Dear users, due to the protests and the disorderly situation in Iran, there is a possibility of Internet interruption in Iran. We apologize in advance if there is a problem in updating the site. MahsaAmini WomanLifeFreedom
What you would learn in A Practical Approach to Timeseries Forecasting using Python course?
This is a comprehensive program for those who are just beginning to understand time series and data analysis and forecasting techniques from beginning to finish. Each module is packed with captivating content, and an efficient approach is utilized alongside brief theoretical concepts. After each module, we will assign the students a hands-on activity or quiz. The solution to the questions is accessible in the following video.
We'll begin by introducing the theories of time series analysis and, following a brief description of its functions and examples, the mechanism behind collecting data from time series and its use within the actual world. We will go through the fundamental methods for computing time-series forecasting.
This comprehensive package will allow learners to understand the basic techniques for advanced visualization and analysis of data about time series using Numpy, Pandas, and Matplotlib. We'll use Python as a programming language during this class, and it is the most sought-after language of the moment when we talk about machine learning. Python will be taught from the beginning until an intermediate level to ensure that every machine learning theory can be implemented.
This course will teach you how to use powerful Python to analyze your time series data sets by analyzing their trend, seasonality, noise, autocorrelation correlation, mean over time, and stationarity. Furthermore, the significance of feature engineering in your data will help you become proficient in performing excellent analyses of your data to build forecasting models. With this information, you'll be able to create your time series dataset to use Machine Learning and RNNs Models to evaluate, test, and assess the forecasted score.
Learn all the essential and fundamental concepts needed for the model of machine learning applied to the real world, such as Moving Average, Auto-Regression ARIMA Auto-ARIMA, SARIMA Auto-SARIMA, and SARIMAX about Time Series Forecasting. Additionally, the performance comparability between these models will be thoroughly discussed.
Machine learning has been listed in the top ten top jobs on Glassdoor as well as the average wage of a machine-learning engineer is more than one hundred thousand dollars within the United States, according to Indeed! Machine Learning is a lucrative job that allows you to solve the most intriguing challenges!
The RNNs Module We'll be learning the complete process of creating GRU, LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM models, as well as the fundamental concepts of bias, bias, overfitting, underfitting, dropping out, variance the role of dense layers, the impact of batch sizes, as well as the performance of various activation methods on RNN models of various layers. Every concept of "Recursive Neural Networks" (RNNs) will be taught through a theoretical approach and will be implemented with Python.
This course is intended for those who are new to programming but have previous programming experience or are naive about Data Analysis, ML, and RNNs!
Learn the fundamentals of Time Series Analysis and Forecasting.
Learn the fundamentals of Data Analysing Techniques and how to Handle Time Series Forecasting.
Learn how to apply the basic concepts of Data Visualization Techniques using Matplotlib
* Learn to evaluate and analyze the Time Series Forecasting Parameters, i.e., Trend, Seasonality, Stationarity, etc.
Learn to calculate and visualize the autocorrelation and the mean over time, standard deviation, and gaussian noise in time series datasets.
* Understand how to evaluate the effectiveness of machine learning applied to Time Series Forecasting
Learn to use Machine Learning Techniques for Time Series Forecasting, i.e., auto Regression, ARIMA, Auto ARIMA, SARIMA, and SARIMAX
Learn the basics about RNN models, i.e., GRU LSTM, BiLSTM
* Learn how to build LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM models to forecast time series.
Learn about the effects of overfitting and underfitting Bias and Variance on the effectiveness of RNN Models
* Learn to use the ML and RNN Models by completing three top-of-the-line projects.
* And many pluses...
Download A Practical Approach to Timeseries Forecasting using Python from below links NOW!
Write your comment!
Access Permission Error
You do not have access to this product!
Dear User! To download this file(s) you need to purchase this product or subscribe to one of our VIP plans.