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# Top-down learning path: Machine Learning for Software Engineers
Inspired by [Google Interview University](https://github.com/jwasham/google-interview-university).
_Se você gostou deste projeto, por favor me dê uma estrela._ ★
## O que é?
Este é meu plano de estudo para ir de desenvolvedor mobile (autodidata, sem diploma) para Engenheiro de Machine Learning.
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.
Por favor, sinta-se livre para fazer qualquer contribuição que você achar que pode o tornar melhor.
---
## Tabela de conteúdo
- [O que é?](#o-que-é)
- [Por que usar?](#por-que-usar)
- [Como usar](#como-usar)
- [Siga-me](#siga-me)
- [Não sinta que não é inteligente o bastante](#não-sinta-que-não-é-inteligente-o-bastante)
- [Sobre Video Resources](#sobre-video-resources)
- [Conhecimento prévio](#conhecimento-prévio)
- [O Plano diário](#o-plano-diário)
- [Motivação](#motivação)
- [Visão geral do Machine Learning](#visão-geral-do-machine-learning)
- [Maestria do Machine Learning](#maestria-do-machine-learning)
- [Machine Learning é divertido](#machine-learning-é-divertido)
- [Machine learning: um guia profundo, não técnico](#machine-learning-um-guia-profundo-não-técnico)
- [Relatos e experiências](#relatos-e-experiências)
- [Livros para iniciantes](#livros-para-iniciantes)
- [Livros para prática](#livros-para-prática)
- [Competições de conhecimento Kaggle](#competições-de-conhecimento-kaggle)
- [Video Series](#video-series)
- [MOOC](#mooc)
- [Pesquisas](pesquisas)
- [Torna-se um contribuidor Open Source](#torne-se-um-contribuidor-open-sourse)
- [Communidades](#comunidades)
- [My admired companies](#my-admired-companies)
---
## Por que usar?
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.
Pense sobre meu interesse em Machine Learning:
- [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)
- Você pode, mas isto é muito mais difícil do que quando eu entrei no campo.
- [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)
- 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.
- [Que habilidades são necessárias para trabalhos de Machine Learning?](http://programmers.stackexchange.com/questions/79476/what-skills-are-needed-for-machine-learning-jobs)
- 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.
- Estatística, propabilidade, computação distribuída e estatística.
Eu me encontro em tempos difíceis.
AFAIK, [Há dois lados para Machine Learning](http://machinelearningmastery.com/programmers-can-get-into-machine-learning/):
- 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.
- 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.
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.
É um plano longo. Isso vai demorar anos para mim. Se você já está familiarizado com bastante disso já, você levará muito menos tempo.
## Como usar
Tudo abaixo é uma estrutura de tópicos, e você deve enfrentar os itens em ordem de cima para baixo.
Eu estou usando o especial Markdown do Github, incluindo a lista de tarefas para verificar o progresso.
- [x] Crie um novo branch, então você poderá verificar itens como esse, apenas coloque um x entre os colchetes.
[More about Github-flavored markdown](https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown)
## Siga-me
Eu sou um engenheiro de Software vietnamita que é realmente apaixonado e quer trabalhar nos EUA.
Quanto eu trabalhei durante este plano? Aproximadamente 4 horas/noite após um dia longo no trabalho.
Eu estou na jornada.
| ![Nam Vu - Top-down learning path: machine learning for software engineers](http://sv1.upsieutoc.com/2016/10/08/331f241c8da44d0c43e9324d55440db6.md.jpg)|
|:---:|
| USA as heck |
## Não sinta que não é inteligente o bastante
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...
- [What if I'm Not Good at Mathematics](http://machinelearningmastery.com/what-if-im-not-good-at-mathematics/)
- [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/)
- [How do I learn machine learning?](https://www.quora.com/Machine-Learning/How-do-I-learn-machine-learning-1)
## Sobre Video Resources
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.
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.
## Conhecimento prévio
Esta seção curta foram pré-requisitos/informações interessantes que eu queria aprender antes de começar o plano diário.
- [ ] [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)
- [ ] [Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn)
- [ ] [Don't Break The Chain](http://lifehacker.com/281626/jerry-seinfelds-productivity-secret)
- [ ] [How to learn on your own](https://metacademy.org/roadmaps/rgrosse/learn_on_your_own)
## O Plano Diário
Cada assunto não requer um dia inteiro para ser capaz de compreendê-lo totalmente, e você pode fazer vários desses em um dia.
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.
# Motivação
- [ ] [Dream](https://www.youtube.com/watch?v=g-jwWYX7Jlo)
## Visão geral do Machine learning
- [ ] [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
- [ ] [A Gentle Guide to Machine Learning](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/)
- [ ] [Machine Learning basics for a newbie](https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/)
## Maestria do Machine learning
- [ ] [The Machine Learning Mastery Method](http://machinelearningmastery.com/machine-learning-mastery-method/)
- [ ] [Machine Learning for Programmers](http://machinelearningmastery.com/machine-learning-for-programmers/)
- [ ] [Applied Machine Learning with Machine Learning Mastery](http://machinelearningmastery.com/start-here/)
- [ ] [Python Machine Learning Mini-Course](http://machinelearningmastery.com/python-machine-learning-mini-course/)
- [ ] [Machine Learning Algorithms Mini-Course](http://machinelearningmastery.com/machine-learning-algorithms-mini-course/)
## Machine learning é divertido
- [ ] [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.37ue6caww)
- [ ] [Part 2: Using Machine Learning to generate Super Mario Maker levels](https://medium.com/@ageitgey/machine-learning-is-fun-part-2-a26a10b68df3#.kh7qgvp1b)
- [ ] [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)
- [ ] [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)
- [ ] [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)
## Machine learning: um guia profundo, não técnico
- [ ] [Overview, goals, learning types, and algorithms](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide/)
- [ ] [Data selection, preparation, and modeling](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-2/)
- [ ] [Model evaluation, validation, complexity, and improvement](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-3/)
- [ ] [Model performance and error analysis](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-4/)
- [ ] [Unsupervised learning, related fields, and machine learning in practice](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-5/)
## Relatos e experiências
- [ ] [Machine Learning in a Week](https://medium.com/learning-new-stuff/machine-learning-in-a-week-a0da25d59850#.tk6ft2kcg)
- [ ] [Machine Learning in a Year](https://medium.com/learning-new-stuff/machine-learning-in-a-year-cdb0b0ebd29c#.hhcb9fxk1)
- [ ] [Learning Path : Your mentor to become a machine learning expert](https://www.analyticsvidhya.com/learning-path-learn-machine-learning/)
- [ ] [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)
