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Path-to-become-Data-Scientist

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Path to become data scientist is step by step collection of videos and reading material to become data scientist.

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# Path to become Data Scientist(Free Videos and reading resources)
<img src="https://github.com/thepradip/Data-Science-Video-resources/blob/master/AI6.jpg" width="900"/>

## Machine learning Introduction:
- [ML Intro](https://www.youtube.com/watch?v=PPLop4L2eGk&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN&index=1)
- [what is ML](https://www.youtube.com/watch?v=HcqpanDadyQ&list=PLMUdwhLW7A0icUPn6LT5Zj0rDqZ2OOP8Y&index=7)
- [7 steps in ML](https://www.youtube.com/watch?v=nKW8Ndu7Mjw&list=PLMUdwhLW7A0icUPn6LT5Zj0rDqZ2OOP8Y&index=3)
- [How to Enter a Kaggle Competition (using Kernels) | Kaggle](https://www.youtube.com/watch?v=GJBOMWpLpTQ)

## Algebra:
- [Linear Albegra| chapter 3](https://www.youtube.com/watch?v=Dft1cqjwlXE&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN&index=12)
- [ 3 blue and 1 brown ](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
## Probability and statistics: 
- [Probability and statistics in data science using python](https://www.edx.org/course/probability-and-statistics-in-data-science-using-python-2) 
- [Khan Academy for probability](https://www.youtube.com/playlist?list=PLC58778F28211FA19) 
- [Khan Academy for statistics](https://www.youtube.com/watch?v=uhxtUt_-GyM&list=PL1328115D3D8A2566) 
- [coursera| Basic statistics](https://www.coursera.org/learn/basic-statistics) 
## Python for data science
- [Datacamp|python and plot](https://www.youtube.com/watch?v=-Rf4fZDQ0yw&list=PLjgj6kdf_snaw8QnlhK5f3DzFDFKDU5f4)

## Machine Learning 
- [Machine Learning by Andrew Ng| concept](https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) 
- [statisticsfun|linear regression|concept](https://www.youtube.com/playlist?list=PLF596A4043DBEAE9C) 
- [Applied Machine Learning](https://www.youtube.com/watch?v=Qd68h4UGlNY&list=PL_pVmAaAnxIQGzQS2oI3OWEPT-dpmwTfA)
- [Machine Learning fun easy| concept](https://www.youtube.com/watch?v=RPvLgzEybJU&list=PL_Nji0JOuXg2udXfS6nhK3CkIYLDtHNLp)
- [Practical ML|sentdex|python](https://www.youtube.com/watch?v=OGxgnH8y2NM&list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v)
- [ML| Udacity](https://classroom.udacity.com/courses/ud120)
- [Machine Learning Recipes with Josh Gordon](https://www.youtube.com/watch?v=cKxRvEZd3Mw&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)
- [Google |mastering machine learning| Crash course| tensorflow](https://developers.google.com/machine-learning/crash-course/ml-intro)
## Evaluation Metrics
- [ML error metrics](https://www.youtube.com/watch?v=aDW44NPhNw0&list=PLs8w1Cdi-zvY9ICoYqu1XV0YoTQgShXw2)
- [datcamp|metrics](https://www.youtube.com/watch?v=85dtiMz9tSo&t=950s)
- [AUC and ROC curve](https://www.youtube.com/watch?v=OAl6eAyP-yo)
- [What's the difference between accuracy and precision?](https://www.youtube.com/watch?v=hRAFPdDppzs)
 ## Deep Learning 
 - [Neural network|3Blue 1Brown](https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
 - [Neural network | hugo](https://www.youtube.com/watch?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH&v=SGZ6BttHMPw)
 - [Keras|edureka](https://www.youtube.com/watch?v=XNKeayZW4dY)
 - [Keras|udemy](https://www.youtube.com/watch?v=7x2YZhEj9Dw)
 - [Getting Started with Keras (AI Adventures)](https://www.youtube.com/watch?v=J6Ok8p463C4)
 - [Ten steps to keras|](https://www.youtube.com/watch?v=FrkYu2zVUyM)
- [Deeplearning.ai](https://www.youtube.com/channel/UCcIXc5mJsHVYTZR1maL5l9w) 
Course 1,2,3,4,5 of deep learning specialization 
- [Convolutional Neural Networks](https://www.youtube.com/watch?v=LxfUGhug-iQ&list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC&index=8&t=0s) 
- [RNN](https://www.youtube.com/watch?v=yCC09vCHzF8&t=58s) 
- [DL|udemy](https://www.youtube.com/watch?v=BR9h47Jtqyw)
- [DL|RNN](https://www.youtube.com/watch?v=UNmqTiOnRfg)
- [7 step to mastering deep learning](https://www.kdnuggets.com/2017/10/seven-steps-deep-learning-keras.html)
 - [Python for data science](https://www.edx.org/professional-certificate/python-data-science) 
 - [Transfer Learning](https://www.youtube.com/watch?v=mPFq5KMxKVw&t=1s) 
 - [Lecture 10: Recurrent Neural Networks, Image Captioning, LSTM](https://www.youtube.com/watch?v=yCC09vCHzF8&list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC&index=11&t=0s) 
- [MIT deep Learning](https://www.youtube.com/playlist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf)

