CodeAcademy AI Course with delivered notebooks through sessions & additional presentations .
Solutions to the exercises and more notes of the program within as well.
Overview
Artificial intelligence involves the development of systems capable of performing tasks that normally require human intelligence. These systems can now mimic routine, non-creative behaviors and automate certain processes. Knowledge of artificial intelligence, which is called the technology of the future, gives a specialist a huge advantage. Artificial intelligence technology with a long enough history is still constantly growing and changing. There are great opportunities in the field of artificial intelligence – after all, it can expand human possibilities in a way that is still difficult to imagine today.
More on the presentation of the program can be found here and refer to this menu for further information.
- Table of contents
- Program
- Jupyter in Browser
- Additional Material and Set Up
- Worthy Repositories
- Worthy Tools
- Notebooks In Progress
- Notebooks Finished
- Problems
- Solutions
- Examples
- Notes
- Public
- Photo
- License
Details about the program can be found in the original CodeAcademy website under here.
You could run Jupyter through browser by doing so here and reading more about it in this article.
If you find difficulty in running Jupyter Notebook through Browser then you could use Google Colab by clicking here. Functionalities of both machines are similar.
Taught material that is not found in this repository can be inspected through here as that's additional course's repository from a colleague of this AI course. There you can also find installation notes.
Some worthy Python repositories related to the AI course with all details included can be found here:
| Title | Description | Link |
|---|---|---|
| Web Scraper | Web crawling/scraping with Python | https://github.com/aurimas13/Web-Scraper |
Worthy websites that include cheat codes for Python, Machine Learning, Deep Learning, Neural Networks and what not apart from other worthy tools like GitHub repositories or books while you are learning. Updated constantly when a worthy material is found to be shared with Code Academy students.
| Title | Description | Link |
|---|---|---|
| Python Cheatsheet | The Python Cheatsheet based on the book "Automate the Boring Stuff with Python" and many other sources. | https://github.com/wilfredinni/python-cheatsheet |
| Machine Learning Algorithms Cheatsheet | The Machine Learning Cheatsheet explaining various models briefly. | https://www.accel.ai/anthology/2022/1/24/machine-learning-algorithms-cheat-sheet |
| GitHub on Coursera's Deep Learning Course | GitHub Repo for Coursera's Deep Learning Specialization by deeplearning.ai | https://github.com/aurimas13/Coursera-Deep-Learning-Specialization |
| Notes on Cousera's Deep Learning Course | Lecture Notes for Coursera's Deep Learning Specialization by deeplearning.ai | https://drive.google.com/drive/folders/1X9qp9KsGs6IuVgzZJZvMJvVzgxPjhNKN?usp=share_link |
Notebooks in Progress can be found here.
Notebooks Finished can be found here. This folder also contains demonstrations done through lectures or any other demonstrations related t the course.
Problems can be found here.
Solutions to the problems can be found here.
Questions asked through lectures and answers to them by showing can be found here
Additional notes that we covered through lectures or additional material that I said about can be found here.
Public folder contains two files:
The logo of the CodeAcademy can be found here.
The MIT LICENSE can be found here.