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For 424 | the avoidance of doubt, this paragraph does not form part of the 425 | public licenses. 426 | 427 | Creative Commons may be contacted at creativecommons.org. -------------------------------------------------------------------------------- /README-pt-BR.md: -------------------------------------------------------------------------------- 1 | # Top-down learning path: Machine Learning for Software Engineers 2 | Inspired by [Google Interview University](https://github.com/jwasham/google-interview-university). 3 | 4 | _Se você gostou deste projeto, por favor me dê uma estrela._ ★ 5 | 6 | ## O que é? 7 | 8 | Este é meu plano de estudo para ir de desenvolvedor mobile (autodidata, sem diploma) para Engenheiro de Machine Learning. 9 | Meu principal objetivo era encontrar uma abordagem para estudar Machine Lerning, que é principalmente hands-on (aprender fazendo) e abstrair a maioria da matemática para o iniciante. Esta abordagem não é convencional porque ela é uma abordagem top-down e resultados-primeiro projetada para engenheiros de software. 10 | 11 | Por favor, sinta-se livre para fazer qualquer contribuição que você achar que pode o tornar melhor. 12 | 13 | --- 14 | 15 | ## Tabela de conteúdo 16 | 17 | - [O que é?](#o-que-é) 18 | - [Por que usar?](#por-que-usar) 19 | - [Como usar](#como-usar) 20 | - [Siga-me](#siga-me) 21 | - [Não sinta que não é inteligente o bastante](#não-sinta-que-não-é-inteligente-o-bastante) 22 | - [Sobre Video Resources](#sobre-video-resources) 23 | - [Conhecimento prévio](#conhecimento-prévio) 24 | - [O Plano diário](#o-plano-diário) 25 | - [Motivação](#motivação) 26 | - [Visão geral do Machine Learning](#visão-geral-do-machine-learning) 27 | - [Maestria do Machine Learning](#maestria-do-machine-learning) 28 | - [Machine Learning é divertido](#machine-learning-é-divertido) 29 | - [Machine learning: um guia profundo, não técnico](#machine-learning-um-guia-profundo-não-técnico) 30 | - [Relatos e experiências](#relatos-e-experiências) 31 | - [Livros para iniciantes](#livros-para-iniciantes) 32 | - [Livros para prática](#livros-para-prática) 33 | - [Competições de conhecimento Kaggle](#competições-de-conhecimento-kaggle) 34 | - [Video Series](#video-series) 35 | - [MOOC](#mooc) 36 | - [Pesquisas](pesquisas) 37 | - [Torna-se um contribuidor Open Source](#torne-se-um-contribuidor-open-sourse) 38 | - [Communidades](#comunidades) 39 | - [My admired companies](#my-admired-companies) 40 | 41 | --- 42 | 43 | ## Por que usar? 44 | 45 | Eu estou seguindo este plano para me preparar para meu próximo futuro emprego: Engenheiro de Machine Learning. Venho construindo aplicativos nativos móveis (iOS/Android/Blackberry) desde 2011. Eu tenho um diploma de engenharia de Software, não um diploma de Ciência da Computação. Tenho um pouco de conhecimentos básicos sobre: cálculo, Álgebra Linear, matemática discreta, probabilidade e estatística na Universidade. 46 | 47 | Pense sobre meu interesse em Machine Learning: 48 | 49 | - [Posso aprender e arrumar um emprego em Machine Learning sem estudar mestrado e Phd em Ciência da Computação?](https://www.quora.com/Can-I-learn-and-get-a-job-in-Machine-Learning-without-studying-CS-Master-and-PhD) 50 | - Você pode, mas isto é muito mais difícil do que quando eu entrei no campo. 51 | 52 | - [Como eu consigo um emprego em Machine Learning como um programador de software que auto-estudou Machine Learning, mas nunca teve a chance de usar isso no trabalho?] (https://www.quora.com/How-do-I-get-a-job-in-Machine-Learning-as-a-software-programmer-who-self-studies-Machine-Learning-but-never-has-a-chance-to-use-it-at-work) 53 | - Estou contratando especialistas de Machine Learning para minha equipe e seu MOOC não vai conseguir para você o trabalho (há melhores notícias abaixo). Na verdade, muitas pessoas com um mestrado em Machine Learning não terão o emprego porque eles (e a maioria que tomaram MOOC) não têm uma compreensão profunda que vai me ajudar a resolver os meus problemas. 54 | 55 | - [Que habilidades são necessárias para trabalhos de Machine Learning?](http://programmers.stackexchange.com/questions/79476/what-skills-are-needed-for-machine-learning-jobs) 56 | - Primeiramente, você precisa ter um decente background de Ciência da Computação/Matemática. ML é um tópico avançado, então a maioria dos livros didáticos assumem que você tem esse background. Por segundo, Machine Learning é um tema muito geral com várias sub especialidades que exigem habilidades únicas. Você pode querer procurar o currículo de um programa de MS em Machine Learning para ver o curso, o currículo e livro didático. 57 | - Estatística, propabilidade, computação distribuída e estatística. 58 | 59 | Eu me encontro em tempos difíceis. 60 | 61 | AFAIK, [Há dois lados para Machine Learning](http://machinelearningmastery.com/programmers-can-get-into-machine-learning/): 62 | - Prática de Machine Learning: Isto é sobre bancos de dados de consultas, limpeza de dados, escrevendo scripts para transformar dados e colagem de algoritmo e bibliotecas juntos e escrever código personalizado para espremer respostas confiáveis de dados para satisfazer as perguntas difíceis e mal definidas. É a porcaria da realidade. 63 | - Teoria de Machine Learning: Isto é sobre matemática e abstração e cenários idealizados e limites e beleza e informando o que é possível. É muito mais puro e mais limpo e removido da confusão da realidade. 