機(jī)器學(xué)習(xí) 深度學(xué)習(xí) ai

I’m sure you’ve heard of the incredible artificial intelligence applications out there — from programs that can beat the world’s best Go players to self-driving cars.

我敢肯定，您已經(jīng)聽說過令人難以置信的人工智能應(yīng)用程序-從可以擊敗世界上最好的圍棋選手的程序到無人駕駛汽車。

The problem is that most people get caught up on the AI hype, mixing technical discussions with philosophical ones.

問題在于，大多數(shù)人都被AI炒作所吸引，將技術(shù)性討論與哲學(xué)性討論混為一談。

If you’re looking to cut through the AI hype and work with practically implemented data models, train towards a data engineer or machine learning engineer position.

如果您希望消除AI的炒作并使用實(shí)際實(shí)現(xiàn)的數(shù)據(jù)模型，請(qǐng)朝著數(shù)據(jù)工程師或機(jī)器學(xué)習(xí)工程師的方向培訓(xùn)。

Don’t look for interesting AI applications within AI articles. Look for them in data engineering or machine learning tutorials.

不要在AI文章中尋找有趣的AI應(yīng)用程序。 在數(shù)據(jù)工程或機(jī)器學(xué)習(xí)教程中查找它們。

These are the steps I took to build this fun little scraper I built to analyze gender diversity in different coding bootcamps. It’s the path I took to do research for Springboard’s new AI/ML online bootcamp with job guarantee.

這些是我為構(gòu)建這個(gè)有趣的小刮板而采取的步驟， 該刮板是為分析不同編碼訓(xùn)練營中的性別多樣性而構(gòu)建的 。 這就是我為具有工作保障的 Springboard 新AI / ML在線訓(xùn)練營進(jìn)行研究的途徑。

Here’s a step-by-step guide to getting into the machine learning space with a critical set of resources attached to each one.

這是進(jìn)入機(jī)器學(xué)習(xí)領(lǐng)域的分步指南，每個(gè)領(lǐng)域都有一組關(guān)鍵資源。

1.開始梳理您的Python和軟件開發(fā)實(shí)踐 (1. Start brushing up on your Python and software development practices)

You’ll want to start off by embracing Python, the language of choice for most machine learning engineers.

首先，您需要擁抱Python，這是大多數(shù)機(jī)器學(xué)習(xí)工程師的首選語言。

The handy scripting language is the tool of choice for most data engineers and data scientists. Most tools for data have been built in Python or have built API access for easy Python access.

方便的腳本語言是大多數(shù)數(shù)據(jù)工程師和數(shù)據(jù)科學(xué)家的首選工具。 大多數(shù)數(shù)據(jù)工具都是使用Python構(gòu)建的，或者已經(jīng)構(gòu)建了API訪問權(quán)限以方便Python訪問。

Thankfully, Python’s syntax is relatively easy to pick up. The language has tons of documentation and training resources. It also includes support for all sorts of programming paradigms from functional programming to object-oriented programming.

幸運(yùn)的是，Python的語法相對(duì)容易掌握。 該語言具有大量的文檔和培訓(xùn)資源。 它還包括對(duì)從功能編程到面向?qū)ο缶幊痰母鞣N編程范例的支持。

The one thing that might be a bit hard to pick up is the tabbing and spacing required to organize and activate your code. In Python, the whitespace really matters.

可能有點(diǎn)難以理解的一件事是組織和激活代碼所需的制表符和空格。 在Python中，空白確實(shí)很重要。

As a machine learning engineer, you’d be working in a team to build complex, often mission-critical applications. So, now is a good time to refresh on software engineering best practices as well.

作為機(jī)器學(xué)習(xí)工程師，您將在一個(gè)團(tuán)隊(duì)中構(gòu)建復(fù)雜的，通常是關(guān)鍵任務(wù)的應(yīng)用程序。 因此，現(xiàn)在也是刷新軟件工程最佳實(shí)踐的好時(shí)機(jī)。

Learn to use collaborative tools such as Github. Get into the habit of writing thorough unit tests for your code using testing frameworks such as nose. Test your APIs using tools such as Postman. Use CI systems such as Jenkins to make sure your code doesn’t break. Develop good code review skills to work better with your future technical colleagues.

