspects of time series data, covering its features, types, and the stages involved in forecasting. You will also learn about the integration of machine learning and neural networks, such as RNNs, in time series prediction.

In this module, we will focus on the essential skills needed to manipulate and visualize time series data using Python. You will learn how to slice, index, and visualize both single and multiple features to better understand time series datasets.

In this module, we will cover key data processing tasks required to prepare your dataset for forecasting. You will work through stationarity checks, noise reduction, and resampling, all essential steps for building a reliable forecasting model.

In this module, we will introduce machine learning approaches for time series forecasting, including ARIMA and SARIMA models. You will learn to implement these techniques using Python and assess their effectiveness through evaluation metrics.

In this module, we will focus on Recurrent Neural Networks (RNNs), specifically LSTM and BiLSTM models, for time series forecasting. You will explore how these deep learning models are applied and optimized for accurate predictions.

In this module, we will guide you through a hands-on project predicting COVID-19 positive cases using machine learning algorithms. You will process and analyze the dataset, followed by the implementation of ARIMA and SARIMA models for future predictions.

In this project, we will focus on predicting Microsoft Corporation's stock prices using RNN models. You will learn how to preprocess the dataset, visualize data patterns, and use LSTM and BiLSTM models for stock price forecasting.

In this project, you will use deep learning techniques to forecast birth rates over time. You will analyze and manipulate the dataset, then apply advanced RNN models like LSTM and BiLSTM to predict future birth rate trends.