Onzo – ChatGPT for Artificial Intelligence

This guide is for anyone who seeks to learn and explore Artificial Intelligence (AI) with the assistance of ChatGPT.

Whether you are a student, a professional in the field, an AI enthusiast, or simply curious about the fascinating world of AI, ChatGPT is here to support your learning journey.

---

Onzo – ChatGPT for Artificial Intelligence

Introducing ChatGPT for Artificial Intelligence

Welcome to the world of ChatGPT – an advanced language model based on Artificial Intelligence (AI), designed to assist and enhance your learning journey in the fascinating field of AI. Developed by OpenAI, ChatGPT leverages the power of the GPT-3.5/4 architecture, making it a versatile and intelligent language model capable of generating human-like responses to a wide range of prompts.

How can ChatGPT Help You Learn AI?

ChatGPT serves as an invaluable learning companion for anyone interested in delving into the world of Artificial Intelligence. By utilizing the provided prompts, you can embark on a journey of exploration, discovery, and understanding of AI concepts, technologies, and applications. The pre-existing AI prompts have been carefully crafted to guide your learning experience and provide you with comprehensive insights into key AI topics.

Ready-to-Use AI Prompts

ChatGPT offers a collection of ready-to-use AI prompts that serve as valuable starting points for your learning endeavors. These prompts encompass a wide spectrum of AI topics, ranging from basic concepts like machine learning and neural networks to advanced subjects such as AI ethics, explainable AI, and AI in business. Simply engage with ChatGPT using these prompts, and you will receive concise and informative responses to expand your knowledge.

Generating Information and Guidelines

In addition to the predefined prompts, ChatGPT can also generate custom information and guidelines for your specific learning needs. You can request ChatGPT to explain specific AI terminologies, provide examples of AI applications in real-world scenarios, or offer guidance on best practices for AI development. The versatility of ChatGPT ensures that you receive tailored and relevant information to accelerate your learning process.

Embrace Interactive Learning

Through interactive conversations with ChatGPT, you can ask questions, seek clarification, and explore AI concepts in an engaging and dynamic manner. This interactive learning experience enables you to grasp complex concepts intuitively, and ChatGPT is here to support your quest for AI knowledge every step of the way.

Leverage ChatGPT’s Knowledge Cutoff

It is essential to note that ChatGPT’s responses are based on a knowledge cutoff up to September 2021. While it holds a wealth of information within that timeframe, please be aware that it may not be aware of more recent developments in the AI field.

Begin Your AI Learning Journey with ChatGPT

Whether you are a student, professional, or AI enthusiast, ChatGPT is your gateway to exploring the wonders of Artificial Intelligence. From fundamental concepts to cutting-edge trends, ChatGPT’s AI prompts are your key to unlocking a deeper understanding of AI technologies, applications, and implications. So, let’s embark on this educational adventure together and unleash the potential of AI learning with ChatGPT!

The bundle includes:

• Introduction to AI
• ChatGPT for Machine Learning Algorithms
• ChatGPT for Deep Learning Algorithms
• ChatGPT Data Science – Master Edition
• ChatGPT Python
• ChatGPT for Python Libraries – Gold Bundle

Who is this guide for? 

This guide is for anyone who seeks to learn and explore Artificial Intelligence (AI) with the assistance of ChatGPT. Whether you are a student, a professional in the field, an AI enthusiast, or simply curious about the fascinating world of AI, ChatGPT is here to support your learning journey. The guide is designed to cater to learners of all levels, offering ready-to-use AI prompts and generating custom information to provide comprehensive insights into various AI concepts, technologies, and applications. So, if you are eager to dive into the world of AI and engage in interactive conversations to enhance your understanding, this guide is the perfect companion for you. Let’s embark on this educational adventure together and discover the potential of AI learning with ChatGPT!

