Course Information
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Course Overview
Silverkite, Additive and Multiplicative seasonality, Univariate and Multavariate imputation, Statsmodels, and so on
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.
Course Content
- 6 section(s)
- 77 lecture(s)
- Section 1 Introduction
- Section 2 Data Acquisition and Cleaning
- Section 3 Introduction to Time Series
- Section 4 Machine Learning for time-series analysis
- Section 5 Introduction to Facebook Prophet
- Section 6 Detecting and Handling Outliers
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
- ARIMA model
Skills covered in this course
Reviews
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LLinh Nguyễn
Great Course for Beginners! The Introduction to Time Series with Python [2023] course is incredibly helpful and easy to follow. The instructor explains concepts clearly, provides real-world examples, and guides learners step by step, making time series analysis much more approachable. The lessons are well-structured, with practical exercises that allow you to apply what you've learned immediately. The content is up-to-date and suitable for both beginners and those looking to reinforce their knowledge. Highly recommended! Thanks to the instructor and Udemy!
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PPhan Quốc Hùng
The course content is arranged logically, and the instructor presents the lessons in a detailed and easy-to-understand manner. The concepts are explained simply. Each example is accompanied by materials so that students can practice on their own.
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DDuong Le Nguyen
I really enjoyed this course, and the instructor was great
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AAnna Mark
giảng viên trình bày bài giảng một cách hiệu quả, có chuyên môn và kiến thức. khoá học rất bổ ích