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DS&ML Learning Resources

Data Structures and Algorithms :

The following resources are for learning basic Algorithm/Data-Structures Stuff that is invaluable for any field of CS. We shall need lots of basic concepts from these domains and a strong base in this regard is required for serious research in any subfield of CS, especially math/computation intensive fields like Data Science and ML/DL.

Test Yourself :
Visualization and Application using p5.js :

Python Tutorials :

Data Science Tech Stack(Basic Python Libraries)

  • Numpy
  • Pandas
  • Matplotlib
  • SeaBorn

Machine Learning :

Courses :

Reference Books :

Tech Stack :

  • Python
  • Numpy
  • Pandas
  • SciPy
  • Scikit Learn
  • Matplotlib

Try to implement basic ML Algorithms on your own using numpy. Afterwards you may use Scikit Learn and compare the results. ( Refer to DataCamp and other online tutorials, including YouTube if you get stuck)


Deep Learning :

Courses and MOOCs :

Maths Needed For Deep Learning

Reference Books :

Tech Stack :

  • Keras
  • Tensorflow
  • TFLearn
  • Pytorch
  • Theano

Additional Misc Resources :

For Deep Learning, trying to implement a basic 3 layer Neural Network in Numpy for solving the XOR problem proves to be a great kickstarting task. ( Refer to DL CheatSheet and other online tutorials, including YouTube if you get stuck) Additionally go over to Kaggle and try to solve a few problems, taking cues from solved ipython notebooks and kernels.

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List of relevant resources for Gen-Y DS&ML Lab Members

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