Introduction to AI

• AI Applications
• AI Ethics
• AI Technologies
• AI in Business
• AI Research and Trends
• AI and Society
• AI in Entertainment
• AI and Sustainability
• AI and Future Predictions
• AI Explained

ChatGPT for Machine Learning Algorithms

• Supervised Learning Algorithms
• Unsupervised Learning Algorithms
• Deep Learning Algorithms
• Natural Language Processing (NLP) Algorithms
• Reinforcement Learning Algorithms
• Ensemble Learning
• Recommendation Systems
• Time Series Analysis
• Machine Learning Model Evaluation and Optimization
• Explainable AI (XAI)

ChatGPT for Deep Learning Algorithms

• Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transformers
• Generative Adversarial Networks (GANs)
• Autoencoders
• Optimization Algorithms
• Regularization Techniques
• Deep Reinforcement Learning
• Quantum Deep Learning

ChatGPT Data Science – Master Edition

Data Analyst R

• Introduction to R
• Introduction to the Tidyverse
• Data Manipulation with dplyr
• Joining Data with dplyr
• Introduction to Statistics in R
• …

Data Scientist R

• Data Communication Concept
• Cleaning Data in R
• Working with Dates and Times in R
• Introduction to Regression in R
• Supervised Learning in R: Classification
• Supervised Learning in R: Regression
• Unsupervised Learning
• …

Data Analyst Python

• Data Manipulation with Pandas
• Joining Data with Pandas
• Introduction to Statistics in Python
• Importing & Cleaning Data with Python
• Exploratory Data Analysis in Python
• Sampling in Python
• …

Data Scientist Python

• Python Programming for Data Science
• Writing Functions in Python
• Python Libraries for Data Science
• Machine Learning Algorithms in Python
• Supervised Learning with scikit-learn
• Machine Learning with Tree-Based Models in Python
• Python for Data Science in the Cloud
• …

Quantitative Analyst R

• Manipulating Time Series with xts and zoo in R
• Arima models in R
• Portfolio analysis and optimization in R
• Risk Management and Simulation with R
• Visualizing Time Series Data in R
• Bond Valuation and Analysis in R
• Financial Trading in R
• …

Data Engineer Python

• Data Ingestion
• Data Processing
• Data Modeling
• Data Pipelines
• Data Quality and Governance
• Data Visualization and Reporting
• Performance Optimization and Scalability
• …

Data Analyst PowerBI

• Data visualization in Power BI
• DAX (Data Analysis Expressions) in Microsoft Power BI
• Power BI Desktop features
• Power BI Query Editor
• Power BI data sources
• Power BI dashboards and reports
• Power BI integration and automation
• …

Data Analyst Tableau

• 10 categories

Statistician

• 10 categories

ML Scientist

• 10 categories

and much more

ChatGPT Python

• Exploring the Basics
• Data Structures and Manipulation
• Reading and Writing Data
• Data Cleaning and Preprocessing
• Exploratory Data Analysis (EDA)
• Statistical Analysis
• Machine Learning for Data Analysis
• Time Series Analysis
• Text Analysis
• Advanced Topics

Exercises

• Python Fundamentals
• Control Flow
• Functions
• Data Structures
• File Handling
• String Manipulation
• Error Handling
• Object-Oriented Programming (OOP)
• Modules and Libraries
• Advanced Concepts

ChatGPT for Python Libraries – Gold Bundle

Pandas

• Pandas Basics
• DataFrame Operations
• Data Cleaning with Pandas
• Data Visualization with Pandas
• Pandas and Data Analysis
• Time Series Analysis with Pandas
• Data Transformation with Pandas
• Grouping and Aggregation
• Pandas Best Practices
• Pandas Case Studies and Projects

NumPy

• Introduction and Basics
• Array Manipulation
• Mathematical Operations
• Array Broadcasting
• Array Indexing and Selection
• Performance Optimization
• Data Analysis and Statistics
• Linear Algebra
• File I/O and Integration
• Advanced NumPy Features

Keras

• Introduction to Keras
• Keras Tutorials
• Model Building with Keras
• Keras Layers and Architectures
• Transfer Learning with Keras
• Hyperparameter Tuning in Keras
• Keras Callbacks
• Keras and TensorFlow Integration
• Keras in Real-World Projects
• Keras Updates and News

TensorFlow

• Introduction to TensorFlow
• Tutorials and How-tos
• Tips and Tricks
• Model Showcase
• Community Spotlights
• Performance Optimization
• Error Handling and Debugging
• Integration with Other Libraries
• Data Visualization with TensorFlow