- [ ] How to become a Data Scientist in 6 months: A hacker’s approach to career planning
- [Video](https://www.youtube.com/watch?v=rIofV14c0tc)
- [Slide](http://www.slideshare.net/TetianaIvanova2/how-to-become-a-data-scientist-in-6-months)
- [ ] [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)
- [ ] [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)
- [ ] [How can one become a good machine learning engineer?](https://www.quora.com/How-can-one-become-a-good-machine-learning-engineer)
## Livros para iniciantes
- [ ] [Data Smart: Using Data Science to Transform Information into Insight 1st Edition](https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X)
- [ ] [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/)
- [ ] [Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die](https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853)
## Livros para prática
- [ ] [Machine Learning for Hackers](https://www.amazon.com/Machine-Learning-Hackers-Drew-Conway/dp/1449303714)
- [GitHub repository](https://github.com/johnmyleswhite/ML_for_Hackers)
- [ ] [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka-ebook/dp/B00YSILNL0)
- [GitHub repository](https://github.com/rasbt/python-machine-learning-book)
- [ ] [Programming Collective Intelligence: Building Smart Web 2.0 Applications](https://www.amazon.com/Programming-Collective-Intelligence-Building-Applications-ebook/dp/B00F8QDZWG)
- [ ] [Machine Learning: An Algorithmic Perspective, Second Edition](https://www.amazon.com/Machine-Learning-Algorithmic-Perspective-Recognition/dp/1466583282)
- [GitHub repository](https://github.com/alexsosn/MarslandMLAlgo)
- [Resource repository](http://seat.massey.ac.nz/personal/s.r.marsland/MLbook.html)
- [ ] [Introduction to Machine Learning with Python: A Guide for Data Scientists](http://shop.oreilly.com/product/0636920030515.do)
- [GitHub repository](https://github.com/amueller/introduction_to_ml_with_python)
- [ ] [Data Mining: Practical Machine Learning Tools and Techniques, Third Edition](https://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0123748569)
- Teaching material
- [Slides for Chapters 1-5 (zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch1-5.zip)
- [Slides for Chapters 6-8 (zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch6-8.zip)
- [ ] [Machine Learning in Action](https://www.amazon.com/Machine-Learning-Action-Peter-Harrington/dp/1617290181/)
- [GitHub repository](https://github.com/pbharrin/machinelearninginaction)
- [ ] [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)
## Competições de conhecimento Kaggle
- [ ] [Kaggle Competitions: How and where to begin?](https://www.analyticsvidhya.com/blog/2015/06/start-journey-kaggle/)
- [ ] [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)
- [ ] [Master Kaggle By Competing Consistently](http://machinelearningmastery.com/master-kaggle-by-competing-consistently/)
## Video Series
- [ ] [Machine Learning for Hackers](https://www.youtube.com/playlist?list=PL2-dafEMk2A4ut2pyv0fSIXqOzXtBGkLj)
- [ ] [Fresh Machine Learning](https://www.youtube.com/playlist?list=PL2-dafEMk2A6Kc7pV6gHH-apBFxwFjKeY)
- [ ] [Machine Learning Recipes with Josh Gordon](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)
- [ ] [Everything You Need to know about Machine Learning in 30 Minutes or Less](https://vimeo.com/43547079)
## MOOC
- [ ] [Udacity's Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120)
- [Udacity Intro to Machine Learning Review](http://hamelg.blogspot.com/2014/12/udacity-intro-to-machine-learning-review.html)
- [ ] [Udacity's Supervised, Unsupervised & Reinforcement](https://www.udacity.com/course/machine-learning--ud262)
- [ ] [Machine Learning Foundations: A Case Study Approach](https://www.coursera.org/learn/ml-foundations)
- [ ] [Coursera's Machine Learning](https://www.coursera.org/learn/machine-learning)
- [Video only](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW)
- [Coursera Machine Learning review](https://rayli.net/blog/data/coursera-machine-learning-review/)
- [Coursera: Machine Learning Roadmap](https://metacademy.org/roadmaps/cjrd/coursera_ml_supplement)
## Pesquisas
- [ ] [Machine Learning for Developers](https://xyclade.github.io/MachineLearning/)
- [ ] [Machine Learning Advice for Developers](https://dev.to/thealexlavin/machine-learning-advice-for-developers)
- [ ] [Machine Learning For Complete Beginners](http://pythonforengineers.com/machine-learning-for-complete-beginners/)
- [ ] [Machine Learning Self-study Resources](https://ragle.sanukcode.net/articles/machine-learning-self-study-resources/)
- [ ] [Level-Up Your Machine Learning](https://metacademy.org/roadmaps/cjrd/level-up-your-ml)
- [ ] [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)
## Torne-se um contribuidor Open Sourse
- [ ] [tensorflow/magenta: Magenta: Music and Art Generation with Machine Intelligence](https://github.com/tensorflow/magenta)
- [ ] [tensorflow/tensorflow: Computation using data flow graphs for scalable machine learning](https://github.com/tensorflow/tensorflow)
- [ ] [cmusatyalab/openface: Face recognition with deep neural networks.](https://github.com/cmusatyalab/openface)
- [ ] [tensorflow/models/syntaxnet: Neural Models of Syntax.](https://github.com/tensorflow/models/tree/master/syntaxnet)
## Comunidades
- ### Quora
- [Machine Learning](https://www.quora.com/topic/Machine-Learning)
- [Statistics](https://www.quora.com/topic/Statistics-academic-discipline)
- [Data Mining](https://www.quora.com/topic/Data-Mining)
- ### Reddit
- [Machine Learning](https://www.reddit.com/r/machinelearning)
- ### [Data Tau](http://www.datatau.com/)
## My admired companies
- [ ] [ELSA - Your virtual pronunciation coach](https://www.elsanow.io/home)

@ -1,307 +0,0 @@
# Top-down learning path and resources: Machine Learning for Software Engineers
Inspired by [Machine Learning for Software Engineers
](https://github.com/ZuzooVn/machine-learning-for-software-engineers) by [Google Interview University](https://github.com/jwasham/google-interview-university).
Translations: [Brazilian Portuguese](https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README-pt-BR.md)
## Table of Contents
- [Sides of machine learning?](#sides-of-machine-learning)
- [Don't feel you aren't smart enough](#dont-feel-you-arent-smart-enough)
- [Prerequisite Knowledge](#prerequisite-knowledge)
- [Machine learning overview](#machine-learning-overview)
- [Machine learning mastery](#machine-learning-mastery)
- [Machine learning is fun](#machine-learning-is-fun)
- [Machine learning: an in-depth, non-technical guide](#machine-learning-an-in-depth-non-technical-guide)
- [Stories and experiences](#stories-and-experiences)
- [Machine Learning Algorithms](#machine-learning-algorithms)
- [Deep Learning Resources](#deep-learning-resources)
- [Deep Learning To read](#deep-learning-to-read)
- [Beginner Books](#beginner-books)
- [Practical Books](#practical-books)
- [Kaggle knowledge competitions](#kaggle-knowledge-competitions)
- [Video Series](#video-series)
- [MOOC](#mooc)
- [Resources](#resources)
- [Becoming an Open Source Contributor](#becoming-an-open-source-contributor)
- [Podcasts](#podcasts)
- [Communities](#communities)
---
## Sides of Machine Learning:
[There are two sides to machine learning](http://machinelearningmastery.com/programmers-can-get-into-machine-learning/):
- 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. Its the mess of reality.
