Python Timeseries Forecasting Online Course
Python Timeseries Forecasting Online Course
This Python Time Series Forecasting online course will teach you how to predict future trends like weather patterns, population growth, and more using machine learning and deep learning models. You’ll start by understanding the fundamentals of time series analysis, including key concepts like seasonality, trend, and stationarity, and explore real-world applications. The course covers data analysis and visualization techniques using Python libraries such as NumPy, Pandas, and Matplotlib. You’ll learn to preprocess time series data for machine learning and RNN models. By the end, you’ll gain hands-on experience with GRU, LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM models to build and evaluate time series forecasts.
Key Benefits
- A comprehensive learning path for beginners to master time series analysis, data analysis, and forecasting techniques from the ground up.
- In-depth coverage of advanced and cutting-edge recurrent neural network (RNN) models, incorporating the latest advancements in the field.
Target Audience
This course is designed for individuals with no prior experience in deep learning (DL), data analysis, or mathematics. It begins with the foundational concepts and gradually builds your knowledge in these areas. A basic understanding of Python is all that is required to get started.
The course is for both beginners with some programming experience as well as those with no background in data analysis, machine learning (ML), or recurrent neural networks (RNNs). It is ideal for anyone looking to enhance their skills in ML and DL, understand the relationship between data science and time series analysis, implement key time series parameters, and evaluate their effects, as well as apply ML algorithms to forecast time series data.
Learning Objectives
- Master data analysis techniques and gain hands-on experience in handling time series forecasting tasks.
- Implement advanced data visualization techniques using Matplotlib to effectively present your time series data.
- Evaluate and apply machine learning models for time series forecasting, including Auto Regression, ARIMA, Auto ARIMA, SARIMA, and SARIMAX.
- Learn how to build and implement various Recurrent Neural Network (RNN) models, including LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM.
- Gain practical experience by implementing machine learning and RNN models through three cutting-edge, real-world projects.
Course Outline
The Python Timeseries Forecasting Exam covers the following topics -
Module 1. Introduction
- Introduction to Time Series Forecasting
- Meet the Instructor
- Course Overview
Module 2. Motivation and Overview of Time Series Analysis
- Introduction to Time Series Forecasting
- Key Features of Time Series
- Types of Time Series Data
- Stages in Time Series Forecasting
- Data Manipulation Techniques for Time Series
- Data Processing for Time Series Forecasting
- Machine Learning Approaches to Forecasting
- RNN for Forecasting
- Projects to Be Covered
Module 3. Basics of Data Manipulation in Time Series
- Module Overview
- Required Packages for Error-Free Code Execution
- Introduction to Basic Plotting and Visualization
- Overview of Time Series Parameters
- Installing Dependencies and Dataset Overview
- Data Manipulation in Python
- Data Slicing and Indexing
- Basic Visualization of a Single Time Series Feature
- Visualizing Multiple Time Series Features
- Customizing Feature Selection in Data Visualization
Module 4. Data Processing for Time Series Forecasting
- Module Overview
- Significance of the Dataset
- Overview and Manipulation of the Dataset
- Data Preprocessing Steps
- RVT Models Overview
- Automatic Time Series Decomposition
- Trend Analysis Using Moving Average Filter
- Seasonality Comparison
- Resampling Techniques
Module 5. Machine Learning in Time Series Forecasting
- Section Overview
- Data Preparation Techniques
- Auto-Correlation and Partial Correlation Analysis
- Splitting Data for Model Training
- Autoregression Techniques
- Implementing Autoregression in Python
- Moving Average and ARMA Models
- ARIMA Model Overview
- Implementing ARIMA in Python
- Auto ARIMA in Python
Module 6. Recurrent Neural Networks in Time Series Forecasting
- Module Overview
- Important Model Parameters
- LSTM Models Overview
- BiLSTM Models Overview
- GRU Models Overview
- Understanding Underfitting and Overfitting
- Models for Underfitting and Overfitting
- Evaluating Models for Underfitting and Overfitting
- Dataset Preparation and Scaling Techniques
- Reshaping Datasets for Model Input
Module 7. Project 1: COVID-19 Positive Cases Prediction Using Machine Learning Algorithms
- Project Overview
- Overview of the COVID-19 Dataset
- Correlation Analysis of the Dataset
- Checking for Missing Values and Dataset Shape
- Visualizing the Data (Area Plot)
- Analyzing Autocorrelation, Standard Deviation, and Mean
- Stationarity Check
- Implementing ARIMA
Module 8. Project 2: Microsoft Corporation Stock Prediction Using RNNs
- Project Overview
- Data Analysis for Stock Prediction
- Data Visualization Techniques (Line and Area Plots)
- Analyzing Autocorrelation, Standard Deviation, and Mean
- Stationarity Check
- Data Preparation for Deep Learning Models
- Dividing the Dataset for Training and Testing
- Implementing and Evaluating LSTM Models
- Stock Prediction Using LSTM
Module 9. Project 3: Birth Rate Forecasting Using RNNs with Advanced Data Analysis
- Project Overview
- Overview of the Birth Rate Dataset
- Visualizing Yearly Birth Distribution and Birth Rate Trends
- Monthly and Day-Wise Birth Distribution and Birth Rate Plots
- Visualizing Birth Rate Range Trends
- Data Manipulation for Forecasting
- Stationarity Check
- Preparing Data for Forecasting
- Scaling Data for Model Input
