A Practical Approach to Timeseries Forecasting using Python
A Practical Approach to Timeseries Forecasting using Python Course
A Complete Course on Time Series Forecasting using Machine Learning and Recursive Neural Networks with Projects
This course is a complete package for the beginners to learn time series, data analysis and forecasting methods from scratch. Every module has engaging content, a complete practical approach is used in along with brief theoretical concepts. At the end of every module, we assign you a hand-on exercise or quiz, the solution to the quizzes is also available in the next video.
We will be starting with the theoretical concepts of time series analysis, after a brief overview of its features, examples, mechanism of time series data collection and its scope in the real world, we will learn the basic bench marked steps to compute time series forecasting.
What you’ll learn
- Learn the basics of Time Series Analysis and Forecasting.
- Learn basics of Data Analysis Techniques and to Handle Time Series Forecasting.
- Learn to implement the basics of Data Visualization Techniques using Matplotlib
- Learn to Evaluate and Analyze Time Series Forecasting Parameters i.e., Seasonality, Trend, and Stationarity etc.
- Learn to compute and visualize the auto correlation, mean over time, standard deviation and gaussian noise in time series datasets.
- Learn to evaluate applied machine learning in Time Series Forecasting
- Learn to implement Machine Learning Techniques for Time Series Forecasting i.e., Auto Regression, ARIMA, Auto ARIMA, SARIMA, and SARIMAX
- Learn basics of RNN Models i.e., GRU, LSTM, BiLSTM
- Learn to model LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM models for time series forecasting.
- Learn the impact of Overfitting, Underfitting, Bias and Variance on the performance of RNN Models
- Learn how to implement ML and RNN Models with three state-of-the-art projects.
- And much more…
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