Time Series Data Analysis & Forecasting using ML/DL | Crash Course | Urdu/Hindi | Under 11 Hours

You can book One to one consultancy session with me on Mentoga: https://mentoga.com/muhammadaammartufail #codanics #dataanalytics #pythonkachilla #pkc24 ✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅ Python ka chilla 2024 You can now register for Python ka chilla 2024 This is a paid course which you can register and find more information at the following link: https://forms.gle/kUU3eZJsFRb7Cn6r8 ✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅ Here you can access all the codes: https://github.com/AammarTufail/python-ka-chilla-2024/tree/main/20_time_series_data_analysis ✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅ --------------------------------------------------------------------------------------------------------------------------------------- Welcome to our Crash Course on Time Series Data Analysis & Forecasting using ML/DL Models—designed especially for Urdu/Hindi speakers! In this comprehensive course, delivered in under 11 hours, you’ll gain the skills to analyze time series data and build powerful forecasting models using both Machine Learning and Deep Learning techniques. What You’ll Learn: Fundamentals of Time Series Analysis: Understand trends, seasonality, and noise in data. Statistical Forecasting Methods: Dive into techniques like ARIMA and exponential smoothing. Machine Learning Approaches: Learn how to apply regression models and ensemble methods. Deep Learning for Time Series: Master LSTM networks and other architectures tailored for sequential data. Hands-On Projects: Follow practical coding sessions in Python with real-world datasets. Data Visualization & Evaluation: Gain insights on model performance and how to improve forecasting accuracy. This crash course is perfect for beginners and intermediate learners who are looking to boost their data science skills in their native language. With clear explanations, practical examples, and step-by-step tutorials, you’ll be well-equipped to tackle time series forecasting challenges in your career or personal projects. Don’t forget to like, subscribe, and share if you find this tutorial helpful. Drop your questions and feedback in the comments below! High Ranking Hashtags: #TimeSeriesAnalysis #Forecasting #MachineLearning #DeepLearning #UrduTutorial #HindiTutorial #DataScience #CrashCourse #Python #ML #DL --------------------------------------------------------------------------------------------------------------------------------------- ✅✅✅RoadMap to Data Analytics✅✅✅ Complete RoadMap to Become a Data Analyst in 2024. Learn and Practice these skills and courses from our youtube Channel Codanics or our website www.codanics.com #TimeSeriesAnalysis #DataScience #UrduTutorial #HindiTutorial #CrashCourse #TimeSeries #Forecasting #DataAnalysis #MachineLearning #Python #ARIMA #DataVisualization #Analytics #LearnDataScience --------------------------------------------------------------------------------------------------------------------------------------- ✅Our Free Books: https://codanics.com/books/abc-of-statistics-for-data-science/ ✅Our website: https://www.codanics.com ✅Our Courses: https://www.codanics.com/courses ✅Our YouTube Channel: www.youtube.com/@Codanics ✅ Our whatsapp channel: https://whatsapp.com/channel/0029Va7nRDq3QxRzGqaQvS3r ✅Our Facebook Group: https://www.facebook.com/groups/codanics ✅Our Discord group for community Discussion: https://discord.gg/QpvUKEtUJD ✉️For more Details contact us at [email protected] Time Stamps: 00:00:00 What is Time Series? 00:00:28 Introduction to Time Series Data 01:10:14 Time Series Data Analysis and Types 01:41:55 Types of Time Data 01:46:59 Time Series in Python 02:15:49 Time Series Project Understanding 03:07:13 Weather Forecasting (A case Study) 04:19:44 In Depth Weather Forecasting (A Case Study) 05:24:03 Details about weather forecasting 05:41:17 Prophet model from Facebook 06:49:14 Machine Learning with Prophet model 07:08:11 Web scraping for Data 07:40:41 Web scraping in Python 08:07:01 Stock Market Data Scraping 08:30:05 ARIMA model for Time Series 09:13:39 ARIMA in Python for Time Series Data 09:22:23 ARIMA in depth 09:39:58 ARIMA SARIMA SARIMAX in Python