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Published by:Start-Tech Academy
What you’ll learn -:
- Get a solid understanding of Time Series Analysis and Forecasting
- Understand the business scenarios where Time Series Analysis is applicable
- Building 5 different Time Series Forecasting Models in Python
- Learn about Auto regression and Moving average Models
- Learn about ARIMA and SARIMA models for forecasting
- Use Pandas DataFrames to manipulate Time Series data and make statistical computations.
Description-:
You’re looking for a complete course on Time Series Forecasting to drive business decisions involving production schedules, inventory management, manpower planning, and many other parts of the business., right?
You’ve found the right Time Series Analysis and Forecasting course. This course teaches you everything you need to know about different forecasting models and how to implement these models in Python.
After completing this course you will be able to:
Implement time series forecasting models such as AutoRegression, Moving Average, ARIMA, SARIMA etc.
Implement multivariate forecasting models based on Linear regression and Neural Networks.
Confidently practice, discuss and understand different Forecasting models used by organizations
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Marketing Analytics: Forecasting Models with Excel course.
If you are a business manager or an executive, or a student who wants to learn and apply forecasting models in real world problems of business, this course will give you a solid base by teaching you the most popular forecasting models and how to implement it.
Why should you choose this course?
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Theoretical concepts and use cases of different forecasting models
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Step-by-step instructions on implement forecasting models in Python
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Downloadable Code files containing data and solutions used in each lecture
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Class notes and assignments to revise and practice the concepts
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See patterns in time series data
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Make forecasts based on models
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Section 1 – IntroductionIn this section we will learn about the course structure
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Section 2 – Python basicsThis section gets you started with Python.This section will help you set up the python and Jupyter environment on your system and it’ll teachyou how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
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Section 3 – Basics of Time Series DataIn this section, we will discuss about the basics of time series data, application of time series forecasting and the standard process followed to build a forecasting model
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Section 4 – Pre-processing Time Series DataIn this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models
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Section 5 – Getting Data Ready for Regression ModelIn this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation.
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Section 6 – Forecasting using Regression ModelThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don’t understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results.
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Section 7 – Theoretical ConceptsThis part will give you a solid understanding of concepts involved in Neural Networks.In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.
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Section 8 – Creating Regression and Classification ANN model in PythonIn this part you will learn how to create ANN models in Python.We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.
- People pursuing a career in data science
- Working Professionals beginning their Machine Learning journey
- Statisticians needing more practical experience
- Anyone curious to master Time Series Analysis using Python in short span of time