- 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.
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.
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.
## Don't feel you aren't smart enough
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 dont know how to get started…
- [What if Im Not Good at Mathematics](http://machinelearningmastery.com/what-if-im-not-good-at-mathematics/)
- [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/)
- [How do I learn machine learning?](https://www.quora.com/Machine-Learning/How-do-I-learn-machine-learning-1)
## Prerequisite Knowledge
This short section were prerequisites/interesting info I wanted to learn before getting started on the daily plan.
- [x] [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)
- [x] [Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn)
- [x] [Dont Break The Chain](http://lifehacker.com/281626/jerry-seinfelds-productivity-secret)
- [x] [How to learn on your own](https://metacademy.org/roadmaps/rgrosse/learn_on_your_own)
## Math Fundamentals
- [ ] [Coding the Matrix](http://codingthematrix.com) [torrent](http://academictorrents.com/details/54cd86f3038dfd446b037891406ba4e0b1200d5a)
- [ ] Coursera introduction to statistics 101
- [ ] [Linear Algebra essentials (https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
- [ ] [Linear Algebra Guide](https://betterexplained.com/articles/linear-algebra-guide/)
## Machine learning overview
- [ ] [Visual Interactive Guide To Basics - Neural Networks](https://jalammar.github.io/visual-interactive-guide-basics-neural-networks/)
- [x] [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
- [ ] [A Gentle Guide to Machine Learning](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/)
- [ ] [Machine Learning basics for a newbie](https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/)
- [ ] [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)
- [ ] [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/)
- [ ] [What is machine learning, and how does it work?](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1)
## Machine learning mastery
- [ ] [Machine Learning Algorithms in pure python](github.com/eriklindernoren/ML-From-Scratch)
- [ ] [The Machine Learning Mastery Method](http://machinelearningmastery.com/machine-learning-mastery-method/)
- [ ] [Machine Learning for Programmers](http://machinelearningmastery.com/machine-learning-for-programmers/)
- [ ] [Applied Machine Learning with Machine Learning Mastery](http://machinelearningmastery.com/start-here/)
- [ ] [Python Machine Learning Mini-Course](http://machinelearningmastery.com/python-machine-learning-mini-course/)
- [ ] [Machine Learning Algorithms Mini-Course](http://machinelearningmastery.com/machine-learning-algorithms-mini-course/)
## Machine learning is fun
- [ ] [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.37ue6caww)
- [ ] [Part 2: Using Machine Learning to generate Super Mario Maker levels](https://medium.com/@ageitgey/machine-learning-is-fun-part-2-a26a10b68df3#.kh7qgvp1b)
- [ ] [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)
- [ ] [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)
- [ ] [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)
## Machine learning: an in-depth, non-technical guide
- [ ] [Overview, goals, learning types, and algorithms](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide/)
- [ ] [Data selection, preparation, and modeling](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-2/)
- [ ] [Model evaluation, validation, complexity, and improvement](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-3/)
- [ ] [Model performance and error analysis](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-4/)
- [ ] [Unsupervised learning, related fields, and machine learning in practice](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-5/)
## Machine Learning in-depth math theory
- [x] [Understanding Bayes: A Look at the Likelihood](https://alexanderetz.com/2015/04/15/understanding-bayes-a-look-at-the-likelihood/)
- [x] [Logistic Regression and Gradient Descent](http://www.cs.rpi.edu/~magdon/courses/LFD-Slides/SlidesLect09.pdf)
- [x] [The Method of Maximum Likelihood for Simple Linear Regression](www.stat.cmu.edu/~cshalizi/mreg/15/lectures/06/lecture-06.pdf)
- [x] [Regression Estimation - Least Squares and Maximum Likelihood](http://www.robots.ox.ac.uk/~fwood/teaching/W4315_Fall2011/Lectures/lecture_3/lecture_3.pdf)
- [x] [SVMs and Kernel Method](http://www.win-vector.com/blog/2011/10/kernel-methods-and-support-vector-machines-de-mystified/)
## Stories and experiences
- [ ] [Machine Learning in a Week](https://medium.com/learning-new-stuff/machine-learning-in-a-week-a0da25d59850#.tk6ft2kcg)
- [ ] [Machine Learning in a Year](https://medium.com/learning-new-stuff/machine-learning-in-a-year-cdb0b0ebd29c#.hhcb9fxk1)
- [ ] [How I wrote my first Machine Learning program in 3 days](http://blog.adnansiddiqi.me/how-i-wrote-my-first-machine-learning-program-in-3-days/)
- [ ] [Learning Path : Your mentor to become a machine learning expert](https://www.analyticsvidhya.com/learning-path-learn-machine-learning/)
- [ ] [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)
- [ ] How to become a Data Scientist in 6 months: A hackers approach to career planning
- [Video](https://www.youtube.com/watch?v=rIofV14c0tc)
- [Slide](http://www.slideshare.net/TetianaIvanova2/how-to-become-a-data-scientist-in-6-months)
- [ ] [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)
- [ ] [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)
- [ ] [How can one become a good machine learning engineer?](https://www.quora.com/How-can-one-become-a-good-machine-learning-engineer)
- [ ] [A Learning Sabbatical focused on Machine Learning](http://karlrosaen.com/ml/)
## Machine Learning Algorithms
- [x] [Gradient Descent with Backpropagation](http://outlace.com/Beginner-Tutorial-Backpropagation/)
- [ ] [10 Machine Learning Algorithms Explained to an Army Soldier](https://www.analyticsvidhya.com/blog/2015/12/10-machine-learning-algorithms-explained-army-soldier/)
- [ ] [Top 10 data mining algorithms in plain English](https://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/)
- [ ] [10 Machine Learning Terms Explained in Simple English](http://blog.aylien.com/10-machine-learning-terms-explained-in-simple/)
- [ ] [A Tour of Machine Learning Algorithms](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/)
- [ ] [The 10 Algorithms Machine Learning Engineers Need to Know](https://gab41.lab41.org/the-10-algorithms-machine-learning-engineers-need-to-know-f4bb63f5b2fa#.ofc7t2965)
- [ ] [Comparing supervised learning algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/)
## Deep Learning Resources
- [x] [i am trask](http://iamtrask.github.io)
- [ ] [Awesome Deep Learning Papers](https://github.com/terryum/awesome-deep-learning-papers)
