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A Practical Approach to Timeseries Forecasting Using Python · LearnSpace
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A Practical Approach to Timeseries Forecasting Using Python

Курс от Packt
Средний≈ 17.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Dive into the dynamic world of time series forecasting with this comprehensive and hands-on Python course. You’ll gain practical skills in data manipulation, visualization, and forecasting techniques—empowering you to uncover trends, identify patterns, and make predictions using real-world datasets. Whether you're preparing stock forecasts or tracking public health trends, you'll be equipped to apply advanced forecasting tools effectively. Your journey begins with the fundamentals of time series data and gradually builds through essential processing techniques, including decomposition, noise reduction, and feature engineering. As the course progresses, you’ll explore powerful statistical models such as ARIMA and SARIMA before moving into deep learning-based forecasting using LSTM, BiLSTM, and GRU models. Hands-on projects like COVID-19 case prediction, Microsoft stock forecasting, and birth rate trend analysis reinforce theoretical knowledge and provide you with ready-to-use code and workflows. Quizzes and real datasets at every step ensure a fully immersive learning experience. This course is ideal for data enthusiasts, analysts, and aspiring machine learning engineers. A basic understanding of Python programming and fundamental statistics is recommended. The course is best suited for learners at an intermediate level.

Навыки, которые вы освоите

Recurrent Neural Networks (RNNs)Machine Learning MethodsData ProcessingData PreprocessingForecastingData Visualization SoftwareModel OptimizationModel EvaluationPredictive AnalyticsFeature EngineeringStatistical Modeling

Программа курса

9 модулей · 131 учебных материалов

01Introduction4 материалов

Introduction

Introduction to Time Series ForecastВидеоFull Course ResourcesЧтениеIntroduction to InstructorВидеоCourse IntroductionВидео
02Motivation and Overview of Time Series Analysis

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Packt - Course Instructors

Преподаватель курса

A Practical Approach to Timeseries Forecasting Using Python
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Обучение на Coursera

≈ 17.1 ч

9 модулей

Язык: Английский

Субтитры: Венгерский, Азербайджанский

Часть программы вашего университета
11 материалов

Motivation and Overview of Time Series Analysis

Introduction to Time Series ForecastingВидеоFeatures of Time SeriesВидеоTypes of Time Series DataВидеоStages for Time Series ForecastingВидеоData Manipulation in Time SeriesВидеоData Processing for Time Series ForecastingВидеоMachine Learning ForecastingВидеоRNN ForecastingВидеоProjects to Be CoveredВидеоExploring Time Series Features and ForecastingDIALOGUEMotivation and Overview of Time Series Analysis - AssessmentЗадание
03Basics of Data Manipulation in Time Series19 материалов

Basics of Data Manipulation in Time Series

Module OverviewВидеоPackages Required to Execute Codes Error-FreeВидеоOverview of Basic Plotting and VisualizationВидеоOverview of Time Series ParametersВидеоDependencies Installation and Dataset OverviewВидеоData Manipulation in PythonВидеоData Slicing and IndexingВидеоBasic Data Visualization with Single Time Series FeatureВидеоData Visualization with Multiple Time Series FeaturesВидеоData Visualization with Customized Features SelectionВидеоArea Plots in Data AnalysisВидеоHistogram with Single FeatureВидеоHistogram Multiple FeaturesВидеоPie ChartsВидеоTime Series ParametersВидеоQuiz VideoВидеоQuiz SolutionВидеоTime Series Forecasting with PythonDIALOGUEBasics of Data Manipulation in Time Series - AssessmentЗадание
04Data Processing for Timeseries Forecasting18 материалов

Data Processing for Timeseries Forecasting

Module OverviewВидеоDataset SignificanceВидеоDataset OverviewВидеоDataset ManipulationВидеоData Pre-ProcessingВидеоRVT ModelsВидеоAutomatic Time Series DecompositionВидеоTrend Using Moving Average FilterВидеоSeasonality ComparisonВидеоResamplingВидеоNoise in Time SeriesВидеоFeature EngineeringВидеоStationarity in Time SeriesВидеоHandling Non-Stationarity in Time SeriesВидеоQuizВидеоQuiz SolutionВидеоWorking with Time Series Data in PythonDIALOGUEData Processing for Timeseries Forecasting - AssessmentЗадание
05Machine Learning in Time Series Forecasting18 материалов

