Moscato Rosario. Mastering ChatGPT and Google Colab for Machine Learning: Automate AI Workflows and Fast-Track Your Machine Learning Tasks with the Power of ChatGPT, Google Colab, and Python

Orange Education Pvt Ltd, AVA, 2024. — 441 p. — ASIN: B0DHNFS35P.Освоение ChatGPT и Google Colab для машинного обучения: автоматизируйте рабочие процессы ИИ и ускоряйте выполнение задач машинного обучения с помощью ChatGPT, Google Colab и PythonLearn how to harness the power of ChatGPT to streamline data analysis, accelerate model development, and unlock innovative solutions to real-world problems.Key Features
- Step-by-step progression from foundational machine learning concepts to advanced techniques using ChatGPT and Google Colab.
- Clear and detailed instructions for data preparation, model training, and evaluation, simplifying complex machine learning tasks.
- Extensive use of Google Colab for coding and experimentation, providing a real-world platform to apply learned techniques effectively.
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.Each chapter unfolds a specific aspect of Data Science and Machine Learning, seamlessly integrated with ChatGPT’s free version capabilities. The foundational chapters introduce key Machine Learning concepts, while advanced sections explore topics such as natural language processing (NLP), sentiment analysis, and predictive analytics—all illustrated with real-world examples and interactive exercises. The later chapters focus on optimizing tasks using the more powerful paid version of ChatGPT, culminating in the creation of a custom GPT named “Data Scientist” to tackle specialized challenges.Imagine having a personal assistant that not only helps you generate code but also completes it for you. Well, with ChatGPT, that’s exactly what you get. Let’s explore how you can harness the power of ChatGPT to create Python code tailored for analyzing datasets, preprocessing them, and building Machine Learning models, even with the free version. ChatGPT, in its free version, might surprise you with its ability to understand and generate Python code snippets. By providing it with context about your dataset and the task you want to perform, you can prompt it to generate code to help you with your analysis. For instance, let’s say you have a dataset containing information about housing prices and you want to build a Machine Learning model to predict these prices based on various features like location, number of bedrooms, and square footage. You can engage with ChatGPT by explaining your dataset and your objective, and it can provide you with Python code snippets to preprocess your data, build models, and even evaluate their performance.
Once you have the code generated by ChatGPT, you can easily test and run it using a platform like Google Colab.Additionally, the book includes a section on best practices, expert tips, and interview questions, making it a comprehensive resource for aspiring data scientists and seasoned professionals alike.What you will learn
- Learn to integrate and optimize ChatGPT and Google Colab for enhanced data science tasks.
- Master techniques for preparing and cleaning data for analysis.
- Gain a solid grasp of statistical concepts essential for data science.
- Learn the processes for training, evaluating, and refining machine learning models.
- Perform data analysis and preprocessing using natural language processing techniques.Introduction to ChatGPT
ChatGPT for Data Science and Machine Learning
Fundamentals of Statistics for Data Science
Missing Values and Outliers
Relation Between Variables and Charts
Data Preparation
Training and Evaluation
Fine Tuning, Features Selection, and Final Model
Data Preparation and Training
Fine Tuning and Final Model
Data Analysis and Dataset Manipulation (NLP)
Sentiment Analysis and Predictions
ChatGPT-4 for a Completely Automated Data Science Workload
Customizing GPT for Applications
Takeaways and Conclusions