Time Series- Mastering Time Series Forecasting using Python

Time Series: Mastering Time Series Forecasting using Python, available at $59.99, has an average rating of 4.42, with 123 lectures, based on 53 reviews, and has 592 subscribers.

You will learn about ? Learn the basics of Time Series Analysis and Forecasting. ? Learn basics of Data Analysis Techniques and to Handle Time Series Forecasting. ? Learn to implement the basics of Data Visualization Techniques using Matplotlib ? Learn to Evaluate and Analyze Time Series Forecasting Parameters i.e., Seasonality, Trend, and Stationarity etc. ? Learn to compute and visualize the auto correlation, mean over time, standard deviation and gaussian noise in time series datasets. ? Learn to evaluate applied machine learning in Time Series Forecasting ? Learn to implement Machine Learning Techniques for Time Series Forecasting i.e., Auto Regression, ARIMA, Auto ARIMA, SARIMA, and SARIMAX ? Learn basics of RNN Models i.e., GRU, LSTM, BiLSTM ? Learn to model LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM models for time series forecasting. ? Learn the impact of Overfitting, Underfitting, Bias and Variance on the performance of RNN Models ? Learn how to implement ML and RNN Models with three state-of-the-art projects. ? And much more… This course is ideal for individuals who are ? People who want to advance their skills in machine learning and deep learning. or ? People who want to master relation of data science with time series analysis. or ? People who want to implement time series parameters and evaluate their impact on it. or ? People who want to implement machine learning algorithms for time series forecasting. or ? Individuals who are passionate about RNNs specially, LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM Models. or ? Machine Learning Practitioners. or ? Research Scholars. or ? Data Scientists. It is particularly useful for ? People who want to advance their skills in machine learning and deep learning. or ? People who want to master relation of data science with time series analysis. or ? People who want to implement time series parameters and evaluate their impact on it. or ? People who want to implement machine learning algorithms for time series forecasting. or ? Individuals who are passionate about RNNs specially, LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM Models. or ? Machine Learning Practitioners. or ? Research Scholars. or ? Data Scientists.

Enroll now: Time Series: Mastering Time Series Forecasting using Python

Summary

Title: Time Series: Mastering Time Series Forecasting using Python

Price: $59.99

Average Rating: 4.42

Number of Lectures: 123

Number of Published Lectures: 123

Number of Curriculum Items: 123

Number of Published Curriculum Objects: 123

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

? Learn the basics of Time Series Analysis and Forecasting.

? Learn basics of Data Analysis Techniques and to Handle Time Series Forecasting.

? Learn to implement the basics of Data Visualization Techniques using Matplotlib

? Learn to Evaluate and Analyze Time Series Forecasting Parameters i.e., Seasonality, Trend, and Stationarity etc.

? Learn to compute and visualize the auto correlation, mean over time, standard deviation and gaussian noise in time series datasets.

? Learn to evaluate applied machine learning in Time Series Forecasting

? Learn to implement Machine Learning Techniques for Time Series Forecasting i.e., Auto Regression, ARIMA, Auto ARIMA, SARIMA, and SARIMAX

? Learn basics of RNN Models i.e., GRU, LSTM, BiLSTM

? Learn to model LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM models for time series forecasting.

? Learn the impact of Overfitting, Underfitting, Bias and Variance on the performance of RNN Models

? Learn how to implement ML and RNN Models with three state-of-the-art projects.

? And much more…

Who Should Attend

? People who want to advance their skills in machine learning and deep learning.

? People who want to master relation of data science with time series analysis.

? People who want to implement time series parameters and evaluate their impact on it.

? People who want to implement machine learning algorithms for time series forecasting.

? Individuals who are passionate about RNNs specially, LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM Models.

? Machine Learning Practitioners.

? Research Scholars.

? Data Scientists.

Target Audiences

? People who want to advance their skills in machine learning and deep learning.

? People who want to master relation of data science with time series analysis.

? People who want to implement time series parameters and evaluate their impact on it.

? People who want to implement machine learning algorithms for time series forecasting.

? Individuals who are passionate about RNNs specially, LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM Models.

? Machine Learning Practitioners.

? Research Scholars.

? Data Scientists.

1. Ever wondered how weather predictions are made?

2. Curious about estimating the global population in 2050?

3. What if you could predict the expected lifespan of our universe from your laptop at home?

It’s all possible through the art of Time Series Forecasting, utilizing cutting-edge and robust Machine Learning and Deep Learning models. 

You may have searched for many relevant courses, but this one stands out!

This course is an all-encompassing package for beginners, designed to teach time series, data analysis, and forecasting methods from the ground up. Each module is packed with engaging content and a practical approach, accompanied by concise theoretical concepts. At the end of each module, you’ll be given hands-on exercises or quizzes, with solutions available in the following video.   