- [x] [Word vectors intuition](https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/)
- [x] [Understanding word2vec NN and how semantics are extracted](http://www-personal.umich.edu/~ronxin/pdf/w2vexp.pdf)
- [x] [word2vec explorable explanation](https://ronxin.github.io/wevi/)
- [ ] [Deep Learning Tensorflow Algorithms](http://deep-learning-tensorflow.readthedocs.io/en/latest/#)
- [ ] [Visualizing and Understanding Convolutional Networks](www.matthewzeiler.com/pubs/arxive2013/eccv2014.pdf)
## Deep Learning To Read
- [ ] [Google NN for machine translation](research.googleblog.com/2016/09/a-neural-network-for-machine.html)
## Beginner Books
- [ ] [Data Smart: Using Data Science to Transform Information into Insight 1st Edition](https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X)
- [ ] [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/)
- [ ] [Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die](https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853)
## Practical Books
- [ ] [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)
- [ ] [Machine Learning for Hackers](https://www.amazon.com/Machine-Learning-Hackers-Drew-Conway/dp/1449303714)
- [GitHub repository(R)](https://github.com/johnmyleswhite/ML_for_Hackers)
- [GitHub repository(Python)](https://github.com/carljv/Will_it_Python)
- [ ] [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka-ebook/dp/B00YSILNL0)
- [GitHub repository](https://github.com/rasbt/python-machine-learning-book)
- [ ] [Programming Collective Intelligence: Building Smart Web 2.0 Applications](https://www.amazon.com/Programming-Collective-Intelligence-Building-Applications-ebook/dp/B00F8QDZWG)
- [ ] [Machine Learning: An Algorithmic Perspective, Second Edition](https://www.amazon.com/Machine-Learning-Algorithmic-Perspective-Recognition/dp/1466583282)
- [GitHub repository](https://github.com/alexsosn/MarslandMLAlgo)
- [Resource repository](http://seat.massey.ac.nz/personal/s.r.marsland/MLbook.html)
- [ ] [Introduction to Machine Learning with Python: A Guide for Data Scientists](http://shop.oreilly.com/product/0636920030515.do)
- [GitHub repository](https://github.com/amueller/introduction_to_ml_with_python)
- [ ] [Data Mining: Practical Machine Learning Tools and Techniques, Third Edition](https://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0123748569)
- Teaching material
- [Slides for Chapters 1-5 (zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch1-5.zip)
- [Slides for Chapters 6-8 (zip)](http://www.cs.waikato.ac.nz/ml/weka/Slides3rdEd_Ch6-8.zip)
- [ ] [Machine Learning in Action](https://www.amazon.com/Machine-Learning-Action-Peter-Harrington/dp/1617290181/)
- [GitHub repository](https://github.com/pbharrin/machinelearninginaction)
- [ ] [Reactive Machine Learning Systems(MEAP)](https://www.manning.com/books/reactive-machine-learning-systems)
- [GitHub repository](https://github.com/jeffreyksmithjr/reactive-machine-learning-systems)
- [ ] [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)
- [GitHub repository(R)](http://www-bcf.usc.edu/~gareth/ISL/code.html)
- [GitHub repository(Python)](https://github.com/JWarmenhoven/ISLR-python)
- [Videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)
- [ ] [Building Machine Learning Systems with Python](https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-systems-python)
- [GitHub repository](https://github.com/luispedro/BuildingMachineLearningSystemsWithPython)
- [ ] [Learning scikit-learn: Machine Learning in Python](https://www.packtpub.com/big-data-and-business-intelligence/learning-scikit-learn-machine-learning-python)
- [GitHub repository](https://github.com/gmonce/scikit-learn-book)
- [ ] [Probabilistic Programming & Bayesian Methods for Hackers](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)
- [ ] [Probabilistic Graphical Models: Principles and Techniques](https://www.amazon.com/Probabilistic-Graphical-Models-Principles-Computation/dp/0262013193)
- [ ] [Machine Learning: Hands-On for Developers and Technical Professionals](https://www.amazon.com/Machine-Learning-Hands-Developers-Professionals/dp/1118889061)
- [Machine Learning Hands-On for Developers and Technical Professionals review](https://blogs.msdn.microsoft.com/querysimon/2015/01/01/book-review-machine-learning-hands-on-for-developers-and-technical-professionals/)
- [GitHub repository](https://github.com/jasebell/mlbook)
- [ ] [Learning from Data](https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/1600490069)
- [Online tutorials](https://work.caltech.edu/telecourse.html)
- [ ] [Reinforcement Learning: An Introduction (2nd Edition)](https://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.html)
- [GitHub repository](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction)
## Kaggle knowledge competitions
- [x] [Titanic Kaggle Challenge](https://www.kaggle.com/omarelgabry/titanic/a-journey-through-titanic)
- [ ] [Kaggle Competitions: How and where to begin?](https://www.analyticsvidhya.com/blog/2015/06/start-journey-kaggle/)
- [ ] [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)
- [ ] [Master Kaggle By Competing Consistently](http://machinelearningmastery.com/master-kaggle-by-competing-consistently/)
## Video Series
- [ ] [Machine Learning for Hackers](https://www.youtube.com/playlist?list=PL2-dafEMk2A4ut2pyv0fSIXqOzXtBGkLj)
- [ ] [Fresh Machine Learning](https://www.youtube.com/playlist?list=PL2-dafEMk2A6Kc7pV6gHH-apBFxwFjKeY)
- [ ] [Machine Learning Recipes with Josh Gordon](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)
- [ ] [Everything You Need to know about Machine Learning in 30 Minutes or Less](https://vimeo.com/43547079)
- [ ] [Nuts and Bolts of Applying Deep Learning - Andrew Ng](https://www.youtube.com/watch?v=F1ka6a13S9I)
- [ ] BigML Webinar
- [Video](https://www.youtube.com/watch?list=PL1bKyu9GtNYHcjGa6ulrvRVcm1lAB8he3&v=W62ehrnOVqo)
- [Resources](https://bigml.com/releases)
- [ ] [mathematicalmonk's Machine Learning tutorials](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA)
- [ ] [Machine learning in Python with scikit-learn](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A)
- [GitHub repository](https://github.com/justmarkham/scikit-learn-videos)
- [Blog](http://blog.kaggle.com/author/kevin-markham/)
- [ ] [My playlist Top YouTube Videos on Machine Learning, Neural Network & Deep Learning](https://www.analyticsvidhya.com/blog/2015/07/top-youtube-videos-machine-learning-neural-network-deep-learning/)
- [ ] [16 New Must Watch Tutorials, Courses on Machine Learning](https://www.analyticsvidhya.com/blog/2016/10/16-new-must-watch-tutorials-courses-on-machine-learning/)
## MOOC
- [ ] [Practical Deep Learning For Coders, Part 1](http://course.fast.ai)
- [ ] [Udacitys Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120)
- [Udacity Intro to Machine Learning Review](http://hamelg.blogspot.com/2014/12/udacity-intro-to-machine-learning-review.html)