64 | 65 | Eu acho que a melhor maneira para metodologia centrada na prática é algo como ['prática - aprendizagem - prática'](http://machinelearningmastery.com/machine-learning-for-programmers/#comment-358985), que significa onde estudantes primeiro vêm com alguns projetos existentes com problemas e soluções (prática) para se familiarizar com os métodos tradicionais na área e talvez também com sua metodologia.Depois de praticar com algumas experiências elementares, podem ir para os livros e estudar a teoria subjacente, que serve para guiar a sua futura prática avançada e reforçará a sua caixa de ferramentas de solução de problemas práticos. Estudar a teoria também melhora ainda mais sua compreensão sobre as experiências elementares e irá ajudá-los a adquirir experiências avançadas mais rapidamente. 66 | 67 | É um plano longo. Isso vai demorar anos para mim. Se você já está familiarizado com bastante disso já, você levará muito menos tempo. 68 | 69 | ## Como usar 70 | Tudo abaixo é uma estrutura de tópicos, e você deve enfrentar os itens em ordem de cima para baixo. 71 | 72 | Eu estou usando o especial Markdown do Github, incluindo a lista de tarefas para verificar o progresso. 73 | 74 | - [x] Crie um novo branch, então você poderá verificar itens como esse, apenas coloque um x entre os colchetes. 75 | 76 | [More about Github-flavored markdown](https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown) 77 | 78 | ## Siga-me 79 | Eu sou um engenheiro de Software vietnamita que é realmente apaixonado e quer trabalhar nos EUA. 80 | 81 | Quanto eu trabalhei durante este plano? Aproximadamente 4 horas/noite após um dia longo no trabalho. 82 | 83 | Eu estou na jornada. 84 | 85 | | ![Nam Vu - Top-down learning path: machine learning for software engineers](http://sv1.upsieutoc.com/2016/10/08/331f241c8da44d0c43e9324d55440db6.md.jpg)| 86 | |:---:| 87 | | USA as heck | 88 | 89 | ## Não sinta que não é inteligente o bastante 90 | Fico desencorajado por livros e cursos que me dizem que o quanto antes eu puder, cálculo multivariável, inferencial e álgebra linear são pré-requisitos. Ainda não sei como começar... 91 | 92 | - [What if I'm Not Good at Mathematics](http://machinelearningmastery.com/what-if-im-not-good-at-mathematics/) 93 | - [5 Techniques To Understand Machine Learning Algorithms Without the Background in Mathematics](http://machinelearningmastery.com/techniques-to-understand-machine-learning-algorithms-without-the-background-in-mathematics/) 94 | - [How do I learn machine learning?](https://www.quora.com/Machine-Learning/How-do-I-learn-machine-learning-1) 95 | 96 | ## Sobre Video Resources 97 | 98 | Alguns vídeos estão disponíveis apenas registrando-se em uma classe Coursera ou EdX. É de graça, mas às vezes as classes já não estão em sessão, então você tem que esperar uns meses, se não, não terá acesso. 99 | Eu vou estar adicionando mais vídeos de fontes públicas e substituindo os vídeos do curso on-line ao longo do tempo. Eu gosto de usar palestras de universidade. 100 | 101 | ## Conhecimento prévio 102 | 103 | Esta seção curta foram pré-requisitos/informações interessantes que eu queria aprender antes de começar o plano diário. 104 | 105 | - [ ] [What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data?](https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1) 106 | - [ ] [Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn) 107 | - [ ] [Don't Break The Chain](http://lifehacker.com/281626/jerry-seinfelds-productivity-secret) 108 | - [ ] [How to learn on your own](https://metacademy.org/roadmaps/rgrosse/learn_on_your_own) 109 | 110 | ## O Plano Diário 111 | 112 | Cada assunto não requer um dia inteiro para ser capaz de compreendê-lo totalmente, e você pode fazer vários desses em um dia. 113 | 114 | Cada dia eu pego um assunto da lista abaixo, leia de capa a capa, tome nota, faça os exercícios e escreva uma implementação em Python ou R. 115 | 116 | # Motivação 117 | - [ ] [Dream](https://www.youtube.com/watch?v=g-jwWYX7Jlo) 118 | 119 | ## Visão geral do Machine learning 120 | - [ ] [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) 121 | - [ ] [A Gentle Guide to Machine Learning](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/) 122 | - [ ] [Machine Learning basics for a newbie](https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/) 123 | 124 | ## Maestria do Machine learning 125 | - [ ] [The Machine Learning Mastery Method](http://machinelearningmastery.com/machine-learning-mastery-method/) 126 | - [ ] [Machine Learning for Programmers](http://machinelearningmastery.com/machine-learning-for-programmers/) 127 | - [ ] [Applied Machine Learning with Machine Learning Mastery](http://machinelearningmastery.com/start-here/) 128 | - [ ] [Python Machine Learning Mini-Course](http://machinelearningmastery.com/python-machine-learning-mini-course/) 129 | - [ ] [Machine Learning Algorithms Mini-Course](http://machinelearningmastery.com/machine-learning-algorithms-mini-course/) 130 | 131 | ## Machine learning é divertido 132 | - [ ] [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.37ue6caww) 133 | - [ ] [Part 2: Using Machine Learning to generate Super Mario Maker levels](https://medium.com/@ageitgey/machine-learning-is-fun-part-2-a26a10b68df3#.kh7qgvp1b) 134 | - [ ] [Part 3: Deep Learning and Convolutional Neural Networks](https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.44rhxy637) 135 | - [ ] [Part 4: Modern Face Recognition with Deep Learning](https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78#.3rwmq0ddc) 136 | - [ ] [Part 5: Language Translation with Deep Learning and the Magic of Sequences](https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa#.wyfthap4c) 137 | 138 | ## Machine learning: um guia profundo, não técnico 139 | - [ ] [Overview, goals, learning types, and algorithms](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide/) 