Introduction to Time Series with Python [2023]

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

• ARIMA model

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.

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