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.
Requirements
• No prior knowledge of Deep Learning, Data Analysis or Maths is needed. We will start from the basics and gradually build your knowledge in the subject.
• A willingness to learn and practice.
• Only basic Python is required.
Description
Comprehensive Course Description
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.
This course is
· Easy to understand.
· Expressive and self-explanatory.
· To the point.
· Practical with live coding.
· A complete package with three in depth projects covering complete course contents.
· Thorough, covering the most advanced and recently discovered RNN models by renowned data scientists.
Teaching Is Our Passion
We focus on creating online tutorials that encourage learning by doing. We aim to provide you with more than a superficial look at time series forecasting with the help of RNNs and Machine Learning Algorithms such as ARIMA, SARIMA and SARIMAX etc. For instance, this course has three projects in the final module which will help you to see for yourself via experimentation the practical implementation of RNNs and ML with advance data analysis on the real-world datasets of Birthrates, Stock Exchange and COVID-19. We have worked extra hard to ensure you understand the concepts clearly. We want you to have a sound understanding of the basics before you move onward to the more complex concepts. The course materials that make certain you accomplish all this include high-quality video content, course notes, meaningful course materials, handouts, and evaluation exercises. You can also get in touch with our friendly team in case of any queries.
Course Content
We'll teach you how to program with Python, how to use it for data visualization, data manipulation and RNNs! Here are just a few of the topics that we will be learning
1. Packages Installation
2. Basic Data Manipulation in Time Series using Python
3. Data Processing for Timeseries Forecasting using Python
4. Machine Learning in Time Series Forecasting using Python
5. Recurrent Neural Networks for Time Series using Python
6. Project 1: COVID-19 Prediction using Machine Learning Algorithms
7. Project 2: Microsoft Corporation Stock Prediction using RNNs
8. Project 3: Birthrate Forecasting using RNNs with Advance Data Analysis and much more
Enroll in the course and become a time series forecasting expert today!
After completing this course successfully, you will be able to
· Relate the concepts and theories for time series forecasting and its parameters.
· Understand evaluate the machine learning models.
· Understand the model and implementation of RNN models for the time series forecasting
Who this course is for
· People who want to advance their skills in machine learning and deep learning.
· People who want to master relation of data science with time series analysis.
· People who want to implement time series parameters and evaluate their impact on it.
· People who want to implement machine learning algorithms for time series forecasting.
· Individuals who are passionate about RNNs specially, LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM Models.
· Machine Learning Practitioners.
· Research Scholars.
· Data Scientists.
Who this course is for
• People who want to advance their skills in machine learning and deep learning.
• People who want to master relation of data science with time series analysis.
• People who want to implement time series parameters and evaluate their impact on it.
• People who want to implement machine learning algorithms for time series forecasting.
• Individuals who are passionate about RNNs specially, LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM Models.
• Machine Learning Practitioners.
• Research Scholars.
• Data Scientists.

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