140 | - [ ] [Data selection, preparation, and modeling](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-2/) 141 | - [ ] [Model evaluation, validation, complexity, and improvement](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-3/) 142 | - [ ] [Model performance and error analysis](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-4/) 143 | - [ ] [Unsupervised learning, related fields, and machine learning in practice](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-5/) 144 | 145 | ## Relatos e experiências 146 | - [ ] [Machine Learning in a Week](https://medium.com/learning-new-stuff/machine-learning-in-a-week-a0da25d59850#.tk6ft2kcg) 147 | - [ ] [Machine Learning in a Year](https://medium.com/learning-new-stuff/machine-learning-in-a-year-cdb0b0ebd29c#.hhcb9fxk1) 148 | - [ ] [Learning Path : Your mentor to become a machine learning expert](https://www.analyticsvidhya.com/learning-path-learn-machine-learning/) 149 | - [ ] [You Too Can Become a Machine Learning Rock Star! No PhD](https://backchannel.com/you-too-can-become-a-machine-learning-rock-star-no-phd-necessary-107a1624d96b#.g9p16ldp7) 150 | - [ ] How to become a Data Scientist in 6 months: A hacker’s approach to career planning 151 | - [Video](https://www.youtube.com/watch?v=rIofV14c0tc) 152 | - [Slide](http://www.slideshare.net/TetianaIvanova2/how-to-become-a-data-scientist-in-6-months) 153 | - [ ] [5 Skills You Need to Become a Machine Learning Engineer](http://blog.udacity.com/2016/04/5-skills-you-need-to-become-a-machine-learning-engineer.html) 154 | - [ ] [Are you a self-taught machine learning engineer? If yes, how did you do it & how long did it take you?](https://www.quora.com/Are-you-a-self-taught-machine-learning-engineer-If-yes-how-did-you-do-it-how-long-did-it-take-you) 155 | - [ ] [How can one become a good machine learning engineer?](https://www.quora.com/How-can-one-become-a-good-machine-learning-engineer) 156 | 157 | ## Livros para iniciantes 158 | - [ ] [Data Smart: Using Data Science to Transform Information into Insight 1st Edition](https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X) 159 | - [ ] [Data Science for Business: What you need to know about data mining and data­ analytic-thinking](https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323/) 160 | - [ ] [Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die](https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853) 161 | 162 | ## Livros para prática 163 | - [ ] [Machine Learning for Hackers](https://www.amazon.com/Machine-Learning-Hackers-Drew-Conway/dp/1449303714) 164 | - [GitHub repository](https://github.com/johnmyleswhite/ML_for_Hackers) 165 | - [ ] [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka-ebook/dp/B00YSILNL0) 166 | - [GitHub repository](https://github.com/rasbt/python-machine-learning-book) 167 | - [ ] [Programming Collective Intelligence: Building Smart Web 2.0 Applications](https://www.amazon.com/Programming-Collective-Intelligence-Building-Applications-ebook/dp/B00F8QDZWG) 168 | - [ ] [Machine Learning: An Algorithmic Perspective, Second Edition](https://www.amazon.com/Machine-Learning-Algorithmic-Perspective-Recognition/dp/1466583282) 169 | - [GitHub repository](https://github.com/alexsosn/MarslandMLAlgo) 170 | - [Resource repository](http://seat.massey.ac.nz/personal/s.r.marsland/MLbook.html) 171 | - [ ] [Introduction to Machine Learning with Python: A Guide for Data Scientists](http://shop.oreilly.com/product/0636920030515.do) 172 | - [GitHub repository](https://github.com/amueller/introduction_to_ml_with_python) 173 | - [ ] [Data Mining: Practical Machine Learning Tools and Techniques, Third Edition](https://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0123748569) 174 | - Teaching material 175 | - [Slides for Chapters 1-5 (zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch1-5.zip) 176 | - [Slides for Chapters 6-8 (zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch6-8.zip) 177 | - [ ] [Machine Learning in Action](https://www.amazon.com/Machine-Learning-Action-Peter-Harrington/dp/1617290181/) 178 | - [GitHub repository](https://github.com/pbharrin/machinelearninginaction) 179 | - [ ] [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) 180 | 181 | ## Competições de conhecimento Kaggle 182 | - [ ] [Kaggle Competitions: How and where to begin?](https://www.analyticsvidhya.com/blog/2015/06/start-journey-kaggle/) 183 | - [ ] [How a Beginner Used Small Projects To Get Started in Machine Learning and Compete on Kaggle](http://machinelearningmastery.com/how-a-beginner-used-small-projects-to-get-started-in-machine-learning-and-compete-on-kaggle) 184 | - [ ] [Master Kaggle By Competing Consistently](http://machinelearningmastery.com/master-kaggle-by-competing-consistently/) 185 | 186 | 187 | ## Video Series 188 | - [ ] [Machine Learning for Hackers](https://www.youtube.com/playlist?list=PL2-dafEMk2A4ut2pyv0fSIXqOzXtBGkLj) 189 | - [ ] [Fresh Machine Learning](https://www.youtube.com/playlist?list=PL2-dafEMk2A6Kc7pV6gHH-apBFxwFjKeY) 190 | - [ ] [Machine Learning Recipes with Josh Gordon](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal) 191 | - [ ] [Everything You Need to know about Machine Learning in 30 Minutes or Less](https://vimeo.com/43547079) 192 | 193 | ## MOOC 194 | - [ ] [Udacity's Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120) 195 | - [Udacity Intro to Machine Learning Review](http://hamelg.blogspot.com/2014/12/udacity-intro-to-machine-learning-review.html) 196 | - [ ] [Udacity's Supervised, Unsupervised & Reinforcement](https://www.udacity.com/course/machine-learning--ud262) 197 | - [ ] [Machine