- [ ] [Udacitys Supervised, Unsupervised & Reinforcement](https://www.udacity.com/course/machine-learning--ud262)
- [ ] [Machine Learning Foundations: A Case Study Approach](https://www.coursera.org/learn/ml-foundations)
- [ ] [Courseras Machine Learning](https://www.coursera.org/learn/machine-learning)
- [Video only](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW)
- [Coursera Machine Learning review](https://rayli.net/blog/data/coursera-machine-learning-review/)
- [Coursera: Machine Learning Roadmap](https://metacademy.org/roadmaps/cjrd/coursera_ml_supplement)
- [ ] [Machine Learning Distilled](https://code.tutsplus.com/courses/machine-learning-distilled)
- [ ] [BigML training](https://bigml.com/training)
- [ ] [Courseras Neural Networks for Machine Learning](https://www.coursera.org/learn/neural-networks)
- Taught by Geoffrey Hinton, a pioneer in the field of neural networks
- [ ] [Machine Learning-CS-Oxford University](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
- [ ] [Creative Applications of Deep Learning with TensorFlow](https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info)
- [ ] [Intro to Descriptive Statistics](https://www.udacity.com/course/intro-to-descriptive-statistics--ud827)
- [ ] [Intro to Inferential Statistics](https://www.udacity.com/course/intro-to-inferential-statistics--ud201)
## Resources
- [ ] [Deep Learning Weekly](www.deeplearningweekly.com/)
- [ ] [Learning Tensor Flow](http://learningtensorflow.com/index.html)
- [ ] [Machine Learning for Developers](https://xyclade.github.io/MachineLearning/)
- [ ] [Machine Learning Advice for Developers](https://dev.to/thealexlavin/machine-learning-advice-for-developers)
- [ ] [Machine Learning For Complete Beginners](http://pythonforengineers.com/machine-learning-for-complete-beginners/)
- [ ] [Machine Learning Self-study Resources](https://ragle.sanukcode.net/articles/machine-learning-self-study-resources/)
- [ ] [Level-Up Your Machine Learning](https://metacademy.org/roadmaps/cjrd/level-up-your-ml)
- [ ] Enough Machine Learning to Make Hacker News Readable Again
- [Video](https://www.youtube.com/watch?v=O7IezJT9uSI)
- [Slide](https://speakerdeck.com/pycon2014/enough-machine-learning-to-make-hacker-news-readable-again-by-ned-jackson-lovely)
- [ ] [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning)
- Flipboard Topics
- [Machine learning](https://flipboard.com/topic/machinelearning)
- [Deep learning](https://flipboard.com/topic/deeplearning)
- [Artificial Intelligence](https://flipboard.com/topic/artificialintelligence)
- Medium Topics
- [Machine learning](https://medium.com/tag/machine-learning/latest)
- [Deep learning](https://medium.com/tag/deep-learning)
- [Artificial Intelligence](https://medium.com/tag/artificial-intelligence)
- Monthly top 10 articles
- Machine Learning
- [July 2016](https://medium.mybridge.co/top-ten-machine-learning-articles-for-the-past-month-9c1202351144#.lyycen64y)
- [August 2016](https://medium.mybridge.co/machine-learning-top-10-articles-for-the-past-month-2f3cb815ffed#.i9ee7qkjz)
- [September 2016](https://medium.mybridge.co/machine-learning-top-10-in-september-6838169e9ee7#.4jbjcibft)
- Algorithms
- [September 2016](https://medium.mybridge.co/algorithm-top-10-articles-in-september-8a0e6afb0807#.hgjzuyxdb)
- [Comprehensive list of data science resources](http://www.datasciencecentral.com/group/resources/forum/topics/comprehensive-list-of-data-science-resources)
- [Machine Learning Summer Schools](http://mlss.cc/)
- [DigitalMind's Artificial Intelligence resources](http://blog.digitalmind.io/post/artificial-intelligence-resources)
- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)
- [CreativeAi's Machine Learning](http://www.creativeai.net/?cat%5B0%5D=machine-learning)
- [AutoGrad - Gradient - Library](github.com/HIPS/autograd/blob/master/docs/tutorial.md)
## Becoming an Open Source Contributor
- [ ] [tensorflow/magenta: Magenta: Music and Art Generation with Machine Intelligence](https://github.com/tensorflow/magenta)
- [ ] [tensorflow/tensorflow: Computation using data flow graphs for scalable machine learning](https://github.com/tensorflow/tensorflow)
- [ ] [cmusatyalab/openface: Face recognition with deep neural networks.](https://github.com/cmusatyalab/openface)
- [ ] [tensorflow/models/syntaxnet: Neural Models of Syntax.](https://github.com/tensorflow/models/tree/master/syntaxnet)
## Podcasts
- ### Podcasts for Beginners:
- [Talking Machines](http://www.thetalkingmachines.com/)
- [Linear Digressions](http://lineardigressions.com/)
- [Data Skeptic](http://dataskeptic.com/)
- [This Week in Machine Learning & AI](https://twimlai.com/)
- ### "More" advanced podcasts
- [Partially Derivative](http://partiallyderivative.com/)
- [OReilly Data Show](http://radar.oreilly.com/tag/oreilly-data-show-podcast)
- [Not So Standard Deviation](https://soundcloud.com/nssd-podcast)
- ### Podcasts to think outside the box:
- [Data Stories](http://datastori.es/)
## Communities
- Quora
- [Machine Learning](https://www.quora.com/topic/Machine-Learning)
- [Statistics](https://www.quora.com/topic/Statistics-academic-discipline)
- [Data Mining](https://www.quora.com/topic/Data-Mining)
- Reddit
- [Machine Learning](https://www.reddit.com/r/machinelearning)
- [Computer Vision](https://www.reddit.com/r/computervision)
- [Natural Language](https://www.reddit.com/r/languagetechnology)
- [Data Science](https://www.reddit.com/r/datascience)
- [Big Data](https://www.reddit.com/r/bigdata)
- [Statistics](https://www.reddit.com/r/statistics)
- [Data Tau](http://www.datatau.com/)
- [Deep Learning News](http://news.startup.ml/)
- [KDnuggets](http://www.kdnuggets.com/)

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<title>Machine learning for software engineers by ZuzooVn</title>
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<div class="inner">
<h1>Machine learning for software engineers</h1>
<h2>A complete daily plan for studying to become a machine learning engineer.</h2>
<a href="https://github.com/ZuzooVn/machine-learning-for-software-engineers" class="button"><small>View project on</small> GitHub</a>
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<h1>
<a id="top-down-learning-path-machine-learning-for-software-engineers" class="anchor" href="#top-down-learning-path-machine-learning-for-software-engineers" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Top-down learning path: machine learning for software engineers</h1>
<p>Inspired by <a href="https://github.com/jwasham/google-interview-university">Google Interview University</a>.</p>
<p><em>If you like this project, please give me a star.</em></p>
<h2>
<a id="what-is-it" class="anchor" href="#what-is-it" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>What is it?</h2>
<p>This is my multi-month study plan for going from mobile developer (self-taught, no CS degree) to machine learning engineer.</p>
<p>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.