We’ll start with the theoretical concepts of time series analysis, offering an overview of its features, real-world examples, data collection mechanisms, and its applications. You’ll learn the fundamental benchmark steps for time series forecasting. 

This comprehensive package will equip you with the skills to perform basic to advanced data analysis and visualization for time series data using Numpy, Pandas, and Matplotlib. Python will be our programming language of choice, and we’ll teach it from elementary to advanced levels, ensuring you can implement any machine learning concept.     

This course serves as your guide to leveraging the power of Python for evaluating time series datasets, considering factors like seasonality, trend, noise, autocorrelation, mean over time, correlation, and stationarity. You’ll also master feature engineering, crucial for effective data handling in your forecasting models. Armed with this knowledge, you’ll be prepared to apply Machine Learning and RNNs Models to test, train, and evaluate your forecasts.   

You’ll gain a deep understanding of essential concepts in applied machine learning, including Auto-Regression, Moving Average, ARIMA, Auto-ARIMA, SARIMA, Auto-SARIMA, and SARIMAX for time series forecasting. Additionally, we’ll comprehensively compare the performance of these models.     

Machine learning ranks among the hottest jobs on Glassdoor, with machine learning engineers earning an average salary of over $110,000 in the United States, according to Indeed. Machine Learning offers a rewarding career, allowing you to tackle some of the world’s most intriguing problems.

 

In the RNNs Module, you’ll delve into building GRU, LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM models. We’ll explore practical concepts like underfitting, overfitting, bias, variance, dropout, the role of dense layers, the impact of batch sizes, and the performance of various activation functions in multi-layer RNN models. Each concept of Recursive Neural Networks (RNNs) will be explained theoretically and implemented using Python.   

Designed for beginners with minimal programming experience, or even those new to Data Analysis, Machine Learning, and RNNs, this comprehensive course rivals others in the field, typically costing thousands of dollars. With over 12 hours of HD video lectures divided into more than 120 videos, along with detailed code notebooks for every topic, it’s one of the most comprehensive courses on Time Series Forecasting with Machine Learning and RNNs on Udemy!

What Sets This Course Apart?

This course not only teaches you the role and impact of time series analysis but also how to apply ML and build RNNs. You’ll understand the training process, the significance of overfitting and underfitting, and gain mastery over Python.

This course is:

Easy to understand

Expressive and self-explanatory

To the point

Practical, with live coding

A comprehensive package with three in-depth projects covering the course’s entire content

Thorough, covering the most advanced RNN models by renowned data scientists

Teaching Is Our Passion:

We emphasize online tutorials that encourage learning by doing. This course takes a practical approach to time series forecasting, using RNNs and Machine Learning Algorithms like ARIMA, SARIMA, and SARIMAX. It includes three projects in the final module, allowing you to experiment and gain practical experience with real-world datasets on Birthrates, Stock Exchange, and COVID-19. We’ve worked tirelessly to ensure you grasp the concepts clearly. Our goal is to give you a solid foundation in the basics before delving into more complex concepts. The course materials include high-quality video content, course notes, meaningful materials, handouts, and evaluation exercises. You can also reach out to our friendly team for any queries.

Course Content:

We’ll teach you how to program with Python and use it for data visualization, data manipulation, and RNNs. Topics covered include:

1. Packages Installation

2. Basic Data Manipulation in Time Series using Python

3. Data Processing for Time Series Forecasting using Python

4. Machine Learning in Time Series Forecasting using Python

5. Recurrent Neural Networks for Time Series using Python

6. Project 1: COVID-19 Prediction using Machine Learning Algorithms

7. Project 2: Microsoft Corporation Stock Prediction using RNNs

8. Project 3: Birthrate Forecasting using RNNs with Advanced Data Analysis, and much more

Enroll in the course and become a time series forecasting expert today!

Who Should Take This Course:

Individuals looking to advance their skills in machine learning and deep learning

Those interested in the relationship between data science and time series analysis

People seeking to implement time series parameters and assess their impact

Individuals interested in implementing machine learning algorithms for time series forecasting

Enthusiasts passionate about RNNs, particularly LSTM, Stacked LSTM, BiLSTM, and Stacked BiLSTM Models

Machine Learning Practitioners

Research Scholars

Data Scientists

What You’ll Learn:

Concepts, principles, and theories of time series forecasting and its parameters

Evaluation of machine learning models

Model and implementation of RNN models for time series forecasting

Why This Course:

Easy to understand and practical with live coding

Comprehensive package with three in-depth projects

Covers advanced RNN models by renowned data scientists

Emphasizes learning by doing

Provides a solid foundation in the basics before delving into complex concepts

Unlock the world of time series forecasting with Python and machine learning today!

