Time Series: Mastering Time Series Forecasting using Python

1. Ever wondered how weather predictions are made?

2. Curious about estimating the global population in 2050?

3. What if you could predict the expected lifespan of our universe from your laptop at home?

It’s all possible through the art of Time Series Forecasting, utilizing cutting-edge and robust Machine Learning and Deep Learning models. 

You may have searched for many relevant courses, but this one stands out!

This course is an all-encompassing package for beginners, designed to teach time series, data analysis, and forecasting methods from the ground up. Each module is packed with engaging content and a practical approach, accompanied by concise theoretical concepts. At the end of each module, you’ll be given hands-on exercises or quizzes, with solutions available in the following video.   

We’ll start with the theoretical concepts of time series analysis, offering an overview of its features, real-world examples, data collection mechanisms, and its applications. You’ll learn the fundamental benchmark steps for time series forecasting. 

This comprehensive package will equip you with the skills to perform basic to advanced data analysis and visualization for time series data using Numpy, Pandas, and Matplotlib. Python will be our programming language of choice, and we’ll teach it from elementary to advanced levels, ensuring you can implement any machine learning concept.     

This course serves as your guide to leveraging the power of Python for evaluating time series datasets, considering factors like seasonality, trend, noise, autocorrelation, mean over time, correlation, and stationarity. You’ll also master feature engineering, crucial for effective data handling in your forecasting models. Armed with this knowledge, you’ll be prepared to apply Machine Learning and RNNs Models to test, train, and evaluate your forecasts.   

You’ll gain a deep understanding of essential concepts in applied machine learning, including Auto-Regression, Moving Average, ARIMA, Auto-ARIMA, SARIMA, Auto-SARIMA, and SARIMAX for time series forecasting. Additionally, we’ll comprehensively compare the performance of these models.     

Machine learning ranks among the hottest jobs on Glassdoor, with machine learning engineers earning an average salary of over $110,000 in the United States, according to Indeed. Machine Learning offers a rewarding career, allowing you to tackle some of the world’s most intriguing problems.

In the RNNs Module, you’ll delve into building GRU, LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM models. We’ll explore practical concepts like underfitting, overfitting, bias, variance, dropout, the role of dense layers, the impact of batch sizes, and the performance of various activation functions in multi-layer RNN models. Each concept of Recursive Neural Networks (RNNs) will be explained theoretically and implemented using Python.   

Designed for beginners with minimal programming experience, or even those new to Data Analysis, Machine Learning, and RNNs, this comprehensive course rivals others in the field, typically costing thousands of dollars. With over 12 hours of HD video lectures divided into more than 120 videos, along with detailed code notebooks for every topic, it’s one of the most comprehensive courses on Time Series Forecasting with Machine Learning and RNNs on Udemy!

What Sets This Course Apart?

This course not only teaches you the role and impact of time series analysis but also how to apply ML and build RNNs. You’ll understand the training process, the significance of overfitting and underfitting, and gain mastery over Python.

This course is:

• Easy to understand

• Expressive and self-explanatory

• To the point

• Practical, with live coding

• A comprehensive package with three in-depth projects covering the course’s entire content

• Thorough, covering the most advanced RNN models by renowned data scientists

Teaching Is Our Passion:

We emphasize online tutorials that encourage learning by doing. This course takes a practical approach to time series forecasting, using RNNs and Machine Learning Algorithms like ARIMA, SARIMA, and SARIMAX. It includes three projects in the final module, allowing you to experiment and gain practical experience with real-world datasets on Birthrates, Stock Exchange, and COVID-19. We’ve worked tirelessly to ensure you grasp the concepts clearly. Our goal is to give you a solid foundation in the basics before delving into more complex concepts. The course materials include high-quality video content, course notes, meaningful materials, handouts, and evaluation exercises. You can also reach out to our friendly team for any queries.

Course Content:

We’ll teach you how to program with Python and use it for data visualization, data manipulation, and RNNs. Topics covered include:

1. Packages Installation

2. Basic Data Manipulation in Time Series using Python

3. Data Processing for Time Series 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 Advanced Data Analysis, and much more

Enroll in the course and become a time series forecasting expert today!

Who Should Take This Course:

• Individuals looking to advance their skills in machine learning and deep learning

• Those interested in the relationship between data science and time series analysis

• People seeking to implement time series parameters and assess their impact

• Individuals interested in implementing machine learning algorithms for time series forecasting

• Enthusiasts passionate about RNNs, particularly LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM Models

• Machine Learning Practitioners

• Research Scholars

• Data Scientists

What You’ll Learn:

• Concepts, principles, and theories of time series forecasting and its parameters

• Evaluation of machine learning models

• Model and implementation of RNN models for time series forecasting

Why This Course:

• Easy to understand and practical with live coding

• Comprehensive package with three in-depth projects

• Covers advanced RNN models by renowned data scientists

• Emphasizes learning by doing

• Provides a solid foundation in the basics before delving into complex concepts

Unlock the world of time series forecasting with Python and machine learning today!

List of Keywords:

• Time Series Forecasting

• Machine Learning

• Deep Learning

• Python

• ARIMA

• SARIMA

• SARIMAX

• RNN

• LSTM

• Stacked LSTM

• BiLSTM

• Stock Prediction

• Data Analysis

• Data Visualization

• Data Manipulation

Rating Distribution

• 1 stars: 1 votes
• 2 stars: 3 votes
• 3 stars: 7 votes
• 4 stars: 17 votes
• 5 stars: 25 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!