## Natural Language Processing:
 - [Deep learning for NLP by Richard](https://www.youtube.com/watch?v=oGk1v1jQITw) 
 - [Deep Learning for NLP](https://www.youtube.com/watch?v=kZteabVD8sU&list=PLcGUo322oqu9n4i0X3cRJgKyVy7OkDdoi&index=1)
 - [Intro to NLP with spaCy (1): Detecting programming languages | Episode 1: Data exploration](https://www.youtube.com/watch?time_continue=815&v=WnGPv6HnBok)
 - [Building new NLP solutions with spaCy and Prodigy - Matthew Honnibal](https://www.youtube.com/watch?time_continue=234&v=jpWqz85F_4Y)
- [ Advanced NLP with Spacy ](https://course.spacy.io/)
- [A Code-First Introduction to Natural Language Processing](https://www.fast.ai/2019/07/08/fastai-nlp/)
## Recommedation engine
- [coursera](https://www.coursera.org/specializations/recommender-systems)
- [Andre ng| chapter 16](https://www.youtube.com/watch?v=giIXNoiqO_U&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN&index=96)
- [How does Netflix recommend movies? Matrix Factorization](https://www.youtube.com/watch?v=ZspR5PZemcs)
- [Movie Recommendation System with Collaborative Filtering](https://www.youtube.com/watch?v=3ecNC-So0r4)
## Computer Vision
- [opencv tutorials](https://docs.opencv.org/master/d9/df8/tutorial_root.html)
- [image-segmentation-tips-and-tricks-from-kaggle-competitions](https://neptune.ai/blog/image-segmentation-tips-and-tricks-from-kaggle-competitions)
- [pyimagesearch| best](https://www.pyimagesearch.com/)
## Big data
- [big data analysis using spark](https://www.edx.org/course/big-data-analytics-using-spark-uc-san-diegox-dse230x) 
## Reinforcement Learning
- [Deep learning| reinforcement Learning](http://videolectures.net/deeplearning2017_montreal/)
- [Simplified Reinforcement learning](https://www.youtube.com/watch?v=e3Jy2vShroE)
- [TensorFlow and deep reinforcement learning, without a PhD (Google I/O '18)](https://www.youtube.com/watch?v=t1A3NTttvBA&t=772s)
- [Reinforcement Learning Course](https://www.youtube.com/watch?v=ELE2_Mftqoc&t=1845s)
- [code camp](https://www.youtube.com/watch?v=ELE2_Mftqoc&t=3647s)
- [Edureka](https://www.youtube.com/watch?v=LzaWrmKL1Z4)
## Artificial Intelligent
- [UC Berkeley](http://ai.berkeley.edu/lecture_videos.html)
- [UC Berkeley|materials](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
## Data Scientist Interiew
- [DataCamp |DataChats](https://www.youtube.com/watch?v=YVMlyOh_eyk&list=PLjgj6kdf_snYAqMEWOlql_DVMPbh8dtP1)
## Awesome talk On Machine Learning
- [Andreas Mueller|MIT](https://www.youtube.com/playlist?list=PL_pVmAaAnxITmBjkH1d0_dANEH7ro2_tH)
## Data Science Courses
 - [Learn Deep learning fundamental with Keras from IBM](https://www.edx.org/course/deep-learning-fundamentals-with-keras)
 - [Python for data science](https://www.edx.org/professional-certificate/python-data-science) 
- [Master the skills needed to solve complex data challenges](https://www.edx.org/micromasters/mitx-statistics-and-data-science)
- [Edx| Learn Data Science by doing data science](https://www.edx.org/micromasters/uc-san-diegox-data-science)
# Data science competition
- [kaggle](https://www.kaggle.com/)
- [drivendata](https://www.drivendata.org/competitions/)
- [crowdanalytix](https://www.crowdanalytix.com/listContests)
- [codalab](https://competitions.codalab.org/)
- [tianchi](https://tianchi.aliyun.com/competition/gameList/activeList)
## Data Scientist Interiew
- [DataCamp |DataChats](https://www.youtube.com/watch?v=YVMlyOh_eyk&list=PLjgj6kdf_snYAqMEWOlql_DVMPbh8dtP1)
- [machine-learning-algorithms-interview](https://workera.ai/resources/machine-learning-algorithms-interview/)
## Data Science Courses
- [Data science course](https://www.edx.org/micromasters/uc-san-diegox-data-science) 
- [Master the skills needed to solve complex data challenges](https://www.edx.org/micromasters/mitx-statistics-and-data-science) 
- [Edx| Learn Data Science by doing data science](https://www.edx.org/micromasters/uc-san-diegox-data-science) 
# Data Science Blogs Resources
### Blog courses