This approach is unconventional because its the top-down and results-first approach designed for software engineers.</p>
<p>Please, feel free to make any contributions you feel will make it better.</p>
<h2>
<a id="start-learning" class="anchor" href="#start-learning" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a><a href="https://github.com/ZuzooVn/machine-learning-for-software-engineers">Start Learning</a>
</h2>
<hr>
<h2>
<a id="why-use-it" class="anchor" href="#why-use-it" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Why use it?</h2>
<p>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 &amp; Statistics at university.
Think about my interest in machine learning:</p>
<ul>
<li>
<a href="https://www.quora.com/Can-I-learn-and-get-a-job-in-Machine-Learning-without-studying-CS-Master-and-PhD">Can I learn and get a job in Machine Learning without studying CS Master and PhD?</a>
<ul>
<li>You can, but it is far more difficult than when I got into the field.</li>
</ul>
</li>
<li>
<a href="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">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?</a>
<ul>
<li>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</li>
</ul>
</li>
<li>
<a href="http://programmers.stackexchange.com/questions/79476/what-skills-are-needed-for-machine-learning-jobs">What skills are needed for machine learning jobs?</a>
<ul>
<li>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.</li>
<li>Statistics, Probability, distributed computing, and Statistics.</li>
</ul>
</li>
</ul>
<p>I find myself in times of trouble.</p>
<p>AFAIK, <a href="http://machinelearningmastery.com/programmers-can-get-into-machine-learning/">There are two sides to machine learning</a>:</p>
<ul>
<li>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. Its the mess of reality.</li>
<li>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.</li>
</ul>
<p>I think the best way for practice-focused methodology is something like <a href="http://machinelearningmastery.com/machine-learning-for-programmers/#comment-358985">'practice — learning — practice'</a>, 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.</p>
<p>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.</p>
<h2>
<a id="how-to-use-it" class="anchor" href="#how-to-use-it" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>How to use it</h2>
<p>Everything below is an outline, and you should tackle the items in order from top to bottom.</p>
<p>I'm using Github's special markdown flavor, including tasks lists to check my progress.</p>
<p>I check each task box at the beginning of a line when I'm done with it. When all sub-items in a block are done,
I put [x] at the top level, meaning the entire block is done. Sorry you have to remove all my [x] markings
to use this the same way. If you search/replace, just replace [x] with [ ].
Sometimes I just put a [x] at top level if I know I've done all the subtasks, to cut down on clutter.</p>
<p>More about Github flavored markdown: <a href="https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown">https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown</a></p>
<h2>
<a id="follow-me" class="anchor" href="#follow-me" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Follow me</h2>
<p>I'm a Vietnamese Software Engineer who are really passionate and want to work in the USA.</p>
<p>How much did I work during this plan? Roughly 4 hours/night after a long, hard day at work.</p>
<p>I'm on the journey. </p>
<p><img src="http://sv1.upsieutoc.com/2016/10/08/331f241c8da44d0c43e9324d55440db6.md.jpg" alt="Nam Vu - Top-down learning path: machine learning for software engineers"></p>
<h2>
<a id="start-learning-1" class="anchor" href="#start-learning-1" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a><a href="https://github.com/ZuzooVn/machine-learning-for-software-engineers">Start Learning</a>
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ul li {
padding-left: 20px;
list-style-position: inside;
list-style: disc;
}
ol li {
padding-left: 3px;
list-style-position: inside;
list-style: decimal;
}
dl dd {
font-style: italic;
font-weight: 100;
}
footer {
padding-top: 20px;
padding-bottom: 30px;
margin-top: 40px;
font-size: 13px;
color: #aaa;
}
footer a {
color: #666;
}
/* MISC */
.clearfix:after {
display: block;
height: 0;
clear: both;
visibility: hidden;
content: '.';
}
.clearfix {display: inline-block;}
* html .clearfix {height: 1%;}
.clearfix {display: block;}

@ -0,0 +1,881 @@
/*! normalize.css v3.0.2 | MIT License | git.io/normalize */
/**
* 1. Set default font family to sans-serif.
* 2. Prevent iOS text size adjust after orientation change, without disabling
* user zoom.
*/
html {
font-family: sans-serif; /* 1 */
-webkit-text-size-adjust: 100%; /* 2 */
-ms-text-size-adjust: 100%; /* 2 */
}
/**
* Remove default margin.
*/
body {
margin: 0;
}
/* HTML5 display definitions
========================================================================== */
/**
* Correct `block` display not defined for any HTML5 element in IE 8/9.
* Correct `block` display not defined for `details` or `summary` in IE 10/11
* and Firefox.
* Correct `block` display not defined for `main` in IE 11.
*/
article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
display: block;
}
/**
* 1. Correct `inline-block` display not defined in IE 8/9.
* 2. Normalize vertical alignment of `progress` in Chrome, Firefox, and Opera.
*/
audio,
canvas,
progress,
video {
display: inline-block; /* 1 */
vertical-align: baseline; /* 2 */
}
/**
* Prevent modern browsers from displaying `audio` without controls.
* Remove excess height in iOS 5 devices.
*/
audio:not([controls]) {
display: none;
height: 0;
}
/**
* Address `[hidden]` styling not present in IE 8/9/10.
* Hide the `template` element in IE 8/9/11, Safari, and Firefox < 22.
*/
[hidden],
template {
display: none;
}
/* Links
========================================================================== */
/**
* Remove the gray background color from active links in IE 10.
*/
a {
background-color: transparent;
}
/**
* Improve readability when focused and also mouse hovered in all browsers.
*/
a:active,
a:hover {
outline: 0;
}
/* Text-level semantics
========================================================================== */
/**
* Address styling not present in IE 8/9/10/11, Safari, and Chrome.
*/
abbr[title] {
border-bottom: 1px dotted;
}
/**
* Address style set to `bolder` in Firefox 4+, Safari, and Chrome.
*/
b,
strong {
font-weight: bold;
}
/**
* Address styling not present in Safari and Chrome.
*/
dfn {
font-style: italic;
}
/**
* Address variable `h1` font-size and margin within `section` and `article`
* contexts in Firefox 4+, Safari, and Chrome.
*/
h1 {
margin: 0.67em 0;
font-size: 2em;
}
/**
* Address styling not present in IE 8/9.
*/
mark {
color: #000;
background: #ff0;
}
/**
* Address inconsistent and variable font size in all browsers.
*/
small {
font-size: 80%;
}
/**
* Prevent `sub` and `sup` affecting `line-height` in all browsers.
*/
sub,
sup {
position: relative;
font-size: 75%;
line-height: 0;
vertical-align: baseline;
}
sup {
top: -0.5em;
}
sub {
bottom: -0.25em;
}
/* Embedded content
========================================================================== */
/**
* Remove border when inside `a` element in IE 8/9/10.