學(xué)習(xí)使用協(xié)作工具，例如Github。 養(yǎng)成使用鼻子等測(cè)試框架為代碼編寫全面的單元測(cè)試的習(xí)慣。 使用Postman等工具測(cè)試您的API。 使用Jenkins等CI系統(tǒng)來確保您的代碼不會(huì)中斷。 培養(yǎng)良好的代碼審查技能，以便與未來的技術(shù)同事更好地合作。

One thing to read: What is the best Python IDE for data science? Take a quick read-through so you can understand what toolset you want to work in to implement Python on datasets.

讀一件事 ： 什么是數(shù)據(jù)科學(xué)最好的Python IDE？ 快速閱讀，以便您了解要在數(shù)據(jù)集上實(shí)現(xiàn)Python的工具集。

I use the Jupyter Notebook myself, since it comes pre-installed with most of the important data science libraries you’ll use. It comes with an easy, clean interactive interface that allows you to edit your code on the fly.

我自己使用Jupyter Notebook ，因?yàn)樗呀?jīng)預(yù)裝了您將要使用的大多數(shù)重要數(shù)據(jù)科學(xué)庫。 它帶有一個(gè)簡(jiǎn)單，干凈的交互式界面，使您可以即時(shí)編輯代碼。

Jupyter Notebook also comes with extensions that allow you to easily share your results with the world at large. The files generated are also super easy to work with on Github.

Jupyter Notebook還帶有擴(kuò)展程序，使您可以輕松地與全世界共享您的結(jié)果。 生成的文件在Github上也非常容易使用。

One thing to do: Pandas Cookbook allows you to fork into live examples of the Pandas framework, one of the most powerful data manipulation libraries. You can quickly work through an example of how to play with a dataset through it.

要做的一件事 ： Pandas Cookbook允許您進(jìn)入Pandas框架的實(shí)時(shí)示例，該框架是功能最強(qiáng)大的數(shù)據(jù)處理庫之一。 您可以快速查看一個(gè)如何通過它處理數(shù)據(jù)集的示例。

2.研究機(jī)器學(xué)習(xí)框架和理論 (2. Look into machine learning frameworks and theory)

Once you’re playing around with Python and practicing with it, it’s time to start looking at machine learning theory.

一旦您開始使用Python并進(jìn)行了實(shí)踐，就該開始研究機(jī)器學(xué)習(xí)理論了。

You’ll learn what algorithms to use. Having a baseline knowledge of the theory behind machine learning will let you implement models with ease.

您將學(xué)習(xí)使用哪些算法。 擁有機(jī)器學(xué)習(xí)背后的理論基礎(chǔ)知識(shí)，可以輕松實(shí)現(xiàn)模型。

One thing to read: A Tour of The Top Ten Algorithms For Machine Learning Newbies will help you get started with the basics. You’ll learn that there isn’t a “free lunch”. There is no algorithm that will give you the optimal result for each setting, so you’ll have to dive into each algorithm.

閱讀一件事 ： 機(jī)器學(xué)習(xí)十大算法新手將幫助您入門基礎(chǔ)知識(shí)。 您會(huì)發(fā)現(xiàn)這里沒有“免費(fèi)午餐”。 沒有一種算法可以為您提供每種設(shè)置的最佳結(jié)果，因此您必須深入研究每種算法。

One thing to do: Play around with the interactive Free Machine Learning in Python Course — develop your Python skills and start implementing algorithms.

一件事要做 ： 在Python課程中體驗(yàn)交互式的免費(fèi)機(jī)器學(xué)習(xí) -開發(fā)您的Python技能并開始實(shí)現(xiàn)算法。

3.開始使用數(shù)據(jù)集并進(jìn)行實(shí)驗(yàn) (3. Start working with datasets and experimenting)

You’ve got the tools and theory under your belt. You should think about doing little mini-projects that can help you refine your skills.

您掌握了工具和理論。 您應(yīng)該考慮做一些小型項(xiàng)目，這些項(xiàng)目可以幫助您提高技能。

One thing to read: Take a look at 19 Free Public Data Sets for Your First Data Science Project and start looking at where you can find different datasets on the web to play around with.

