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.
- Daniel Shiffman : Algorithms and Graphs
- Daniel Shiffman : Genetic Algorithms
- University of Michigan - Python (First Three Courses of the Specialization Only, for now)
- DataCamp Python[RECOMMENDED]
- Socratica Python[USEFUL]
- Numpy
- Pandas
- Matplotlib
- SeaBorn
- Machine Learning Coursera[NECESSARY]
- MIT 6.034
- Machine Learning Crash Course by Google[RECOMMENDED]
- Kaggle - Machine Learning[QUICKSTARTER]
- 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)
- DeepLearning.AI Coursera[NECESSARY]
- Deep Learning by Google on Udacity[RECOMMENDED]
- Kaggle - Deep Learning
- Keras
- Tensorflow
- TFLearn
- Pytorch
- Theano
- Youtube
- Blogs :
- Sebastian Ruder
- Towards Data Science(Medium)
- Other Medium Publications including ML and IM
- Machine Learning Mastery
- Christopher Olah
- Machine Learning Subreddit
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.