Scrapy

• Getting Started
• Spider Development
• XPath and CSS Selectors
• Middleware and Pipelines
• Crawling Best Practices
• Using Proxies and User Agents
• Scrapy Extensions and Customizations
• Real-World Use Cases

SciPy

• Introduction to SciPy
• Key Modules and Functions
• Use Cases and Applications
• Tutorials and How-tos
• Performance and Optimization
• Data Visualization with SciPy
• Comparison with Other Libraries
• Tips and Tricks

PyTorch

• Tutorials for Beginners
• Advanced Tutorials
• Model Building and Training
• PyTorch and Computer Vision
• Natural Language Processing (NLP) with PyTorch
• PyTorch and Reinforcement Learning
• Deployment and Production
• PyTorch Ecosystem
• PyTorch and Research

LightGBM

• Introduction and Basics
• Installation and Setup
• Feature Engineering
• Hyperparameter Tuning
• Model Training and Evaluation
• Advanced Features
• Model Interpretability
• Integration with Other Libraries
• Real-World Applications

Theano

• Introduction to Theano
• Theano Tutorials
• Advanced Theano Techniques
• Comparisons with Other Libraries
• Optimization and Performance
• Real-world Use Cases
• Debugging and Troubleshooting
• Theano Tips and Best Practices

Scikit Learn

• Introduction to Scikit Learn
• Key Features and Functions
• Tutorials and How-To Guides
• Data Preprocessing with Scikit Learn
• Model Evaluation and Metrics
• Ensemble Methods
• Hyperparameter Tuning
• Handling Imbalanced Data
• Working with Text Data
• Deploying Machine Learning Models

Introducing ChatGPT for Data Engineering

Data engineering is the backbone of modern data-driven organizations. It involves the processes of collecting, transforming, and delivering data to enable valuable insights, decision-making, and machine learning. Data engineers play a pivotal role in designing and maintaining data pipelines, ensuring data quality, and facilitating seamless data access for analysts and data scientists.

Our goal with ChatGPT for Data Engineering is to provide a comprehensive resource that empowers you to:

1. Learn Key Concepts: Gain a solid understanding of fundamental data engineering concepts, such as ETL (Extract, Transform, Load), data warehousing, data quality, and real-time data processing.
2. Master Best Practices: Access best practices and industry insights to build efficient and scalable data pipelines, implement robust data governance, and enhance data security.
3. Navigate Tools and Technologies: Explore a variety of data engineering tools and technologies, including cloud platforms, big data frameworks, and data integration solutions, with guidance on selecting the right tools for your projects.
4. Achieve Compliance: Understand the importance of data compliance and learn how to align your data engineering efforts with regulations like GDPR, HIPAA, and more.
5. Enhance Career Skills: Discover tips and strategies for career development in the data engineering field, including skill enhancement, networking, and staying up-to-date with industry trends.

In this guide, you will find a range of prompts that serve different purposes. Some prompts are ready-made, providing immediate answers to common questions, while others generate detailed information and guidelines for more in-depth learning. These prompts are designed to foster a dynamic and interactive learning experience, allowing you to explore various aspects of data engineering at your own pace.

Whether you’re seeking specific answers or looking to deepen your knowledge, ChatGPT for Data Engineering is here to assist you on your journey. Let’s embark on this learning adventure together, unlocking the vast potential of data engineering and paving the way for data-driven success.

What’s in the guide?

• Foundations
• Python
• Java
• Databricks
• Apache Spark
• Kafka
• Kubernetes
• Docker
• SQL
• NoSQL
• MongoDB
• Software Engineering
• DevOps
• AWS Machine Learning
• Google Cloud Machine Learning
• Microsoft Azure Machine Learning

Total prompts: 5000+

How does it work?

1. Pay what you want (or enter $0 if the free version)
2. Go to the Notion page containing the guide and bookmark it
3. Or choose to duplicate the page into your own Notion workspace to save it
4. You’ll be able to navigate through the directory using the different categories and tags.
5. Bonus: Add your own resources to the guide and keep building!