Udemy – Introduction to Time Series with Python (2023) 2023-7 – Download

Description

Course Introduction to Time Series with Python 2023. Are you interested in the topic of time series? Then this course is right for you! This course is designed by a software engineer. With the experience and knowledge I have gained over the years, I can share my knowledge and help you learn complex theory, algorithms, and coding libraries easily. I will introduce you to the concept of time series and how to apply machine learning techniques to time series. With each tutorial, you will develop new skills and improve your understanding of this challenging but rewarding subfield of machine learning. This course is fun and exciting, but at the same time, we take you deeper into time series with concepts and exercises to help you understand what time series are and how to apply them. Throughout the brand new version of the course, we cover many tools and techniques, including:

Panda
Matplotlib
learned
State model
Scipy
Prophet
Born in the sea
Z Score
Turkish method
silver
red and white sound
Breakage
XGBOST
Alibi_Identity
STL Parsing
Convergence
Autocorrelation
Spectral residuals
MaxNLocator
Winsorization
February Order
Additive seasonality
Qualitative seasoning
Univariate Assignment
Multivariate imputation
interpolation
front padding and back padding
moving average
Autoregressive Moving Average Model
Fourier Analysis
Arima Model

In addition, the course is full of practical exercises based on real-life examples. So you will not only learn the theory, but you will also have practical practice in creating your own models. There are five major projects on health care problems and one minor project on action. These projects are listed below:

NYC Taxi Project
Air passenger project
Movie Box Office Project.
The CO2 Project
Click on the project.
Sales Project.
Beer production project
medical treatment project
Divvy bike sharing app.
Instagram.
Sunspots

What you will learn in the Introduction to Time Series with Python 2023 course

front padding and back padding
Autoregressive Moving Average Model

This course is suitable for those who

Anyone interested in machine learning.
Students who have at least a high school background in mathematics and want to get started in machine learning, deep learning, and artificial intelligence.
All those people who are not very comfortable with coding but are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it to datasets easily.
Any college student who wants to start a career in data science
Anyone who wants to create added value for their business using the powerful tools of Machine Learning, Artificial Intelligence and Deep Learning. All people who want to work as a Data Engineer, Machine Learning, Deep Learning and Artificial Intelligence Engineer in an Automotive company.
Anyone who wants to improve their knowledge in Machine Learning, Deep Learning, and Artificial Intelligence