*/
img {
border: 0;
}
/**
* Correct overflow not hidden in IE 9/10/11.
*/
svg:not(:root) {
overflow: hidden;
}
/* Grouping content
========================================================================== */
/**
* Address margin not present in IE 8/9 and Safari.
*/
figure {
margin: 1em 40px;
}
/**
* Address differences between Firefox and other browsers.
*/
hr {
height: 0;
-moz-box-sizing: content-box;
box-sizing: content-box;
}
/**
* Contain overflow in all browsers.
*/
pre {
overflow: auto;
}
/**
* Address odd `em`-unit font size rendering in all browsers.
*/
code,
kbd,
pre,
samp {
font-family: monospace, monospace;
font-size: 1em;
}
/* Forms
========================================================================== */
/**
* Known limitation: by default, Chrome and Safari on OS X allow very limited
* styling of `select`, unless a `border` property is set.
*/
/**
* 1. Correct color not being inherited.
* Known issue: affects color of disabled elements.
* 2. Correct font properties not being inherited.
* 3. Address margins set differently in Firefox 4+, Safari, and Chrome.
*/
button,
input,
optgroup,
select,
textarea {
margin: 0; /* 3 */
font: inherit; /* 2 */
color: inherit; /* 1 */
}
/**
* Address `overflow` set to `hidden` in IE 8/9/10/11.
*/
button {
overflow: visible;
}
/**
* Address inconsistent `text-transform` inheritance for `button` and `select`.
* All other form control elements do not inherit `text-transform` values.
* Correct `button` style inheritance in Firefox, IE 8/9/10/11, and Opera.
* Correct `select` style inheritance in Firefox.
*/
button,
select {
text-transform: none;
}
/**
* 1. Avoid the WebKit bug in Android 4.0.* where (2) destroys native `audio`
* and `video` controls.
* 2. Correct inability to style clickable `input` types in iOS.
* 3. Improve usability and consistency of cursor style between image-type
* `input` and others.
*/
button,
html input[type="button"], /* 1 */
input[type="reset"],
input[type="submit"] {
-webkit-appearance: button; /* 2 */
cursor: pointer; /* 3 */
}
/**
* Re-set default cursor for disabled elements.
*/
button[disabled],
html input[disabled] {
cursor: default;
}
/**
* Remove inner padding and border in Firefox 4+.
*/
button::-moz-focus-inner,
input::-moz-focus-inner {
padding: 0;
border: 0;
}
/**
* Address Firefox 4+ setting `line-height` on `input` using `!important` in
* the UA stylesheet.
*/
input {
line-height: normal;
}
/**
* It's recommended that you don't attempt to style these elements.
* Firefox's implementation doesn't respect box-sizing, padding, or width.
*
* 1. Address box sizing set to `content-box` in IE 8/9/10.
* 2. Remove excess padding in IE 8/9/10.
*/
input[type="checkbox"],
input[type="radio"] {
box-sizing: border-box; /* 1 */
padding: 0; /* 2 */
}
/**
* Fix the cursor style for Chrome's increment/decrement buttons. For certain
* `font-size` values of the `input`, it causes the cursor style of the
* decrement button to change from `default` to `text`.
*/
input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
height: auto;
}
/**
* 1. Address `appearance` set to `searchfield` in Safari and Chrome.
* 2. Address `box-sizing` set to `border-box` in Safari and Chrome
* (include `-moz` to future-proof).
*/
input[type="search"] {
-webkit-box-sizing: content-box; /* 2 */
-moz-box-sizing: content-box;
box-sizing: content-box;
-webkit-appearance: textfield; /* 1 */
}
/**
* Remove inner padding and search cancel button in Safari and Chrome on OS X.
* Safari (but not Chrome) clips the cancel button when the search input has
* padding (and `textfield` appearance).
*/
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
-webkit-appearance: none;
}
/**
* Define consistent border, margin, and padding.
*/
fieldset {
padding: 0.35em 0.625em 0.75em;
margin: 0 2px;
border: 1px solid #c0c0c0;
}
/**
* 1. Correct `color` not being inherited in IE 8/9/10/11.
* 2. Remove padding so people aren't caught out if they zero out fieldsets.
*/
legend {
padding: 0; /* 2 */
border: 0; /* 1 */
}
/**
* Remove default vertical scrollbar in IE 8/9/10/11.
*/
textarea {
overflow: auto;
}
/**
* Don't inherit the `font-weight` (applied by a rule above).
* NOTE: the default cannot safely be changed in Chrome and Safari on OS X.
*/
optgroup {
font-weight: bold;
}
/* Tables
========================================================================== */
/**
* Remove most spacing between table cells.
*/
table {
border-spacing: 0;
border-collapse: collapse;
}
td,
th {
padding: 0;
}
/* LAYOUT STYLES */
body {
font-family: 'Helvetica Neue', Helvetica, Arial, serif;
font-size: 15px;
font-weight: 400;
line-height: 1.5;
color: #666;
background: #fafafa url(../images/body-bg.jpg) 0 0 repeat;
}
p {
margin-top: 0;
}
a {
color: #2879d0;
}
a:hover {
color: #2268b2;
}
header {
padding-top: 40px;
padding-bottom: 40px;
font-family: 'Architects Daughter', 'Helvetica Neue', Helvetica, Arial, serif;
background: #2e7bcf url(../images/header-bg.jpg) 0 0 repeat-x;
border-bottom: solid 1px #275da1;
}
header h1 {
width: 540px;
margin-top: 0;
margin-bottom: 0.2em;
font-size: 72px;
font-weight: normal;
line-height: 1;
color: #fff;
letter-spacing: -1px;
}
header h2 {
width: 540px;
margin-top: 0;
margin-bottom: 0;
font-size: 26px;
font-weight: normal;
line-height: 1.3;
color: #9ddcff;
letter-spacing: 0;
}
.inner {
position: relative;
width: 940px;
margin: 0 auto;
}
#content-wrapper {
padding-top: 30px;
border-top: solid 1px #fff;
}
#main-content {
float: left;
width: 690px;
}
#main-content img {
max-width: 100%;
}
aside#sidebar {
float: right;
width: 200px;
min-height: 504px;
padding-left: 20px;
font-size: 12px;
line-height: 1.3;
background: transparent url(../images/sidebar-bg.jpg) 0 0 no-repeat;
}
aside#sidebar p.repo-owner,
aside#sidebar p.repo-owner a {
font-weight: bold;
}
#downloads {
margin-bottom: 40px;
}
a.button {
width: 134px;
height: 58px;
padding-top: 22px;
padding-left: 68px;