- [Machine Learning Mastery](https://machinelearningmastery.com/start-here/)
- [Machine Learning is fun series](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471)
- [Best kaggle kernel tutorials](https://www.kaggle.com/kanncaa1/kernels)
- [Data school](https://www.dataschool.io/)
 - [Getting started with kaggle](https://www.kaggle.com/getting-started/44915)
- [Machine Learning @ Berkeley|crash](https://medium.com/@ml.at.berkeley/machine-learning-crash-course-part-1-9377322b3042)
- [Kaggle ](https://www.kaggle.com/kanncaa1/machine-learning-tutorial-for-beginners)
- [ML from scratch](https://github.com/eriklindernoren/ML-From-Scratch)
- [Mlcourse.ai](https://mlcourse.ai/)
- [Andrew Trask](http://iamtrask.github.io/)
- [Andrej karpathy](http://karpathy.github.io/)
# Visualization of model
- [Tensorboard](https://www.tensorflow.org/tensorboard)
- [Hand-on Tensorboard](https://www.youtube.com/watch?v=eBbEDRsCmv4)
- [Weight and Biases](https://www.wandb.com/tutorials)
## Math for Data Sciecne
- [math for ML](http://users.umiacs.umd.edu/~hal/courses/2013S_ML/math4ml.pdf)
- [Linear Algebra guide](https://betterexplained.com/articles/linear-algebra-guide/)
- [Linear Algebra cheatsheet](https://towardsdatascience.com/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c)
- [A brief introduction to probability and statistics](https://betterexplained.com/articles/a-brief-introduction-to-probability-statistics/)
- [review probability](http://cs229.stanford.edu/section/cs229-prob.pdf)
- [Probability for ML](https://see.stanford.edu/materials/aimlcs229/cs229-prob.pdf)
 - [probability ppt](http://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/tutorial1.pdf)
### Linear Regression
- [ML cheatsheet| linear](https://ml-cheatsheet.readthedocs.io/en/latest/linear_regression.html)
- [Simple Linear Regression Tutorial for Machine Learning](https://machinelearningmastery.com/simple-linear-regression-tutorial-for-machine-learning/)
- [regression](https://www.datasciencecentral.com/profiles/blogs/26-great-articles-and-tutorials-about-regression-analysis)
## Logistic Regression
- [ML cheatsheet| logistic](https://ml-cheatsheet.readthedocs.io/en/latest/logistic_regression.html)
- [Machine Learning Mastery| logistic](https://machinelearningmastery.com/logistic-regression-tutorial-for-machine-learning/)
## Naive Bayes
- [Practical explanation naive bayes classifier](https://monkeylearn.com/blog/practical-explanation-naive-bayes-classifier/)
- [understanding bayes theorem with ratios](https://betterexplained.com/articles/understanding-bayes-theorem-with-ratios/)
## K nearest Neighbour
- [implementing your own knn](https://www.kdnuggets.com/2016/01/implementing-your-own-knn-using-python.html)
## Support Vector Machines
 - [introduction to support vector machines svm](https://monkeylearn.com/blog/introduction-to-support-vector-machines-svm/)
 - [standford notes](http://cs229.stanford.edu/notes/cs229-notes3.pdf)
 - [standford linear classification](http://cs231n.github.io/linear-classify/)
 ## Decision trees
 - [Datacamp| decision trees](https://www.datacamp.com/community/tutorials/decision-tree-classification-python)
 - [How To Implement The Decision Tree Algorithm From Scratch In Python](https://machinelearningmastery.com/implement-decision-tree-algorithm-scratch-python/)
 - [Decision tree |medium](https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052)

- [Ensemble ](https://www.analyticsvidhya.com/blog/2015/08/introduction-ensemble-learning/)
 - [Ensemble_learning algo](http://www.scholarpedia.org/article/Ensemble_learning)
 # Time series
 - [great articles on Time series](https://www.datasciencecentral.com/profiles/blogs/21-great-articles-and-tutorials-on-time-series)

