Github/ml-study-plan
1
0
Fork 0
mirror of https://github.com/python-engineer/ml-study-plan synced 2024-11-23 11:04:57 +00:00
Code Issues Projects Releases Wiki Activity

Added resources for Python, Deep Learning and others

Browse Source 
I have added the following:
1. Automate the Boring Stuff with Python Book
2. Neural Networks series by 3Blue1Brown
3. An article on beginner level datasets
4. An article on the life cycle of a data science project
This commit is contained in:
Nirmalya Misra 2021-09-20 13:32:32 +05:30 committed by GitHub
parent d99704de7d
commit d06bc0aef6
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 5 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
README.md
View File

@ -26,7 +26,8 @@ Watch the video on YouTube for instructions:
- [ ] Statistics
- [ ] [Khan Academy - Statistics Probability](https://www.khanacademy.org/math/statistics-probability)
- [ ] Python
- [ ] [Python Full Course 4 Hours - FreeCodeCamp on YouTube](https://www.youtube.com/watch?v=rfscVS0vtbw)
- [ ] [Book: Automate The Boring Stuff with Python](https://automatetheboringstuff.com/) (Till Chapter 6 for Python Basics, the remaining chapters include the applications of Python)
- [ ] [Python Full Course 4 Hours - FreeCodeCamp on YouTube](https://www.youtube.com/watch?v=rfscVS0vtbw)
- [ ] [Advanced Python - Playlist on YouTube (Python Engineer)](https://www.youtube.com/watch?v=QLTdOEn79Rc&list=PLqnslRFeH2UqLwzS0AwKDKLrpYBKzLBy2)
- [ ] [Numpy - Free Udemy Course](https://www.udemy.com/course/deep-learning-prerequisites-the-numpy-stack-in-python/)
- [ ] Matplotlib
@ -45,6 +46,7 @@ Watch the video on YouTube for instructions:
- [ ] [Introduction to Machine Learning with Python - Andreas Müller](https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413/ref=sr_1_1?crid=WAQPG9CEM3W&keywords=introduction+to+machine+learning+with+python&qid=1584648523&sprefix=introduc%2Caps%2C238&sr=8-1)
### 2. Deep Learning
- [ ] [Basics of Neural Networks, how they learn and some of the involved Mathematics(3Blue1Brown series)](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
- [ ] [Stanford Lecture - Convolutional Neural Networks for Visual Recognition](https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv)
- [ ] Learn PyTorch (or Tensorflow)
- [ ] [pytorch.org official Tutorials](https://pytorch.org/tutorials/)
@ -62,6 +64,7 @@ Optional:
- [ ] Datasets (develop own projects)
- [ ] Competitions (start with Getting started section)
- [ ] [8 Fun Machine Learning Projects For Beginners](https://elitedatascience.com/machine-learning-projects-for-beginners)
- [ ] [Article on Beginner Level Datasets](https://medium.com/machine-learning-india/getting-started-in-data-science-beginner-level-datasets-376ffe60c6fe)
### 4. Prep for Interviews
- [ ] https://github.com/alexeygrigorev/data-science-interviews
@ -81,6 +84,7 @@ Optional:
- https://medium.com/@gordicaleksa/get-started-with-ai-and-machine-learning-in-3-months-5236d5e0f230
- https://towardsdatascience.com/beginners-guide-to-machine-learning-with-python-b9ff35bc9c51
- https://www.fast.ai/2019/01/02/one-year-of-deep-learning/
- https://medium.com/machine-learning-india/the-life-cycle-of-a-data-science-project-d614d8d233b7
GitHub:
- https://github.com/ZuzooVn/machine-learning-for-software-engineers