Introduction to Time Series with Python [2023]

Silverkite, Additive and Multiplicative seasonality, Univariate and Multavariate imputation, Statsmodels, and so on

What you'll learn
Pandas
Matplotlib
Statsmodels
Scipy
Prophet
seaborn
Z-score
Turkey method
Silverkite
Red and white noise
rupture
XGBOOST
Alibi_detect
STL decomposition
Cointegration
sklearn
Autocorrelation
Spectral Residual
MaxNLocator
Winsorization
Fourier order
Additive seasonality
Multiplicative seasonality
Univariate imputation
multavariate imputation
interpolation
forward fill and backward fill
Moving average
Autoregressive Moving Average models
Fourier Analysis


Requirements
Basic python is required
Basic machine learning knowledge is required
Description
Interested in the field of time-series? Then this course is for you!
A software engineer has designed this course. With the experience and knowledge I did gain throughout the years, I can share my knowledge and help you learn complex theory, algorithms, and coding libraries simply.
I will walk you into the concept of time series and how to apply Machine Learning techniques in time series. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of machine learning.
This course is fun and exciting, but at the same time, we dive deep into time-series with concepts and practices for you to understand what is time-series and how to implement them. Throughout the brand new version of the course, we cover tons of tools and technologies, including:
Pandas.
Matplotlib
sklearn
Statsmodels
Scipy
Prophet
seaborn
Z-score
Turkey method
Silverkite
Red and white noise
rupture
XGBOOST
Alibi_detect
STL decomposition
Cointegration
Autocorrelation
Spectral Residual
MaxNLocator
Winsorization
Fourier order
Additive seasonality
Multiplicative seasonality
Univariate imputation
Multavariate imputation
interpolation
forward fill and backward fill
Moving average
Autoregressive Moving Average models
Fourier Analysis
Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are five big projects on healthcare problems and one small project to practice. These projects are listed below:
Nyc taxi Project
Air passengers Project.
Movie box office Project.
CO2 Project.
Click Project.
Sales Project.
Beer production Project.
Medical Treatment Project.
Divvy bike share program.
Instagram.
Sunspots.
Who this course is for:
Anyone interested in Machine Learning.
Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence
Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.
Any students in college who want to start a career in Data Science
Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.
Anyone who wants to improve their knowledge in machine learning, deep learning and artificial intelligence



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