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A Practical Approach to Timeseries Forecasting using Python

A Practical Approach to Timeseries Forecasting using Python

A Complete Course on Time Series Forecasting using Machine Learning and Recursive Neural Networks with Projects

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What you'll learn  A Practical Approach to Timeseries Forecasting using Python

• 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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Comprehensive Course Description: A Practical Approach to Timeseries Forecasting using Python

Have you ever wondered, how weather predictions are made?

Have you ever thought to estimate the global population in 2050!

What if, someone told you that you can predict the expected life of our universe by just sitting next to your laptop in your home.

Its all true! Just because of the Time Series Forecasting pedagogies by using state-of-the-art and robust models of Machine Learning and Deep Learning.

You might have searched for many relevant courses, but this course is different!

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.

This complete package will enable you to learn the basic to advance data analysis and visualization with respect to time series data by using Numpy, Pandas and Matplotlib. We’ll be using Python as a programming language in this course, which is the hottest language nowadays if we talk about machine leaning. Python will be taught from elementary level up to an advanced level so that any machine learning concept can be implemented.

This comprehensive course will be your guide to learning how to use the power of Python to evaluate your time series datasets on the basis of seasonality, trend, noise, autocorrelation, mean overtime, correlation, and on stationarity. Moreover, the impact and role of feature engineering will make you capable of performing exceptional data handling for your forecasting models. Based on this learning you will be able to prepare your time series data for the applied Machine Learning and RNNs Models to test, train and evaluate your forecasted scores.

We’ll learn all the basic and necessary concepts for the applied machine learning models such as Auto-Regression, Moving Average, ARIMA, Auto-ARIMA, SARIMA, Auto-SARIMA and SARIMAX in the perspective of the time series forecasting. Moreover, the performance comparison of these models will also be comprehensively discussed.

Machine learning has been ranked as one of the hottest jobs on Glassdoor, and the average salary of a machine learning engineer is over $110,000 in the United States, according to Indeed! Machine Learning is a rewarding career that allows you to solve some of the world's most interesting problems!

In the RNNs Module, we’ll be learning a complete mechanism of building GRU, LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM models along with the practical concepts of the underfitting, overfitting, bias, variance, dropout, role of dense layers, impact of batch sizes, and performance of different activation functions on the RNN models of multiple different layers. Each concept of the “Recursive Neural Networks” (RNNs) will be taught theoretically and will be implemented using Python.

This course is designed for both beginners with some programming experience or even those who know nothing about Data Analysis, ML and RNNs!

This comprehensive course is comparable to other Time Series Courses using Machine Learning and RNNs courses that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost in only one course! With over 12 hours of HD video lectures that are divided into more than 120 videos and detailed code notebooks for every address this is one of the most comprehensive courses for Time Series Forecasting with Machine Learning and RNNs on Udemy!

Why Should You Enroll in This Course?

The course is crafted to help you understand not only the role and impact of timeseries analysis and how to use ML and build RNNs but also how to train them, understand their impact with the key concept of overfitting and underfitting. This straightforward learning by doing course will help you in mastering the concepts and methodology with regards to Python.