Learning Foundations: A Case Study Approach](https://www.coursera.org/learn/ml-foundations) 198 | - [ ] [Coursera's Machine Learning](https://www.coursera.org/learn/machine-learning) 199 | - [Video only](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW) 200 | - [Coursera Machine Learning review](https://rayli.net/blog/data/coursera-machine-learning-review/) 201 | - [Coursera: Machine Learning Roadmap](https://metacademy.org/roadmaps/cjrd/coursera_ml_supplement) 202 | 203 | ## Pesquisas 204 | - [ ] [Machine Learning for Developers](https://xyclade.github.io/MachineLearning/) 205 | - [ ] [Machine Learning Advice for Developers](https://dev.to/thealexlavin/machine-learning-advice-for-developers) 206 | - [ ] [Machine Learning For Complete Beginners](http://pythonforengineers.com/machine-learning-for-complete-beginners/) 207 | - [ ] [Machine Learning Self-study Resources](https://ragle.sanukcode.net/articles/machine-learning-self-study-resources/) 208 | - [ ] [Level-Up Your Machine Learning](https://metacademy.org/roadmaps/cjrd/level-up-your-ml) 209 | - [ ] [Enough Machine Learning to Make Hacker News Readable Again](https://speakerdeck.com/pycon2014/enough-machine-learning-to-make-hacker-news-readable-again-by-ned-jackson-lovely) 210 | 211 | ## Torne-se um contribuidor Open Sourse 212 | - [ ] [tensorflow/magenta: Magenta: Music and Art Generation with Machine Intelligence](https://github.com/tensorflow/magenta) 213 | - [ ] [tensorflow/tensorflow: Computation using data flow graphs for scalable machine learning](https://github.com/tensorflow/tensorflow) 214 | - [ ] [cmusatyalab/openface: Face recognition with deep neural networks.](https://github.com/cmusatyalab/openface) 215 | - [ ] [tensorflow/models/syntaxnet: Neural Models of Syntax.](https://github.com/tensorflow/models/tree/master/syntaxnet) 216 | 217 | ## Comunidades 218 | - ### Quora 219 | - [Machine Learning](https://www.quora.com/topic/Machine-Learning) 220 | - [Statistics](https://www.quora.com/topic/Statistics-academic-discipline) 221 | - [Data Mining](https://www.quora.com/topic/Data-Mining) 222 | 223 | - ### Reddit 224 | - [Machine Learning](https://www.reddit.com/r/machinelearning) 225 | 226 | - ### [Data Tau](http://www.datatau.com/) 227 | 228 | ## My admired companies 229 | - [ ] [ELSA - Your virtual pronunciation coach](https://www.elsanow.io/home) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Top-down learning path: Machine Learning for Software Engineers 2 | 3 |

4 | 5 | Top-down learning path: Machine Learning for Software Engineers 6 | 7 | 8 | GitHub stars 9 | 10 | 11 | GitHub forks 12 | 13 |

14 | 15 | Inspired by [Google Interview University](https://github.com/jwasham/google-interview-university). 16 | 17 | Translations: [Brazilian Portuguese](https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README-pt-BR.md) 18 | 19 | ## What is it? 20 | 21 | This is my multi-month study plan for going from mobile developer (self-taught, no CS degree) to machine learning engineer. 22 | 23 | My main goal was to find an approach to studying Machine Learning that is mainly hands-on and abstracts most of the math for the beginner. 24 | This approach is unconventional because it’s the top-down and results-first approach designed for software engineers. 25 | 26 | Please, feel free to make any contributions you feel will make it better. 27 | 28 | --- 29 | 30 | ## Table of Contents 31 | 32 | - [What is it?](#what-is-it) 33 | - [Why use it?](#why-use-it) 34 | - [How to use it](#how-to-use-it) 35 | - [Follow me](#follow-me) 36 | - [Don't feel you aren't smart enough](#dont-feel-you-arent-smart-enough) 37 | - [About Video Resources](#about-video-resources) 38 | - [Prerequisite Knowledge](#prerequisite-knowledge) 39 | - [The Daily Plan](#the-daily-plan) 40 | - [Motivation](#motivation) 41 | - [Machine learning overview](#machine-learning-overview) 42 | - [Machine learning mastery](#machine-learning-mastery) 43 | - [Machine learning is fun](#machine-learning-is-fun) 44 | - [Machine learning: an in-depth, non-technical guide](#machine-learning-an-in-depth-non-technical-guide) 45 | - [Stories and experiences](#stories-and-experiences) 46 | - [Machine Learning Algorithms](#machine-learning-algorithms) 47 | - [Beginner Books](#beginner-books) 48 | - [Practical Books](#practical-books) 49 | - [Kaggle knowledge competitions](#kaggle-knowledge-competitions) 50 | - [Video Series](#video-series) 51 | - [MOOC](#mooc) 52 | - [Becoming an Open Source Contributor](#becoming-an-open-source-contributor) 53 | - [Communities](#communities) 54 | - [Interview Questions](#interview-questions) 55 | - [My admired companies](#my-admired-companies) 56 | 57 | --- 58 | 59 | ## Why use it? 60 | 61 | I'm following this plan to prepare for my near future job: Machine learning engineer. I've been building the native mobile application (Android/iOS/Blackberry) since 2011. I have a Software Engineering degree, not a Computer Science degree. I have itty bitty of basic knowledge about: Calculus, Linear Algebra, Discrete Mathematics, Probability & Statistics at university. 62 | Think about my interest in machine learning: 63 | - [Can I learn and get a job in Machine Learning without studying CS Master and PhD?](https://www.quora.com/Can-I-learn-and-get-a-job-in-Machine-Learning-without-studying-CS-Master-and-PhD) 64 | - You can, but it is far more difficult than when I got into the field. 