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Learn how to harness the power of ChatGPT to streamline data analysis, accelerate model development, and unlock innovative solutions to real-world problems.
Book Description
Unlock the future of machine learning by mastering Google Colab, trusted by over 5 million data scientists, and ChatGPT, powering 100 million users worldwide. This book bridges the latest in AI with practical, hands-on applications for data science.
With these game-changing tools at your command, you'll be able to streamline complex workflows, automate tedious tasks, and propel your AI skills to new heights-making machine learning faster, smarter, and more accessible than ever before.
Table of Contents
1. Introduction to ChatGPT
2. ChatGPT for Data Science and Machine Learning
3. Fundamentals of Statistics for Data Science
4. Missing Values and Outliers
5. Relation Between Variables and Charts
6. Data Preparation
7. Training and Evaluation
8. Fine Tuning, Features Selection, and Final Model
9. Data Preparation and Training
10. Fine Tuning and Final Model
11. Data Analysis and Dataset Manipulation (NLP)
12. Sentiment Analysis and Predictions
13. ChatGPT-4 for a Completely Automated Data Science Workload
14. Customizing GPT for Applications
15. Takeaways and Conclusions
Index
Book Description
Unlock the future of machine learning by mastering Google Colab, trusted by over 5 million data scientists, and ChatGPT, powering 100 million users worldwide. This book bridges the latest in AI with practical, hands-on applications for data science.
With these game-changing tools at your command, you'll be able to streamline complex workflows, automate tedious tasks, and propel your AI skills to new heights-making machine learning faster, smarter, and more accessible than ever before.
Table of Contents
1. Introduction to ChatGPT
2. ChatGPT for Data Science and Machine Learning
3. Fundamentals of Statistics for Data Science
4. Missing Values and Outliers
5. Relation Between Variables and Charts
6. Data Preparation
7. Training and Evaluation
8. Fine Tuning, Features Selection, and Final Model
9. Data Preparation and Training
10. Fine Tuning and Final Model
11. Data Analysis and Dataset Manipulation (NLP)
12. Sentiment Analysis and Predictions
13. ChatGPT-4 for a Completely Automated Data Science Workload
14. Customizing GPT for Applications
15. Takeaways and Conclusions
Index
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