Updated on May, 2024
Language - English
It is good to work with easy-to-understand step-by-step instructions. Also, good to have the data set to practice on my own time. There is limitation too. It is hard to get instant feedback from human interaction when I am stuck.
Time Series Analysis And Forecasting Using Python
Master time series analysis & forecasting models in Python along with Time Data Visualization, AR, MA, ARIMA, Regression, and ANN.
Lectures -95
Resources -2
Duration -13 hours
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Course Description
Time Series Analysis And Forecasting Using Python is a comprehensive Time Series Forecasting course that helps you make decisions on how to manage your inventory, plan your workforce, and many other business-related tasks.
The course offers training on time series analysis and forecasting needed to know about various forecasting models and how to use them in Python.
Time Series Analysis And Forecasting Using Python Overview
This online course will teach you how to implement time series forecasting models such as AutoRegression, Moving Average, ARIMA, SARIMA, etc. You will also learn how to implement multivariate forecasting models based on Linear regression and Neural Networks. By the end of the course, you will be able to confidently practice, discuss, and understand different Forecasting models used by organizations
How this course will help you?
All participants in this course receive a Verifiable Certificate of Completion.
This course will provide you with a solid foundation by instructing you on the most popular forecasting models. It teaches how to use them if you're a business manager, executive, or student who wants to study and apply forecasting models in real-world business situations.
Why should you choose this course?
The course walks you through the curriculum by teaching with examples. The main goal of each Part is to teach you the ideas through practical examples. Each section has the following components:
Use cases and theoretical concepts of various forecasting models
Detailed instructions for putting forecasting models into practice in Python
Downloadable code files with data and solutions from each lecture
Lecture notes and assignments to review and apply the principles
This course stands out from other online courses due to the practical classes where we develop the models for each of these tactics.
Who this course is for:
Individuals who want to work in data science
Professionals in the workforce just starting their machine learning career
Statisticians should gain more relevant experience.
Anyone who desires to quickly learn Time Series Analysis using Python
Goals
What will you learn in this course:
Get a firm grasp of time series analysis and forecasting.
Recognize the business contexts in which time series analysis can be used.
Learning about Auto Regression and Moving Average Models by Creating 5 Different Time Series Forecasting Models in Python
Learn about the forecasting models SARIMA and ARIMA.
To manage Time Series data and perform statistical calculations, use Pandas DataFrames.
Prerequisites
What are the prerequisites for this course?
Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
1 Lectures
-
Introduction
02:10
02:10
Time Series - Basics
5 Lectures
Setting up Python and Python Crash Course
9 Lectures
Time Series - Data Loading
1 Lectures
Time Series - Visualization
2 Lectures
Time Series - Feature Engineering
2 Lectures
Time Series - Resampling
2 Lectures
Time Series - Transformation
3 Lectures
Time Series - Important Concepts
5 Lectures
Time Series - Test Train Split
1 Lectures
Time Series - Naive (Persistence) model
1 Lectures
Time Series - Auto Regression Model
3 Lectures
Time Series - Moving Average model
2 Lectures
Time Series - ARIMA model
4 Lectures
Time Series - SARIMA model
2 Lectures
Stationary time Series
1 Lectures
Linear Regression - Data Preprocessing
20 Lectures
Linear Regression - Model Creation
12 Lectures
Introduction to ANN
1 Lectures
Single Cells - Perceptron and Sigmoid Neuron
3 Lectures
Neural Networks - Stacking cells to create network
3 Lectures
Important concepts: Common Interview questions
1 Lectures
Standard Model Parameters
1 Lectures
Tensorflow and Keras
2 Lectures
Python - Dataset for classification problem
2 Lectures
Python - Building and training the Model
4 Lectures
Python - Solving a Regression problem using ANN
1 Lectures
Instructor Details
Abhishek and Pukhraj
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