font-family: 'Architects Daughter', 'Helvetica Neue', Helvetica, Arial, serif;
font-size: 23px;
line-height: 1.2;
color: #fff;
}
a.button small {
display: block;
font-size: 11px;
}
header a.button {
position: absolute;
top: 0;
right: 0;
background: transparent url(../images/github-button.png) 0 0 no-repeat;
}
aside a.button {
display: block;
width: 138px;
padding-left: 64px;
margin-bottom: 20px;
font-size: 21px;
background: transparent url(../images/download-button.png) 0 0 no-repeat;
}
code, pre {
margin-bottom: 30px;
font-family: Monaco, "Bitstream Vera Sans Mono", "Lucida Console", Terminal, monospace;
font-size: 13px;
color: #222;
}
code {
padding: 0 3px;
background-color: #f2f8fc;
border: solid 1px #dbe7f3;
}
pre {
padding: 20px;
overflow: auto;
text-shadow: none;
background: #fff;
border: solid 1px #f2f2f2;
}
pre code {
padding: 0;
color: #2879d0;
background-color: #fff;
border: none;
}
ul, ol, dl {
margin-bottom: 20px;
}
/* COMMON STYLES */
hr {
height: 0;
margin-top: 1em;
margin-bottom: 1em;
border: 0;
border-top: solid 1px #ddd;
}
table {
width: 100%;
border: 1px solid #ebebeb;
}
th {
font-weight: 500;
}
td {
font-weight: 300;
text-align: center;
border: 1px solid #ebebeb;
}
form {
padding: 20px;
background: #f2f2f2;
}
/* GENERAL ELEMENT TYPE STYLES */
#main-content h1 {
margin-top: 0;
margin-bottom: 0;
font-family: 'Architects Daughter', 'Helvetica Neue', Helvetica, Arial, serif;
font-size: 2.8em;
font-weight: normal;
color: #474747;
text-indent: 6px;
letter-spacing: -1px;
}
#main-content h1:before {
padding-right: 0.3em;
margin-left: -0.9em;
color: #9ddcff;
content: "/";
}
#main-content h2 {
margin-bottom: 8px;
font-family: 'Architects Daughter', 'Helvetica Neue', Helvetica, Arial, serif;
font-size: 22px;
font-weight: bold;
color: #474747;
text-indent: 4px;
}
#main-content h2:before {
padding-right: 0.3em;
margin-left: -1.5em;
content: "//";
color: #9ddcff;
}
#main-content h3 {
margin-top: 24px;
margin-bottom: 8px;
font-family: 'Architects Daughter', 'Helvetica Neue', Helvetica, Arial, serif;
font-size: 18px;
font-weight: bold;
color: #474747;
text-indent: 3px;
}
#main-content h3:before {
padding-right: 0.3em;
margin-left: -2em;
content: "///";
color: #9ddcff;
}
#main-content h4 {
margin-bottom: 8px;
font-family: 'Architects Daughter', 'Helvetica Neue', Helvetica, Arial, serif;
font-size: 15px;
font-weight: bold;
color: #474747;
text-indent: 3px;
}
h4:before {
padding-right: 0.3em;
margin-left: -2.8em;
content: "////";
color: #9ddcff;
}
#main-content h5 {
margin-bottom: 8px;
font-family: 'Architects Daughter', 'Helvetica Neue', Helvetica, Arial, serif;
font-size: 14px;
color: #474747;
text-indent: 3px;
}
h5:before {
padding-right: 0.3em;
margin-left: -3.2em;
content: "/////";
color: #9ddcff;
}
#main-content h6 {
margin-bottom: 8px;
font-family: 'Architects Daughter', 'Helvetica Neue', Helvetica, Arial, serif;
font-size: .8em;
color: #474747;
text-indent: 3px;
}
h6:before {
padding-right: 0.3em;
margin-left: -3.7em;
content: "//////";
color: #9ddcff;
}
p {
margin-bottom: 20px;
}
a {
text-decoration: none;
}
p a {
font-weight: 400;
}
blockquote {
padding: 0 0 0 30px;
margin-bottom: 20px;
font-size: 1.6em;
border-left: 10px solid #e9e9e9;
}
ul {
list-style-position: inside;
list-style: disc;
padding-left: 20px;
}
ol {
list-style-position: inside;
list-style: decimal;
padding-left: 3px;
}
dl dd {
font-style: italic;
font-weight: 100;
}
footer {
padding-top: 20px;
padding-bottom: 30px;
margin-top: 40px;
font-size: 13px;
color: #aaa;
background: transparent url('../images/hr.png') 0 0 no-repeat;
}
footer a {
color: #666;
}
footer a:hover {
color: #444;
}
/* MISC */
.clearfix:after {
display: block;
height: 0;
clear: both;
visibility: hidden;
content: '.';
}
.clearfix {display: inline-block;}
* html .clearfix {height: 1%;}
.clearfix {display: block;}
/* #Media Queries
================================================== */
/* Smaller than standard 960 (devices and browsers) */
@media only screen and (max-width: 959px) { }
/* Tablet Portrait size to standard 960 (devices and browsers) */
@media only screen and (min-width: 768px) and (max-width: 959px) {
.inner {
width: 740px;
}
header h1, header h2 {
width: 340px;
}
header h1 {
font-size: 60px;
}
header h2 {
font-size: 30px;
}
#main-content {
width: 490px;
}
#main-content h1:before,
#main-content h2:before,
#main-content h3:before,
#main-content h4:before,
#main-content h5:before,
#main-content h6:before {
padding-right: 0;
margin-left: 0;
content: none;
}
}
/* All Mobile Sizes (devices and browser) */
@media only screen and (max-width: 767px) {
.inner {
width: 93%;
}
header {
padding: 20px 0;
}
header .inner {
position: relative;
}
header h1, header h2 {
width: 100%;
}
header h1 {
font-size: 48px;
}
header h2 {
font-size: 24px;
}
header a.button {
position: relative;
display: inline-block;
width: auto;
height: auto;
padding: 5px 10px;
margin-top: 15px;
font-size: 13px;
line-height: 1;
color: #2879d0;
text-align: center;
background-color: #9ddcff;
background-image: none;
border-radius: 5px;
-moz-border-radius: 5px;
-webkit-border-radius: 5px;
}
header a.button small {
display: inline;
font-size: 13px;
}
#main-content,
aside#sidebar {
float: none;
width: 100% ! important;
}
aside#sidebar {
min-height: 0;
padding: 20px 0;
margin-top: 20px;
background-image: none;
border-top: solid 1px #ddd;
}
aside#sidebar a.button {
display: none;
}
#main-content h1:before,
#main-content h2:before,
#main-content h3:before,
#main-content h4:before,
#main-content h5:before,
#main-content h6:before {
padding-right: 0;
margin-left: 0;
content: none;
}
}
/* Mobile Landscape Size to Tablet Portrait (devices and browsers) */
@media only screen and (min-width: 480px) and (max-width: 767px) { }
/* Mobile Portrait Size to Mobile Landscape Size (devices and browsers) */
@media only screen and (max-width: 479px) { }
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