Where to start with Data Science
There is now way to be taught to be data scientist, but you can learn how to become one yourself. There is no right way, but there is a way, which was adopted by a number of data scientists and it goes through online courses (MOOC). Following suggested order is not required, but might be helpful.
Best resources to study Data Science /Machine Learning
1. Andrew Ng’s Machine Learning (https://www.coursera.org/learn/machine-learning).
2. Geoffrey Hinton’s Neural Networks for Machine Learning (https://www.coursera.org/learn/neural-networks).
3. Probabilistic Graphical Models specialisation on Coursera from Stanford (https://www.coursera.org/specializations/probabilistic-graphical-models).
4. Learning from data by Caltech (https://work.caltech.edu/telecourse.html).
5. CS229 from Stanford by Andrew Ng (http://cs229.stanford.edu/materials.html)
6. CS224d: Deep Learning for Natural Language Processing from Stanford (http://cs224d.stanford.edu/syllabus.html).
7. CS231n: Convolutional Neural Networks for Visual Recognition from Stanford (http://cs231n.stanford.edu/syllabus.html).
8. Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville (http://www.deeplearningbook.org/).
9. Machine Learning Yearning by Andrew Ng (http://www.mlyearning.org/).
#books #wheretostart #mooc
There is now way to be taught to be data scientist, but you can learn how to become one yourself. There is no right way, but there is a way, which was adopted by a number of data scientists and it goes through online courses (MOOC). Following suggested order is not required, but might be helpful.
Best resources to study Data Science /Machine Learning
1. Andrew Ng’s Machine Learning (https://www.coursera.org/learn/machine-learning).
2. Geoffrey Hinton’s Neural Networks for Machine Learning (https://www.coursera.org/learn/neural-networks).
3. Probabilistic Graphical Models specialisation on Coursera from Stanford (https://www.coursera.org/specializations/probabilistic-graphical-models).
4. Learning from data by Caltech (https://work.caltech.edu/telecourse.html).
5. CS229 from Stanford by Andrew Ng (http://cs229.stanford.edu/materials.html)
6. CS224d: Deep Learning for Natural Language Processing from Stanford (http://cs224d.stanford.edu/syllabus.html).
7. CS231n: Convolutional Neural Networks for Visual Recognition from Stanford (http://cs231n.stanford.edu/syllabus.html).
8. Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville (http://www.deeplearningbook.org/).
9. Machine Learning Yearning by Andrew Ng (http://www.mlyearning.org/).
#books #wheretostart #mooc
Coursera
Probabilistic Graphical Models
Offered by Stanford University. Probabilistic Graphical ... Enroll for free.
Great collections of Data Science learning materials
The list includes free books and online courses on range of DS-related disciplines:
Machine learning (#ML)
Deep Learning (#DL)
Reinforcement learning (#RL)
#NLP
Tutorials on #Keras, #Tensorflow, #Torch, #PyTorch, #Theano
Notable researchers, papers and even #datasets. It is a great place to start reviewing your knowledge or learning something new.
Link: https://hackmd.io/@chanderA/aiguide
#wheretostart #entrylevel #novice #studycontent #studymaterials #books #MOOC #meta
The list includes free books and online courses on range of DS-related disciplines:
Machine learning (#ML)
Deep Learning (#DL)
Reinforcement learning (#RL)
#NLP
Tutorials on #Keras, #Tensorflow, #Torch, #PyTorch, #Theano
Notable researchers, papers and even #datasets. It is a great place to start reviewing your knowledge or learning something new.
Link: https://hackmd.io/@chanderA/aiguide
#wheretostart #entrylevel #novice #studycontent #studymaterials #books #MOOC #meta