| 01 - Data preprocessing the telecom dataset.mp4 | 13.6 MB | ||
| 01 - Data preprocessing the telecom dataset.srt | 10.1 KB | ||
| 01 - Data with a structure.mp4 | 8.2 MB | ||
| 01 - Data with a structure.srt | 6.3 KB | ||
| 01 - Deep learning Predict customer lifetime value.mp4 | 9.7 MB | ||
| 01 - Deep learning Predict customer lifetime value.srt | 6.9 KB | ||
| 01 - Exploratory data analysis (EDA).mp4 | 10 MB | ||
| 01 - Exploratory data analysis (EDA).srt | 7.2 KB | ||
| 01 - Introduction to capstone and use case.mp4 | 8.5 MB | ||
| 01 - Introduction to capstone and use case.srt | 5.9 KB | ||
| 01 - Leverage generative AI for analytics and insights.mp4 | 3.7 MB | ||
| 01 - Leverage generative AI for analytics and insights.srt | 1.1 KB | ||
| 01 - Next steps.mp4 | 2.9 MB | ||
| 01 - Next steps.srt | 2.9 KB | ||
| 01 - Overview of predictive and generative AI.mp4 | 10.6 MB | ||
| 01 - Overview of predictive and generative AI.srt | 6.4 KB | ||
| 01 - We live in a data-driven world!.mp4 | 9.1 MB | ||
| 01 - We live in a data-driven world!.srt | 5.3 KB | ||
| 02 - Challenge Perform exploratory data analysis.mp4 | 4 MB | ||
| 02 - Challenge Perform exploratory data analysis.srt | 3.2 KB | ||
| 02 - Challenge Predict customer lifetime value.mp4 | 21.6 MB | ||
| 02 - Challenge Predict customer lifetime value.srt | 10.8 KB | ||
| 02 - Challenge Predict media channel sales using Keras.mp4 | 1.3 MB | ||
| 02 - Challenge Predict media channel sales using Keras.srt | 614.4 B | ||
| 02 - Data without a structure.mp4 | 11.8 MB | ||
| 02 - Data without a structure.srt | 7.4 KB | ||
| 02 - Introduction to text preprocessing.mp4 | 10.6 MB | ||
| 02 - Introduction to text preprocessing.srt | 7.6 KB | ||
| 02 - Our use case.mp4 | 6.4 MB | ||
| 02 - Our use case.srt | 3.7 KB | ||
| 02 - What is deep learning.mp4 | 16.4 MB | ||
| 02 - What is deep learning.srt | 7 KB | ||
| 02 - What you should know.mp4 | 2.1 MB | ||
| 02 - What you should know.srt | 1.5 KB | ||
| 03 - Challenge Data preprocessing the telecom dataset.mp4 | 3.1 MB | ||
| 03 - Challenge Data preprocessing the telecom dataset.srt | 2.1 KB | ||
| 03 - Generative modeling use cases.mp4 | 6.7 MB | ||
| 03 - Generative modeling use cases.srt | 5.3 KB | ||
| 03 - How to use the challenge exercise files.mp4 | 6.2 MB | ||
| 03 - How to use the challenge exercise files.srt | 2.9 KB | ||
| 03 - Raw data is messy.mp4 | 13.4 MB | ||
| 03 - Raw data is messy.srt | 5.8 KB | ||
| 03 - Solution Perform exploratory data analysis.mp4 | 12.4 MB | ||
| 03 - Solution Perform exploratory data analysis.srt | 7.6 KB | ||
| 03 - Solution Predict customer lifetime value.mp4 | 7.9 MB | ||
| 03 - Solution Predict customer lifetime value.srt | 4 KB | ||
| 03 - Solution Predict media channel sales using Keras.mp4 | 3.7 MB | ||
| 03 - Solution Predict media channel sales using Keras.srt | 2.1 KB | ||
| 03 - Using simple Python code to check your data.mp4 | 4.3 MB | ||
| 03 - Using simple Python code to check your data.srt | 3.5 KB | ||
| 04 - Optional challenge Generate sentiments using BERT.mp4 | 1.7 MB | ||
| 04 - Optional challenge Generate sentiments using BERT.srt | 1 KB | ||
| 04 - Predictive modeling use cases.mp4 | 6.2 MB | ||
| 04 - Predictive modeling use cases.srt | 4.9 KB | ||
| 04 - Python for data preprocessing with Pandas and Matplotlib.mp4 | 9.7 MB | ||
| 04 - Python for data preprocessing with Pandas and Matplotlib.srt | 7.5 KB | ||
| 04 - Role of data in the machine learning workflow.mp4 | 3.6 MB | ||
| 04 - Role of data in the machine learning workflow.srt | 2.7 KB | ||
| 04 - Solution Data preprocessing the telecom dataset.mp4 | 17.3 MB | ||
| 04 - Solution Data preprocessing the telecom dataset.srt | 9.1 KB | ||
| 05 - Challenge Load and check the data using Python.mp4 | 5.8 MB | ||
| 05 - Challenge Load and check the data using Python.srt | 3.8 KB | ||
| 05 - Solution Generate sentiments using BERT.mp4 | 9.8 MB | ||
| 05 - Solution Generate sentiments using BERT.srt | 4.7 KB | ||
| 06 - Solution Load and check the data using Python.mp4 | 4.8 MB | ||
| 06 - Solution Load and check the data using Python.srt | 2.6 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 68 total files | |||
Deep Learning and Generative AI: Data Prep, Analysis, and Visualization with Python
https://DevCourseWeb.com
Released 10/2024
With Gwendolyn Stripling
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 1h 56m 27s | Size: 267 MB
Learn the knowledge and practical skills needed to effectively utilize deep learning techniques using the Python programming language.
Course details
If you’re looking to keep up with the rapid advancements and applications of deep learning techniques, this course provides a comprehensive guide that can help you stay relevant and competitive in the evolving landscape of AI and data-driven technologies.
Instructor Gwendolyn Stripling shows you how to transform raw data into valuable insights and build the foundation for cutting-edge AI applications. The course focuses on the concepts, with minimal coding required, so even if you’re not an experienced coder, Gwendolyn shows you how to use simple Python code to work with data. Test your learning with a series of challenges, and cap off the course with building and evaluating a predictive and generative model.
Homepage
| torrent name | size | uploader | age | seed | leech |
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| 4 GB | freecoursewb | 2 months | 12 | 10 | |
| 498 MB | freecoursewb | 3 months | 16 | 2 | |
| 4 GB | freecoursewb | 5 months | 24 | 10 | |
| 2.5 GB | freecoursewb | 7 months | 14 | 3 | |
| 785.3 MB | freecoursewb | 8 months | 13 | 1 |
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