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

Outdated, but still valuable blog to dive into deep learning:

http://deeplearning.net/tutorial/

#tutorial #wheretostart

Upcoming series of #ML lectures from Columbia Universite will be published on youtube.

If you are looking #wheretostart, this is one of the great places.

YouTuve Playlist: https://www.youtube.com/playlist?list=PL_pVmAaAnxIQGzQS2oI3OWEPT-dpmwTfA
Syllabus: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/

#beginner #novice #entrylevel

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

🎓CS224W: Machine Learning with Graphs

Great course from #Stanford. You still on time to jump at studying from one of the best schools.

Students are introduced to machine learning techniques and data mining tools apt to reveal insights on the social, technological, and natural worlds, by means of studying their underlying network structure and interconnections.

Topics include: robustness and fragility of food webs and financial markets; algorithms for the World Wide Web; graph neural networks and representation learning; identification of functional modules in biological networks; disease outbreak detection.

Link: http://cs224w.stanford.edu
Videos link: http://snap.stanford.edu/class/cs224w-videos-2019/

#MOOC #entrylevel #wheretostart

​​GANs from Scratch 1: A deep introduction.

Great introduction and tutorial. With code in PyTorch and TensorFlow

Link: https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f

#tensorflow #pytorch #GAN #tutorial #entrylevel #novice #wheretostart

Classification and Loss Evaluation - Softmax and Cross Entropy Loss

Nice notes on softmax cross entropy loss and how to implement it in numpy.

Link: https://deepnotes.io/softmax-crossentropy

#nn #entrylevel #wheretostart

Big scandal on popular YouTuber ML course

Siraj Raval, who raised his audience on devliering various YouTube videos, explaning #ML and #DL concepts as long with interviews with leading persons, launched his own course, but failed to provide much value.

His course was built on open and free tutorials, created by passionate enthusiasts, but he failed to attribute them properly and charged money for ununique content without any proper support for students.

He also oversold his course and tried to hide that from students, claiming to provide personal feedbacks, but failing to do so due to being too greedy.


Most of the best online courses and study programms are available online and for free, including those, we gathered here on our channel and attributed with hastags #wheretostart #entrylevel #MOOC #tutorial. Feel free to click these hashtags and browse for best available resources to start learning data science.

Link: https://www.theregister.co.uk/AMP/2019/09/27/youtube_ai_star

AMA today at 15:00 GMT (in 4 hours). In a couple of hours we will publish link to private chat for AMA session.

Stay tuned, prepare your questions. Please do not ask trivial and gramatically incorrect questions like 'where to start data science'.
First of all, use search, we have nice collections of resources for starting a DS career, tagged with #wheretostart #entrylevel #novice. Secondly, pay respect to our guest and ask questions more relevant to his area of experise.

Simple comic on how #ML works from #Google

Make sure you save the link (or this message) to show it to people without great technical background for it is one of the best and clear explanations there is.

Link: https://cloud.google.com/products/ai/ml-comic-1/

#wheretostart #entrylevel #novice #explainingtochildren

All the vector algebra you need for understanding neural networks

Article contains great explanations and description of matrix calculus you need to know and understand to really grok neural networks.

Link: https://explained.ai/matrix-calculus/index.html

#WhereToStart #entrylevel #novice #base #DL #nn

Free eBook from Stanford: Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares

Base material you need to understand how neural networks and other #ML algorithms work.

Link: https://web.stanford.edu/~boyd/vmls/

#Stanford #MOOC #WhereToStart #free #ebook #algebra #linalg #NN

Soon we will give a try to certain solution which will allow commenting on the posts in this channel.

Therefore, we will at first release the Ultimate Post on #wheretostart with Data Science, describing various entry points, books and courses. We want to provide extensive and thorough manual (just check out the name we chose), so we would be grateful if you can submit any resourses on getting starting with DS (any sphere) through our bot @opendatasciencebot (make sure you add your username, so we can reach you back)

You are most welcome to share:

Favourite books, youtube playlists, courses or even success stories.

​​Ultimate post on where to start learning DS

Most common request we received through the years was to share insights and advices on how to start career in data science and to recommend decent cources. Apparently, using hashtag #wheretostart wasn't enough so we were sharing some general advices.

So we assembled a through guide on how to start learning machine learning and created another #ultimatepost (in a form of a github repo, so it will be keep updated and anyone can submit worthy piece of advice to it).

We welcome you to share your stories and advices on how to start rolling into data science, as well as to spread the link to the repo to those your friends who might benefit from it.

Link: Ultimate post

#entrylevel #beginner #junior #MOOC #learndatascience #courses #mlcourse #opensource

​​Tutorial on Generative Adversarial Networks (GANs) with Keras and TensorFlow

Nice tutorial with enough theory to understand what you are doing and code to get it done.

Link: https://www.pyimagesearch.com/2020/11/16/gans-with-keras-and-tensorflow/

#Keras #TensorFlow #tutorial #wheretostart #GAN

👩‍🎓Online lectures on Special Topics in AI: Deep Learning

Fresh free and open playlist on special topics in #DL from University of Wisconsin-Madison. Topics covering reliable deep learning, generalization, learning with less supervision, lifelong learning, deep generative models and more.

Overview Lecture: https://www.youtube.com/watch?v=6LSErxKe634&list=PLKvO2FVLnI9SYLe1umkXsOfIWmEez04Ii
YouTube Playlist: https://www.youtube.com/playlist?list=PLKvO2FVLnI9SYLe1umkXsOfIWmEez04Ii
Syllabus: http://pages.cs.wisc.edu/~sharonli/courses/cs839_fall2020/schedule.html

#wheretostart #lectures #YouTube

Hands on ML notebook series

Updated our ultimate post with a series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.


Link: https://github.com/ageron/handson-ml

#wheretostart #opensource #jupyter

🎓Online Berkeley Deep Learning Lectures 2021

University of Berkeley released its fresh course lectures online for everyone to watch. Welcome Berkeley CS182/282 Deep Learnings - 2021!

YouTube: https://www.youtube.com/playlist?list=PLuv1FSpHurUevSXe_k0S7Onh6ruL-_NNh

#MOOC #wheretostart #Berkeley #dl

​​Simple book about #ML — Machine Learning Simplified

The main purpose of the book is to build an intuitive understanding of how algorithms work through basic examples. In order to understand the presented material, it is enough to know basic mathematics and linear algebra.

After reading this book, you will know the basics of supervised learning, understand complex mathematical models, understand the entire pipeline of a typical ML project, and also be able to share your knowledge with colleagues from related industries and with technical professionals.

And for those who find the theoretical part not enough - the book is supplemented with a repository on GitHub, which has Python implementation of all methods and algorithms described in chapters.

Book is absolutely free to read.

Link: themlsbook.com

#wheretostart #book