要讀的一件事 ： 為您的第一個(gè)數(shù)據(jù)科學(xué)項(xiàng)目查看19個(gè)免費(fèi)公共數(shù)據(jù)集，然后開始查看可以在網(wǎng)上找到不同數(shù)據(jù)集的地方。

One thing to do: Kaggle Datasets will let you work with lots of publicly available datasets. What’s cool about this collection is you can see how popular certain datasets are. You can also see what other projects have been built with the same dataset.

要做的一件事 ： Kaggle數(shù)據(jù)集將使您可以處理許多公開可用的數(shù)據(jù)集。 這個(gè)集合的優(yōu)點(diǎn)是您可以看到某些數(shù)據(jù)集的受歡迎程度。 您還可以查看使用相同數(shù)據(jù)集構(gòu)建的其他項(xiàng)目。

4.利用Hadoop或Spark擴(kuò)展數(shù)據(jù)技能 (4. Scale your data skills with Hadoop or Spark)

Now that you’re practicing on smaller datasets, you’ll want to learn how to work with Hadoop or Spark. Data engineers work with streaming, real-time production-level data at the terabyte and sometimes petabyte scale. Skill up by learning your way through a big data framework.

現(xiàn)在，您正在處理較小的數(shù)據(jù)集，您將需要學(xué)習(xí)如何使用Hadoop或Spark。 數(shù)據(jù)工程師使用TB級(jí)(有時(shí)甚至PB級(jí))的流式實(shí)時(shí)生產(chǎn)級(jí)數(shù)據(jù)。 通過學(xué)習(xí)大數(shù)據(jù)框架來掌握技能。

One thing to read: This short article How do Hadoop and Spark Stack Up? will help you walk through both Hadoop and Spark and how they compare and contrast with one another.

閱讀一件事 ：這篇簡(jiǎn)短的文章Hadoop和Spark如何堆疊？ 將幫助您遍歷Hadoop和Spark以及它們?nèi)绾蜗嗷ケ容^。

One thing to do: If you want to start working with a big data framework right away, Spark Jupyter notebooks hosted on Databricks offers a tutorial-level introduction to the framework, and gets you to practice with production-level code examples.

要做的一件事 ：如果您想立即開始使用大數(shù)據(jù)框架， Databricks上托管的Spark Jupyter筆記本會(huì)提供該框架的教程級(jí)介紹，并讓您練習(xí)生產(chǎn)級(jí)代碼示例。

5.使用TensorFlow等深度學(xué)習(xí)框架 (5. Work with a deep learning framework like TensorFlow)

You’re done exploring machine learning algorithms and working with the different big data tools out there.

您已經(jīng)完成了機(jī)器學(xué)習(xí)算法的探索，并可以使用各種不同的大數(shù)據(jù)工具。

Now it’s time to take on the sort of powerful reinforcement learning that has been the focus of new advances. Learn the TensorFlow framework and you’ll be on the cutting edge of machine learning work.

現(xiàn)在是時(shí)候進(jìn)行強(qiáng)大的強(qiáng)化學(xué)習(xí)，而這正是新進(jìn)展的重點(diǎn)。 學(xué)習(xí)TensorFlow框架，您將處在機(jī)器學(xué)習(xí)工作的最前沿。

One thing to read: Read What is TensorFlow? and understand what’s going on below-the-hood when it comes to this powerful deep learning framework.

要閱讀的一件事 ：閱讀什么是TensorFlow？ 并了解有關(guān)此強(qiáng)大的深度學(xué)習(xí)框架的內(nèi)幕。

One thing to do: TensorFlow and Deep Learning without a PhD is an interactive course built by Google that combines theory placed into slides with practical labs with code.

要做的一件事 ： TensorFlow和沒有博士學(xué)位的深度學(xué)習(xí)是Google制作的一門互動(dòng)課程，它將幻燈片中的理論與帶有代碼的實(shí)際實(shí)驗(yàn)室相結(jié)合。

6.開始使用大型生產(chǎn)級(jí)數(shù)據(jù)集 (6. Start working with big production-level datasets)

Now that you’ve worked with deep learning frameworks, you can start working towards large production-level datasets.

既然您已經(jīng)使用了深度學(xué)習(xí)框架，就可以開始處理大型生產(chǎn)級(jí)數(shù)據(jù)集。

As a machine learning engineer, you’ll be making complex engineering decisions on managing large amounts of data and deploying your systems.