## Clustering
 - [Kmean visuallization](https://www.naftaliharris.com/blog/visualizing-k-means-clustering/)
 - [DB scan visualization](https://www.naftaliharris.com/blog/visualizing-dbscan-clustering/)
 - [clustering](https://www.datasciencecentral.com/profiles/blogs/14-great-articles-and-tutorials-on-clustering)
 - [14 Clustering tutorials](https://www.datasciencecentral.com/profiles/blogs/14-great-articles-and-tutorials-on-clustering)
 # Deep Learning
 - [15 Deep Learning tutorials](https://www.datasciencecentral.com/profiles/blogs/15-deep-learning-tutorials)
- [Initialization weight in NN](https://www.deeplearning.ai/ai-notes/initialization/index.html?utm_source=deeplearningai&utm_medium=institutions&utm_campaign=TwitterApril2019)
- [Parameter optimization in neural networks](https://www.deeplearning.ai/ai-notes/optimization/)
 ## Tensorflow
 - [tensorflow|documentation](https://www.tensorflow.org/tutorials/)
 - [TensorFlow: A primer](https://blog.metaflow.fr/tensorflow-a-primer-4b3fa0978be3)
- [RNNs in Tensorflow](http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/)
- [Implementing a CNN for Text Classification in TensorFlow](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/)
- [how to run text summarization with tensorflow](http://pavel.surmenok.com/2016/10/15/how-to-run-text-summarization-with-tensorflow/)
# Transfer Learning
 - [transfer](https://github.com/thepradip/Transfer-Learning/settings)
## Keras
- [keras documentaion](https://keras.io/)
- [keras|kdnuggets](https://www.kdnuggets.com/2017/10/seven-steps-deep-learning-keras.html)

## NLP
- [Coursera| NLP](https://www.coursera.org/learn/language-processing/)
- [Standford NLP course](https://web.stanford.edu/class/cs124/)
- [Stanford deep NLP](http://cs224d.stanford.edu/syllabus.html)
- [Data Schools|NLP](https://github.com/yandexdataschool/nlp_course/tree/master)
- [OXFORD |DLNLP](https://www.cs.ox.ac.uk/teaching/courses/2016-2017/dl/timetable.html)
- [Washington](https://courses.cs.washington.edu/courses/cse517/17wi/)
- [The Definitive Guide to Natural Language Processing](https://monkeylearn.com/blog/definitive-guide-natural-language-processing/)
- [Approaching-almost-any-nlp-problem-on-kaggle](https://www.kaggle.com/abhishek/approaching-almost-any-nlp-problem-on-kaggle)
- [NLP is fun](https://medium.com/@ageitgey/natural-language-processing-is-fun-9a0bff37854e)
- [Deep	Learning	for	NLP	](https://nlp.stanford.edu/courses/NAACL2013/NAACL2013-Socher-Manning-DeepLearning.pdf)
- [ Advanced NLP with Spacy ](https://course.spacy.io/)
- [A Code-First Introduction to Natural Language Processing](https://www.fast.ai/2019/07/08/fastai-nlp/)
- [practitioner guide to NLP](https://towardsdatascience.com/a-practitioners-guide-to-natural-language-processing-part-i-processing-understanding-text-9f4abfd13e72)
### Chatbot
- [Dialogflow](https://www.youtube.com/watch?v=ADD-rvsS5z4&t=864s)
- [webbook](https://www.youtube.com/watch?v=gWNUg_v25dw&t=485s)
## Big Data
- [big data 11](https://www.datasciencecentral.com/profiles/blogs/11-great-hadoop-spark-and-map-reduce-articles)
## GAN
- [All about GANs](https://github.com/thepradip/GANs)
## Reinforcement Learning
- [Learning Reinforcement Learning](http://www.wildml.com/2016/10/learning-reinforcement-learning/)
- [Intro to Reinforcement](https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/)
- [RL tutorials](https://web.mst.edu/~gosavia/tutorial.pdf)
- [Deep Reinforcement Learning: Pong from Pixels](http://karpathy.github.io/2016/05/31/rl/)
## Machine Learning best practice
- [ML rules](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)
- [which ML algo to use ](https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/)
- [ Data-Analysis-and-Machine-Learning-Projects](https://nbviewer.jupyter.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb)
# Open cv
- [Pyimagesearch](https://www.pyimagesearch.com/start-here/)
## AI Trend
- [AI trend](https://www.aitrends.com/)
## Data Science Projects
- [kaggle project with kernel ](https://www.kaggle.com/m2skills/datasets-and-tutorial-kernels-for-beginners)
## Datasets
- [50 NLP Datasets](https://blog.cambridgespark.com/50-free-machine-learning-datasets-natural-language-processing-d88fb9c5c8da)
- [Data science Datasets](https://www.springboard.com/blog/free-public-data-sets-data-science-project/)
- [Machine Mastery](https://machinelearningmastery.com/datasets-natural-language-processing/)
- [Cambridgespark](https://blog.cambridgespark.com/50-free-machine-learning-datasets-natural-language-processing-d88fb9c5c8da)
- [Public Datasets ](https://www.springboard.com/blog/free-public-data-sets-data-science-project/)
- [NLP dataset](https://github.com/niderhoff/nlp-datasets)
## Books
- ISLR
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow]s by Aurélien Géron
- Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 by Sebastian Raschka

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