Machine Learning in Time Series Forecasting

Section OverviewВидеоData PreparationВидеоAuto Correlation and Partial CorrelationВидеоData SplittingВидеоAutoregressionВидеоAutoregression in PythonВидеоMoving Average and ARMAВидеоARIMAВидеоARIMA in PythonВидеоAuto ARIMA in PythonВидеоSARIMAВидеоSARIMA in PythonВидеоAuto SARIMA in PythonВидеоFuture Predictions Using SARIMAВидеоQuizВидеоQuiz SolutionВидеоApplying Machine Learning Models to Time Series ForecastingDIALOGUEMachine Learning in Time Series Forecasting - AssessmentЗадание
06Recurrent Neural Networks in Time Series Forecasting19 материалов

Recurrent Neural Networks in Time Series Forecasting

Module OverviewВидеоImportant ParametersВидеоLSTM ModelsВидеоBiLSTM ModelsВидеоGRU ModelsВидеоUnderfitting and OverfittingВидеоModel for Underfitting and OverfittingВидеоModel Evaluation for Underfitting and OverfittingВидеоDataset Preparation and ScalingВидеоDataset ReshapingВидеоLSTM Implementation on DatasetВидеоTime Series Forecasting (TSF) Using LSTMВидеоGraph for TSF Using LSTMВидеоLSTM Parameter Change and Stacked LSTMВидеоBiLSTM for Time Series ForecastingВидеоQuizВидеоQuiz SolutionВидеоIntroduction to RNN Models for Time SeriesDIALOGUERecurrent Neural Networks in Time Series Forecasting - AssessmentЗадание
07Project 1: COVID-19 Positive Cases Prediction Using Machine Learning Algorithm13 материалов

Project 1: COVID-19 Positive Cases Prediction Using Machine Learning Algorithm

Project OverviewВидеоDataset OverviewВидеоDataset CorrelationВидеоShape and NULL CheckВидеоDataset IndexВидеоVisualize the DataВидеоArea PlotВидеоAutocorrelation, Standard Deviation, and MeanВидеоStationarity CheckВидеоARIMA ImplementationВидеоSARIMA ImplementationВидеоVariations in SARIMAВидеоProject 1: COVID-19 Positive Cases Prediction Using Machine Learning Algorithm - AssessmentЗадание
08Project 2: Microsoft Corporation Stock Prediction Using RNNs14 материалов

Project 2: Microsoft Corporation Stock Prediction Using RNNs

Module OverviewВидеоData AnalysisВидеоData Visualization Line PlotsВидеоArea PlotsВидеоAuto Correlation, Standard Deviation, and MeanВидеоStationarity CheckВидеоData Manipulation for Deep LearningВидеоDataset DivisionВидеоLSTM Implementation and ErrorsВидеоLSTM ForecastingВидеоStacked LSTM ForecastingВидеоBiLSTM and Stacked BiLSTMВидеоImplementing Time Series Forecasting with LSTMsDIALOGUEProject 2: Microsoft Corporation Stock Prediction Using RNNs - AssessmentЗадание
09Project 3: Birth Rate Forecasting Using RNNs with Advanced Data Analysis15 материалов

Project 3: Birth Rate Forecasting Using RNNs with Advanced Data Analysis

Project OverviewВидеоDataset OverviewВидеоYearly Birth Distribution Plot and Birth Rate PlotВидеоMonthly Birth Distribution Plot and Birth Rate PlotВидеоDay-Wise and Date-Wise Birth Distribution Plot and Birth Rate PlotВидеоBirth Rate Range PlotВидеоData ManipulationВидеоStationarity CheckВидеоManipulation for ForecastingВидеоScalingВидеоLSTM ForecastingВидеоStacked LSTM and BiLSTMВидеоCourse ConclusionВидеоFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание