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TutorialsExplore Comprehensive Tutorials for Various SkillsAccess a wide range of tutorials on various topics, including technology, design, business, and more. Whether you're a beginner or an expert, find the perfect tutorial to help you improve your skills and achieve your goals.  MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + .srt | Duration: 169 lectures (13h 14m) | Size: 4.89 GB Create a 5 games 5 projects - Draw on Canvas - Amazing effects with jаvascript jаvascript on the HTML5 Canvas element  Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 48000 Hz Language: English | VTT | Size: 277 MB | Duration: 1h 12m  Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz Language: English | VTT | Size: 1.79 GB | Duration: 3h 9m  Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz Language: English | VTT | Size: 2.60 GB | Duration: 5h 19m  Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 48000 Hz Language: English | Size: 5.76 GB | Duration: 7h 19m  Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz Language: English | Size: 669 MB | Duration: 1h 12m  MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + .srt | Duration: 11 lectures (44m) | Size: 403.7 MB Administrative Office Management is the process of. planning, organizing and controlling all the information related act  Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 48000 Hz Language: German | Size: 3.47 GB | Duration: 2h 39m  MP4 | Video: h264, 2560x1440 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 84 Lessons (3h 53m) | Size: 2.33 GB Course Summary  Duration: 1h20m | Video: .TS 1920x1080, 30 fps(r) | Audio: AAC, 44000Hz, 2ch | Size: 433 MB Genre: eLearning | Language: English In this video, you will explore forecasting techniques in Python, including how to use machine learning models from Scikit Learn, as well as integrating R as a sub process to gain access to the robust forecast library, incorporating the auto.arima and tbats models. We create our own Python class called Forecaster that stores all of the relevant information about the predictions, including error metrics, model forms, hyperparameter selections, and the forecasts themselves. The module is written in such a way that models can easily be added to the framework for increasing accuracy. |
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