65 | - [How do I get a job in Machine Learning as a software programmer who self-studies Machine Learning, but never has a chance to use it at work?](https://www.quora.com/How-do-I-get-a-job-in-Machine-Learning-as-a-software-programmer-who-self-studies-Machine-Learning-but-never-has-a-chance-to-use-it-at-work) 66 | - I'm hiring machine learning experts for my team and your MOOC will not get you the job (there is better news below). In fact, many people with a master's in machine learning will not get the job because they (and most who have taken MOOCs) do not have a deep understanding that will help me solve my problems 67 | - [What skills are needed for machine learning jobs?](http://programmers.stackexchange.com/questions/79476/what-skills-are-needed-for-machine-learning-jobs) 68 | - First, you need to have a decent CS/Math background. ML is an advanced topic so most textbooks assume that you have that background. Second, machine learning is a very general topic with many sub specialties requiring unique skills. You may want to browse the curriculum of an MS program in Machine Learning to see the course, curriculum and textbook. 69 | - Statistics, Probability, distributed computing, and Statistics. 70 | 71 | I find myself in times of trouble. 72 | 73 | AFAIK, [There are two sides to machine learning](http://machinelearningmastery.com/programmers-can-get-into-machine-learning/): 74 | - Practical Machine Learning: This is about queries databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality. 75 | - Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality. 76 | 77 | I think the best way for practice-focused methodology is something like ['practice — learning — practice'](http://machinelearningmastery.com/machine-learning-for-programmers/#comment-358985), that means where students first come with some existing projects with problems and solutions (practice) to get familiar with traditional methods in the area and perhaps also with their methodology. After practicing with some elementary experiences, they can go into the books and study the underlying theory, which serves to guide their future advanced practice and will enhance their toolbox of solving practical problems. Studying theory also further improves their understanding on the elementary experiences, and will help them acquire advanced experiences more quickly. 78 | 79 | It's a long plan. It's going to take me years. If you are familiar with a lot of this already it will take you a lot less time. 80 | 81 | ## How to use it 82 | Everything below is an outline, and you should tackle the items in order from top to bottom. 83 | 84 | I'm using Github's special markdown flavor, including tasks lists to check progress. 85 | 86 | - [x] Create a new branch so you can check items like this, just put an x in the brackets: [x] 87 | 88 | [More about Github-flavored markdown](https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown) 89 | 90 | ## Follow me 91 | I'm a Vietnamese Software Engineer who are really passionate and want to work in the USA. 92 | 93 | How much did I work during this plan? Roughly 4 hours/night after a long, hard day at work. 94 | 95 | I'm on the journey. 96 | 97 | - Twitter: [@Nam Vu](https://twitter.com/zuzoovn) 98 | 99 | | ![Nam Vu - Top-down learning path: machine learning for software engineers](http://sv1.upsieutoc.com/2016/10/08/331f241c8da44d0c43e9324d55440db6.md.jpg)| 100 | |:---:| 101 | | USA as heck | 102 | 103 | ## Don't feel you aren't smart enough 104 | I get discouraged from books and courses that tell me as soon as I can that multivariate calculus, inferential statistics and linear algebra are prerequisites. I still don’t know how to get started… 105 | 106 | - [What if I’m Not Good at Mathematics](http://machinelearningmastery.com/what-if-im-not-good-at-mathematics/) 107 | - [5 Techniques To Understand Machine Learning Algorithms Without the Background in Mathematics](http://machinelearningmastery.com/techniques-to-understand-machine-learning-algorithms-without-the-background-in-mathematics/) 108 | - [How do I learn machine learning?](https://www.quora.com/Machine-Learning/How-do-I-learn-machine-learning-1) 109 | 110 | ## About Video Resources 111 | 112 | Some videos are available only by enrolling in a Coursera or EdX class. It is free to do so, but sometimes the classes 113 | are no longer in session so you have to wait a couple of months, so you have no access. I'm going to be adding more videos 114 | from public sources and replacing the online course videos over time. I like using university lectures. 115 | 116 | ## Prerequisite Knowledge 117 | 118 | This short section were prerequisites/interesting info I wanted to learn before getting started on the daily plan. 119 | 120 | - [ ] [What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data?](https://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1) 121 | - [ ] [Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn) 122 | - [ ] [Don’t Break The Chain](http://lifehacker.com/281626/jerry-seinfelds-productivity-secret) 123 | - [ ] [How to learn on your own](https://metacademy.org/roadmaps/rgrosse/learn_on_your_own) 124 | 125 | ## The Daily Plan 126 | 127 | Each subject does not require a whole day to be able to understand it fully, and you can do multiple of these in a day. 128 | 129 | Each day I take one subject from the list below, read it cover to cover, take note, do the exercises and write an implementation in Python or R. 130 | 131 | # Motivation 132 | - [ ] [Dream](https://www.youtube.com/watch?v=g-jwWYX7Jlo) 133 | 134 | ## Machine learning overview 135 | - [ ] [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) 136 | - [ ] [A Gentle Guide to Machine Learning](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/) 137 | - [ ] [Machine Learning basics for a newbie](https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/) 138 | - [ ] [How do you explain