作為機(jī)器學(xué)習(xí)工程師，您將在管理大量數(shù)據(jù)和部署系統(tǒng)方面做出復(fù)雜的工程決策。

That would include collecting data from APIs and web scraping, SQL + NoSQL databases and when you’d use them, use of pipeline frameworks such as Luigi or Airflow.

這將包括從API和Web抓取，SQL + NoSQL數(shù)據(jù)庫收集數(shù)據(jù)，以及在使用它們時(shí)使用諸如Luigi或Airflow之類的管道框架。

When you deploy your applications, you might use container-based systems such as Docker for scalability and reliability, and tools such as Flask to create APIs for your application.

部署應(yīng)用程序時(shí)，可以使用基于容器的系統(tǒng)(例如Docker)來實(shí)現(xiàn)可伸縮性和可靠性，并使用工具(例如Flask)來為應(yīng)用程序創(chuàng)建API。

One thing to read: 7 Ways to Handle Large Data Files for Machine Learning is a nice theoretical exercise into how you would handle big datasets, and can serve as a handy checklist of tactics to use.

要讀的一件事 ： 處理機(jī)器學(xué)習(xí)的大數(shù)據(jù)文件的7種方法是一個(gè)很好的理論練習(xí)，介紹了如何處理大數(shù)據(jù)集，并且可以用作方便使用的策略清單。

One thing to do: Publicly Available Big Data Sets is a list of places where you can get very large datasets — ready to practice your newfound data engineering skills on.

要做的一件事 ： 公開可用的大數(shù)據(jù)集是可以獲取非常大的數(shù)據(jù)集的位置的列表-準(zhǔn)備練習(xí)新發(fā)現(xiàn)的數(shù)據(jù)工程技能。

7.練習(xí)，練習(xí)，練習(xí)，建立投資組合然后再工作 (7. Practice, practice, practice, build towards a portfolio and then a job)

Finally, you’ve gotten to a point where you can build working machine learning models. The next step to advance your machine learning career is to find a job with a company that holds those large datasets so you can apply your skills every day to a cutting-edge machine learning problem.

最后，您到了可以構(gòu)建有效的機(jī)器學(xué)習(xí)模型的地步。 推進(jìn)機(jī)器學(xué)習(xí)事業(yè)的下一步是在擁有大量數(shù)據(jù)集的公司中找到工作，以便您每天可以將自己的技能應(yīng)用于前沿的機(jī)器學(xué)習(xí)問題。

One thing to read: 41 Essential Machine Learning Interview Questions (with answers) will help you practice the knowledge you need to ace a machine learning interview.

要讀的一件事 ： 41必備的機(jī)器學(xué)習(xí)面試問題(包括答案??)將幫助您練習(xí)掌握機(jī)器學(xué)習(xí)面試所需的知識(shí)。

One thing to do: Go out and find meetups that are dedicated to machine learning or data engineering on Meetup — it’s a great way to meet peers in the space and potential hiring managers.

要做的一件事 ：出去玩 ，在Meetup上找到專門用于機(jī)器學(xué)習(xí)或數(shù)據(jù)工程的聚會(huì)–這是結(jié)識(shí)空間中的同行和潛在招聘經(jīng)理的好方法。

Hopefully, this tutorial has helped cut through the hype around AI to something practical and tailored that you can use. If you feel like you need a little bit more, the company I work with, Springboard, offers a career track bootcamp dedicated to AI and machine learning with a job guarantee, and 1:1 mentorship from machine learning experts.

希望本教程有助于將圍繞AI的炒作切入您可以使用的實(shí)用且量身定制的內(nèi)容。 如果您覺得需要更多一點(diǎn)，與我合作的公司Springboard會(huì)提供專門針對(duì)AI和機(jī)器學(xué)習(xí)的職業(yè)訓(xùn)練營，并提供工作保證 ，并由機(jī)器學(xué)習(xí)專家提供1：1指導(dǎo)。

翻譯自: https://www.freecodecamp.org/news/how-to-cut-through-the-ai-hype-to-become-a-machine-learning-engineer-b0d2c5e4ae02/

機(jī)器學(xué)習(xí) 深度學(xué)習(xí) ai

總結(jié)

以上是生活随笔為你收集整理的机器学习 深度学习 ai_如何突破AI炒作成为机器学习工程师的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。