ML-DL Problem Statements

This Repository contains a Compilation of Machine Learning and Deep Learning Problem Statements with Solutions and Full Scale Project, divided into week-wise modules for guiding and helping learners follow the right path.

Website Link: https://shubham99bisht.github.io/Skillconnect-Community/

Solutions

Each folder contains a list of problem statements divided into three categories Basic, Intermediate and Advanced along with official solutions.
The README file available inside each folder provides links to Best Solutions from the Community.

How to play with these solutions on Colab

Open Google Colab and follow these steps:

1. Click on File -> Open Notebook
2. Navigate to Github tab
3. Enter the url to the .ipynb file (Jupyter notebooks)
4. See Picture here

Resources References with Learning Plan

The Resources.md file contains links to external resources for learning and solving these challenges.

This Repository contains a list of Reference Links to Online Resources for learning Machine Learning, Deep Learning, Computer Vision and other prerequisites like Maths and Frameworks.

This guide is divided into four sections:

1. General Tips
2. Beginners - For those who don't have knowledge about any programming language.
3. Intermediate - For those who have some basic idea about what ML is.
4. Advanced - For those who have some practical experience and want to explore advanced Frameworks like Tensorflow.

NOTE:

1. This guide contains list in the order in which I learnt them, please go through the list and follow the order which suits best for you. Best wishes for your journey into ML.

2. For hands-on experience please find the corresponding practicals mentioned in Week-wise modules in the main directory.

3. I'll keep updating this page so makes sure you star mark the repository and visit it frequently.

1. General Tips

Why you should work on projects?

1. https://towardsdatascience.com/how-to-build-a-data-science-portfolio-5f566517c79c

Here's a good collection of free fundamental Mathematics courses required for Machine learning

1. https://towardsdatascience.com/4-free-maths-courses-to-do-in-quarantine-and-level-up-your-data-science-skills-f815daca56f7

Software Development

1. SQL : https://lnkd.in/feTgrpd
2. Git : https://lnkd.in/fgVr72x
3. Flask : https://lnkd.in/ffPvTHN
4. Django : https://lnkd.in/f_fJtK5
5. ML : https://github.com/ayonroy2000/100DaysOfMLCode

2. Beginners

Python 3 course

1. https://courses.edx.org/courses/course-v1:Microsoft+DAT208x+5T2016/course/
2. https://pythonprogramming.net/introduction-learn-python-3-tutorials/
3. https://lnkd.in/fE3Dbqq

3. Intermediate

Basics of Neural Networks:

1. https://www.youtube.com/watch?v=ZzWaow1Rvho&list=PLxt59R_fWVzT9bDxA76AHm3ig0Gg9S3So

Machine Learning

1. https://lnkd.in/fPnGqwV
2. Andrew Ng, Coursera: https://www.coursera.org/learn/machine-learning

Statistics

1. https://lnkd.in/fp_4JRd

4. Advanced

Computer Vision (CNN) CS231n by Stanford

1. http://cs231n.stanford.edu/syllabus.html

TensorFlow Series from Scratch to Advanced on PluralSight

1. https://app.pluralsight.com/paths/skills/tensorflow

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