Machine Learning and Data Mining to non Computer Science people?](https://www.quora.com/How-do-you-explain-Machine-Learning-and-Data-Mining-to-non-Computer-Science-people) 139 | - [ ] [Machine Learning: Under the hood. Blog post explains the principles of machine learning in layman terms. Simple and clear](https://georgemdallas.wordpress.com/2013/06/11/big-data-data-mining-and-machine-learning-under-the-hood/) 140 | 141 | ## Machine learning mastery 142 | - [ ] [The Machine Learning Mastery Method](http://machinelearningmastery.com/machine-learning-mastery-method/) 143 | - [ ] [Machine Learning for Programmers](http://machinelearningmastery.com/machine-learning-for-programmers/) 144 | - [ ] [Applied Machine Learning with Machine Learning Mastery](http://machinelearningmastery.com/start-here/) 145 | - [ ] [Python Machine Learning Mini-Course](http://machinelearningmastery.com/python-machine-learning-mini-course/) 146 | - [ ] [Machine Learning Algorithms Mini-Course](http://machinelearningmastery.com/machine-learning-algorithms-mini-course/) 147 | 148 | ## Machine learning is fun 149 | - [ ] [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.37ue6caww) 150 | - [ ] [Part 2: Using Machine Learning to generate Super Mario Maker levels](https://medium.com/@ageitgey/machine-learning-is-fun-part-2-a26a10b68df3#.kh7qgvp1b) 151 | - [ ] [Part 3: Deep Learning and Convolutional Neural Networks](https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.44rhxy637) 152 | - [ ] [Part 4: Modern Face Recognition with Deep Learning](https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78#.3rwmq0ddc) 153 | - [ ] [Part 5: Language Translation with Deep Learning and the Magic of Sequences](https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa#.wyfthap4c) 154 | 155 | ## Machine learning: an in-depth, non-technical guide 156 | - [ ] [Overview, goals, learning types, and algorithms](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide/) 157 | - [ ] [Data selection, preparation, and modeling](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-2/) 158 | - [ ] [Model evaluation, validation, complexity, and improvement](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-3/) 159 | - [ ] [Model performance and error analysis](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-4/) 160 | - [ ] [Unsupervised learning, related fields, and machine learning in practice](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-5/) 161 | 162 | ## Stories and experiences 163 | - [ ] [Machine Learning in a Week](https://medium.com/learning-new-stuff/machine-learning-in-a-week-a0da25d59850#.tk6ft2kcg) 164 | - [ ] [Machine Learning in a Year](https://medium.com/learning-new-stuff/machine-learning-in-a-year-cdb0b0ebd29c#.hhcb9fxk1) 165 | - [ ] [Learning Path : Your mentor to become a machine learning expert](https://www.analyticsvidhya.com/learning-path-learn-machine-learning/) 166 | - [ ] [You Too Can Become a Machine Learning Rock Star! No PhD](https://backchannel.com/you-too-can-become-a-machine-learning-rock-star-no-phd-necessary-107a1624d96b#.g9p16ldp7) 167 | - [ ] How to become a Data Scientist in 6 months: A hacker’s approach to career planning 168 | - [Video](https://www.youtube.com/watch?v=rIofV14c0tc) 169 | - [Slide](http://www.slideshare.net/TetianaIvanova2/how-to-become-a-data-scientist-in-6-months) 170 | - [ ] [5 Skills You Need to Become a Machine Learning Engineer](http://blog.udacity.com/2016/04/5-skills-you-need-to-become-a-machine-learning-engineer.html) 171 | - [ ] [Are you a self-taught machine learning engineer? If yes, how did you do it & how long did it take you?](https://www.quora.com/Are-you-a-self-taught-machine-learning-engineer-If-yes-how-did-you-do-it-how-long-did-it-take-you) 172 | - [ ] [How can one become a good machine learning engineer?](https://www.quora.com/How-can-one-become-a-good-machine-learning-engineer) 173 | - [ ] [A Learning Sabbatical focused on Machine Learning](http://karlrosaen.com/ml/) 174 | 175 | ## Machine Learning Algorithms 176 | - [ ] [10 Machine Learning Algorithms Explained to an ‘Army Soldier’](https://www.analyticsvidhya.com/blog/2015/12/10-machine-learning-algorithms-explained-army-soldier/) 177 | - [ ] [Top 10 data mining algorithms in plain English](https://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/) 178 | - [ ] [10 Machine Learning Terms Explained in Simple English](http://blog.aylien.com/10-machine-learning-terms-explained-in-simple/) 179 | - [ ] [A Tour of Machine Learning Algorithms](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/) 180 | 181 | ## Beginner Books 182 | - [ ] [Data Smart: Using Data Science to Transform Information into Insight 1st Edition](https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X) 183 | - [ ] [Data Science for Business: What you need to know about data mining and data­ analytic-thinking](https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323/) 184 | - [ ] [Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die](https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853) 185 | 186 | ## Practical Books 187 | - [ ] [Machine Learning for Hackers](https://www.amazon.com/Machine-Learning-Hackers-Drew-Conway/dp/1449303714) 188 | - [GitHub repository(R)](https://github.com/johnmyleswhite/ML_for_Hackers) 189 | - [GitHub repository(Python)](https://github.com/carljv/Will_it_Python) 190 | - [ ] [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka-ebook/dp/B00YSILNL0) 191 | - [GitHub repository](https://github.com/rasbt/python-machine-learning-book) 192 | - [ ] [Programming Collective Intelligence: Building Smart Web 2.0 