Chat GPT 4

Could our mismanagement of our planet result in an AI takeover? Exponential, greed funded performance growth learning on internet prejudiced and misinformed data, what could go wrong? AIs’ rapid evolution into a higher life form is unlikely to submit human dominance once . Someone releasing an AI product deciding to end humanity is nearly possible. Until that happens, AI technologies like GPT 4 have the potential to solve problems and possibly even save the world. The efforts and progress we make in helping solve problems like hunger, climate change and poverty just might help a near future AI decide if we are worth saving.

I have been studying Python for a while from multiple sources so a asked GPT 4 for for assistance as both a practice exercise in simple prompting and to help me evaluate where my learning holes might be. The rest of this page contains the results of my request. As I verify these results and resubmit partial requests I will update this page and note actual corrections if found.

[1] “Step 1: Learn Python If you want to learn data science from scratch, the first thing you need to do is learn how to code. Pick a programming language (either Python or R), and start learning. I suggest starting out with Python because it is more widely used than R.” URL: https://towardsdatascience.com/a-complete-data-science-roadmap-in-2021-77a15d6be1d9

[2] “A learning roadmap is an extension of a curriculum. It charts out a multi-level skills map with details about what skills you want to hone, how you will measure the outcome at each level, and techniques to further master each skill.” URL: https://www.freecodecamp.org/news/data-science-learning-roadmap/

3] “2 Learn Python 3 Learn the command-line 4 A Data Science Working environment 5 Reading data 6 Crunching data 7 Visualization 8 Keep learning The Python Fundamentals Course For Beginners Python Fundamentals I is a course for beginners that will get you started with Python in no time.” URL: https://python.land/data-science Current date: 4/27/2023 Instructions: Using the provided web search results, write a comprehensive reply to the given query. Make sure to cite results using [[number](URL)] notation after the reference. If the provided search results refer to multiple subjects with the same name, write separate answers for each subject. Query: Please create a roadmap for learning Python for Data Science (Deep Learning) in 3 months complete with links to all relevant sources.

To create a comprehensive 3-month roadmap for learning Python for Data Science, specifically focusing on Deep Learning, follow these steps and resources:

Learn Python: Start by learning the basics of Python, a versatile and widely-used programming language for data science. Refer to the Python Fundamentals Course for Beginners provided in [3].

Learn the command-line: Familiarize yourself with the command-line to navigate and manage your files and folders efficiently. This is an essential skill for any data scientist.

Data Science Working Environment: Set up a working environment for data science projects. Install necessary libraries and tools such as Jupyter Notebook or Google Colab, which will allow you to write and test your code interactively.

Reading Data: Learn how to read and manipulate data using Python libraries like Pandas and NumPy. These libraries provide essential data manipulation functionalities for data science. Refer to the resources provided in [1] and [3] for more guidance.

Crunching Data: Get comfortable with data analysis and statistical methods using Python. Learn how to clean, preprocess, and analyze data using libraries like SciPy and scikit-learn.

Data Visualization: Master data visualization techniques using Python libraries such as Matplotlib, Seaborn, and Plotly to create effective visualizations that communicate your findings.

Introduction to Deep Learning: Gain an understanding of the basics of deep learning, including neural networks, backpropagation, and optimization techniques. Use resources like the Deep Learning Specialization on Coursera to build your foundation.

Deep Learning Libraries: Learn how to use popular deep learning libraries such as TensorFlow and Keras to build, train, and evaluate deep learning models. You can find tutorials and documentation on the official websites for TensorFlow and Keras.

Deep Learning Projects: Practice your deep learning skills by working on real-world projects. Use open datasets from platforms like Kaggle or Google Dataset Search to create your own projects or participate in competitions.

Keep Learning: Continue to expand your knowledge and stay up-to-date with the latest developments in the field of data science and deep learning. Follow blogs, research papers, and online forums to stay current with new techniques, libraries, and tools.

Refer to the learning roadmap provided in [2] to tailor your learning plan according to your needs and goals. Remember to practice regularly, seek feedback from peers, and apply your newly acquired skills to real-world projects.