List of Keywords:

Time Series Forecasting

Machine Learning

Deep Learning

Python

ARIMA

SARIMA

SARIMAX

RNN

LSTM

Stacked LSTM

BiLSTM

Stock Prediction

Data Analysis

Data Visualization

Data Manipulation

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Instructor

Lecture 2: Course Introduction

Lecture 3: Request for Your Honest Review

Lecture 4: Links for the Courses Materials and Codes

Chapter 2: Motivation and Overview of Time Series Analysis

Lecture 1: Links for the Courses Materials and Codes

Lecture 2: Time Series Introduction and Motivation

Lecture 3: Features of Time Series

Lecture 4: Types of Time Series Data

Lecture 5: Stages For Time Series Forecasting

Lecture 6: Data Manipulation Motivation

Lecture 7: Data Processing for Time Series Motivation

Lecture 8: Machine Learning Motivation

Lecture 9: RNN Motivation

Lecture 10: Projects to be Covered

Chapter 3: Basics of Data Manipulation in Time Series

Lecture 1: Links for the Courses Materials and Codes

Lecture 2: Module Overview

Lecture 3: Packages Installation

Lecture 4: Overview of Basic Plotting and Visualization

Lecture 5: Overview of Time Series Parameters

Lecture 6: Dependencies Installation and Dataset Overview

Lecture 7: Data Manipulation in Python

Lecture 8: Data Slicing and Indexing

Lecture 9: Basic Data Visualization with Single Time Series Feature

Lecture 10: Data Visualization with Multiple Time Series Feature

Lecture 11: Data Visualization with Customized Features Selection

Lecture 12: Area Plots in Data Analysis

Lecture 13: Histogram with Single Feature

Lecture 14: Histogram Multiple Features

Lecture 15: Pie Charts

Lecture 16: Time Series Parameters

Lecture 17: Quiz Video

Lecture 18: Quiz Solution

Chapter 4: Data Processing for Timeseries Forecasting

Lecture 1: Links for the Courses Materials and Codes

Lecture 2: Module Overview

Lecture 3: Dataset Significance

Lecture 4: Dataset Overview

Lecture 5: Dataset Manipulation

Lecture 6: Data Preprocessing

Lecture 7: RVT Models

Lecture 8: Automatic Time Series Decomposition

Lecture 9: Trend using Moving Average Filter

Lecture 10: Seasonality Comparison

Lecture 11: Resampling

Lecture 12: Noise in Time Series

Lecture 13: Feature Engineering

Lecture 14: Stationarity in Time Series

Lecture 15: Handling Non- Stationarity in Time Series

Lecture 16: Quiz

Lecture 17: Quiz Solution

Chapter 5: Machine Learning in Time Series Forecasting

Lecture 1: Links for the Courses Materials and Codes

Lecture 2: Section Overview

Lecture 3: Data Prepration

Lecture 4: Auto Correlation and Partial Correlation

Lecture 5: Data Splitting

Lecture 6: AutoRegression

Lecture 7: AutoRegression in Python

Lecture 8: Moving Average and ARMA

Lecture 9: ARIMA

Lecture 10: ARIMA in Python

Lecture 11: AutoArima in Python

Lecture 12: SARIMA

Lecture 13: SARIMA in Python

Lecture 14: AutoSARIMA in Python

Lecture 15: Future Predictions using SARIMA

Lecture 16: Quiz

Lecture 17: Quiz Solution

Chapter 6: Recurrent Neural Networks in Time Series Forecasting

Lecture 1: Links for the Courses Materials and Codes

Lecture 2: Module Overview

Lecture 3: Important Parameters

Lecture 4: LSTM Models

Lecture 5: BiLSTM Models

Lecture 6: GRU Models

Lecture 7: Concept of Underfitting and Overfitting

Lecture 8: Model for Underfitting and Overfitting

Lecture 9: Model Evaluation for Underfitting and Overfitting

Lecture 10: DataSet Prepration and Scaling

Lecture 11: Dataset Reshaping

Lecture 12: LSTM Implementation on Dataset

Lecture 13: Time Series Forecasting (TSF) using LSTM

Lecture 14: Graph for TSF using LSTM

Lecture 15: LSTM Parameter Change and Stacked LSTM

Lecture 16: Bi-LSTM for Time Series Forecasting

Lecture 17: Quiz

Lecture 18: Quiz Solution

Chapter 7: Project 1 COVID-19 Positive Cases Prediction using Machine Learning Algorith

Lecture 1: Links for the Courses Materials and Codes

Lecture 2: Project Overview

Lecture 3: Dataset Overview

Lecture 4: Dataset Correlation

Lecture 5: Shape and NULL Check

Lecture 6: Dataset Index

Lecture 7: Visualize the Data

Lecture 8: Area Plot

Lecture 9: Autocorrelation, Std. Deviation and Mean

Instructors

AI Sciences
AI Experts & Data Scientists |4+ Rated | 168+ Countries

AI Sciences Team
Support Team AI Sciences

Rating Distribution

1 stars: 1 votes

2 stars: 3 votes

3 stars: 7 votes

4 stars: 17 votes

5 stars: 25 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!