Applications](https://www.amazon.com/Programming-Collective-Intelligence-Building-Applications-ebook/dp/B00F8QDZWG) 193 | - [ ] [Machine Learning: An Algorithmic Perspective, Second Edition](https://www.amazon.com/Machine-Learning-Algorithmic-Perspective-Recognition/dp/1466583282) 194 | - [GitHub repository](https://github.com/alexsosn/MarslandMLAlgo) 195 | - [Resource repository](http://seat.massey.ac.nz/personal/s.r.marsland/MLbook.html) 196 | - [ ] [Introduction to Machine Learning with Python: A Guide for Data Scientists](http://shop.oreilly.com/product/0636920030515.do) 197 | - [GitHub repository](https://github.com/amueller/introduction_to_ml_with_python) 198 | - [ ] [Data Mining: Practical Machine Learning Tools and Techniques, Third Edition](https://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0123748569) 199 | - Teaching material 200 | - [Slides for Chapters 1-5 (zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch1-5.zip) 201 | - [Slides for Chapters 6-8 (zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch6-8.zip) 202 | - [ ] [Machine Learning in Action](https://www.amazon.com/Machine-Learning-Action-Peter-Harrington/dp/1617290181/) 203 | - [GitHub repository](https://github.com/pbharrin/machinelearninginaction) 204 | - [ ] [Reactive Machine Learning Systems(MEAP)](https://www.manning.com/books/reactive-machine-learning-systems) 205 | - [GitHub repository](https://github.com/jeffreyksmithjr/reactive-machine-learning-systems) 206 | - [ ] [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) 207 | - [GitHub repository(R)](http://www-bcf.usc.edu/~gareth/ISL/code.html) 208 | - [GitHub repository(Python)](https://github.com/JWarmenhoven/ISLR-python) 209 | - [ ] [Building Machine Learning Systems with Python](https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-systems-python) 210 | - [GitHub repository](https://github.com/luispedro/BuildingMachineLearningSystemsWithPython) 211 | - [ ] [Probabilistic Programming & Bayesian Methods for Hackers](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) 212 | - [ ] [Probabilistic Graphical Models: Principles and Techniques](https://www.amazon.com/Probabilistic-Graphical-Models-Principles-Computation/dp/0262013193) 213 | 214 | ## Kaggle knowledge competitions 215 | - [ ] [Kaggle Competitions: How and where to begin?](https://www.analyticsvidhya.com/blog/2015/06/start-journey-kaggle/) 216 | - [ ] [How a Beginner Used Small Projects To Get Started in Machine Learning and Compete on Kaggle](http://machinelearningmastery.com/how-a-beginner-used-small-projects-to-get-started-in-machine-learning-and-compete-on-kaggle) 217 | - [ ] [Master Kaggle By Competing Consistently](http://machinelearningmastery.com/master-kaggle-by-competing-consistently/) 218 | 219 | ## Video Series 220 | - [ ] [Machine Learning for Hackers](https://www.youtube.com/playlist?list=PL2-dafEMk2A4ut2pyv0fSIXqOzXtBGkLj) 221 | - [ ] [Fresh Machine Learning](https://www.youtube.com/playlist?list=PL2-dafEMk2A6Kc7pV6gHH-apBFxwFjKeY) 222 | - [ ] [Machine Learning Recipes with Josh Gordon](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal) 223 | - [ ] [Everything You Need to know about Machine Learning in 30 Minutes or Less](https://vimeo.com/43547079) 224 | - [ ] [Nuts and Bolts of Applying Deep Learning - Andrew Ng](https://www.youtube.com/watch?v=F1ka6a13S9I) 225 | - [ ] BigML Webinar 226 | - [Video](https://www.youtube.com/watch?list=PL1bKyu9GtNYHcjGa6ulrvRVcm1lAB8he3&v=W62ehrnOVqo) 227 | - [Resources](https://bigml.com/releases) 228 | - [ ] [mathematicalmonk's Machine Learning tutorials](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA) 229 | 230 | 231 | ## MOOC 232 | - [ ] [Udacity’s Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120) 233 | - [Udacity Intro to Machine Learning Review](http://hamelg.blogspot.com/2014/12/udacity-intro-to-machine-learning-review.html) 234 | - [ ] [Udacity’s Supervised, Unsupervised & Reinforcement](https://www.udacity.com/course/machine-learning--ud262) 235 | - [ ] [Machine Learning Foundations: A Case Study Approach](https://www.coursera.org/learn/ml-foundations) 236 | - [ ] [Coursera’s Machine Learning](https://www.coursera.org/learn/machine-learning) 237 | - [Video only](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW) 238 | - [Coursera Machine Learning review](https://rayli.net/blog/data/coursera-machine-learning-review/) 239 | - [Coursera: Machine Learning Roadmap](https://metacademy.org/roadmaps/cjrd/coursera_ml_supplement) 240 | - [ ] [Machine Learning Distilled](https://code.tutsplus.com/courses/machine-learning-distilled) 241 | - [ ] [BigML training](https://bigml.com/training) 242 | - [ ] [Coursera’s Neural Networks for Machine Learning](https://www.coursera.org/learn/neural-networks) 243 | - Taught by Geoffrey Hinton, a pioneer in the field of neural networks 244 | - [ ] [Machine Learning - CS - Oxford University](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) 245 | - [ ] [Creative Applications of Deep Learning with TensorFlow](https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info) 246 | - [ ] [Intro to Descriptive Statistics](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827) 247 | - [ ] [Intro to Inferential Statistics](https://www.udacity.com/course/intro-to-inferential-statistics--ud201) 248 | 249 | ## Resources 250 | - [ ] [Machine Learning for Developers](https://xyclade.github.io/MachineLearning/) 251 | - [ ] [Machine Learning Advice for Developers](https://dev.to/thealexlavin/machine-learning-advice-for-developers) 252 | - [ ] [Machine Learning For Complete Beginners](http://pythonforengineers.com/machine-learning-for-complete-beginners/) 253 | - [ ] [Machine Learning Self-study Resources](https://ragle.sanukcode.net/articles/machine-learning-self-study-resources/) 254 | - [ ] [Level-Up Your Machine Learning](https://metacademy.org/roadmaps/cjrd/level-up-your-ml) 255 | - [ ] Enough Machine Learning to Make Hacker News Readable Again 256 | - [Video](https://www.youtube.com/watch?v=O7IezJT9uSI) 257 | - [Slide](https://speakerdeck.com/pycon2014/enough-machine-learning-to-make-hacker-news-readable-again-by-ned-jackson-lovely) 258 | - [ ] [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning) 259 | - Flipboard Topics 260 | - [Machine learning](https://flipboard.com/topic/machinelearning) 261 | - [Deep learning](https://flipboard.com/topic/deeplearning) 262 | - [Artificial Intelligence](https://flipboard.com/topic/artificialintelligence) 263 | - Medium Topics 264 | - [Machine learning](https://medium.com/tag/machine-learning/latest) 265 | - [Deep learning](https://medium.com/tag/deep-learning) 266 | - [Artificial Intelligence](https://medium.com/tag/artificial-intelligence) 267 | - Monthly top 10 articles 268 | - Machine Learning 269 | - [July 2016](https://medium.mybridge.co/top-ten-machine-learning-articles-for-the-past-month-9c1202351144#.lyycen64y) 270 | - [August 2016](https://medium.mybridge.co/machine-learning-top-10-articles-for-the-past-month-2f3cb815ffed#.i9ee7qkjz) 271 | - [September 2016](https://medium.mybridge.co/machine-learning-top-10-in-september-6838169e9ee7#.4jbjcibft) 272 | - Algorithms 273 | - [September 2016](https://medium.mybridge.co/algorithm-top-10-articles-in-september-8a0e6afb0807#.hgjzuyxdb) 274 | - [Comprehensive list of data science resources](http://www.datasciencecentral.com/group/resources/forum/topics/comprehensive-list-of-data-science-resources) 275 | - [Machine Learning Summer Schools](http://mlss.cc/) 276 | - [DigitalMind's Artificial Intelligence resources](http://blog.digitalmind.io/post/artificial-intelligence-resources) 277 | 278 | ## Becoming an Open Source Contributor 279 | - [ ] [tensorflow/magenta: Magenta: Music and Art Generation with Machine Intelligence](https://github.com/tensorflow/magenta) 280 | - [ ] [tensorflow/tensorflow: Computation using data flow graphs for scalable machine learning](https://github.com/tensorflow/tensorflow) 281 | - [ ] [cmusatyalab/openface: Face recognition with deep neural networks.](https://github.com/cmusatyalab/openface) 282 | - [ ] [tensorflow/models/syntaxnet: Neural Models of Syntax.](https://github.com/tensorflow/models/tree/master/syntaxnet) 283 | 284 | ## Podcasts 285 | - ### Podcasts for Beginners: 286 | - [Talking Machines](http://www.thetalkingmachines.com/) 287 | - [Linear Digressions](http://lineardigressions.com/) 288 | - [Data Skeptic](http://dataskeptic.com/) 289 | - [This Week in Machine Learning & AI](https://twimlai.com/) 290 | 291 | - ### "More" advanced podcasts 292 | - [Partially Derivative](http://partiallyderivative.com/) 293 | - [O’Reilly Data Show](http://radar.oreilly.com/tag/oreilly-data-show-podcast) 294 | - [Not So Standard Deviation](https://soundcloud.com/nssd-podcast) 295 | 296 | - ### Podcasts to think outside the box: 297 | - [Data Stories](http://datastori.es/) 298 | 299 | ## Communities 300 | - Quora 301 | - [Machine Learning](https://www.quora.com/topic/Machine-Learning) 302 | - [Statistics](https://www.quora.com/topic/Statistics-academic-discipline) 303 | - [Data Mining](https://www.quora.com/topic/Data-Mining) 304 | 305 | - Reddit 306 | - [Machine Learning](https://www.reddit.com/r/machinelearning) 307 | - [Computer Vision](https://www.reddit.com/r/computervision) 308 | - [Natural Language](https://www.reddit.com/r/languagetechnology) 309 | - [Data Science](https://www.reddit.com/r/datascience) 310 | - [Big Data](https://www.reddit.com/r/bigdata) 311 | - [Statistics](https://www.reddit.com/r/statistics) 312 | 313 | - [Data Tau](http://www.datatau.com/) 314 | 315 | - [Deep Learning News](http://news.startup.ml/) 316 | 317 | - [KDnuggets](http://www.kdnuggets.com/) 318 | 319 | ##Interview Questions 320 | - [ ] [How To Prepare For A Machine Learning Interview](http://blog.udacity.com/2016/05/prepare-machine-learning-interview.html) 321 | - [ ] [40 Interview Questions asked at Startups in Machine Learning / Data Science](https://www.analyticsvidhya.com/blog/2016/09/40-interview-questions-asked-at-startups-in-machine-learning-data-science) 322 | - [ ] [21 Must-Know Data Science Interview Questions and Answers](http://www.kdnuggets.com/2016/02/21-data-science-interview-questions-answers.html) 323 | - [ ] [Top 50 Machine learning Interview questions & Answers](http://career.guru99.com/top-50-interview-questions-on-machine-learning/) 324 | - [ ] [Machine Learning Engineer interview questions](https://resources.workable.com/machine-learning-engineer-interview-questions) 325 | - [ ] [Popular Machine Learning Interview Questions](http://www.learn4master.com/machine-learning/popular-machine-learning-interview-questions) 326 | - [ ] [What are some common Machine Learning interview questions?](https://www.quora.com/What-are-some-common-Machine-Learning-interview-questions) 327 | - [ ] [What are the best interview questions to evaluate a machine learning researcher?](https://www.quora.com/What-are-the-best-interview-questions-to-evaluate-a-machine-learning-researcher) 328 | - [ ] [Collection of Machine Learning Interview Questions](http://analyticscosm.com/machine-learning-interview-questions-for-data-scientist-interview/) 329 | 330 | 331 | 332 | ## My admired companies 333 | - [ ] [ELSA - Your virtual pronunciation coach](https://www.elsanow.io/home) 334 | --------------------------------------------------------------------------------