I recently graduated with a bachelor’s degree in Civil Engineering and was all set to start with a Master’s degree in Transportation Engineering this fall. Unfortunately, my plans got pushed to the winter term because of COVID-19. So as of January this year, I have had no school and no work.

我最近获得了土木工程学士学位，并准备在今年秋天开始获得运输工程硕士学位。 不幸的是，由于COVID-19，我的计划被推迟到了冬季。 因此，从今年1月起，我没有上学也没有工作。

While looking at some of the research going on in my future grad school, I came across Machine Learning and Deep Learning being implemented in a lot of Transportation Engineering related research projects. At the time, I had no clue what ML, DL, or even Data Science as a whole was! So, I started looking into the subject. I talked to a few of my friends who are Computer and Software Engineers and what I understood from them was that Machine Learning is rooted in Statistics, Calculus, and Linear Algebra, all of which are some of my favorite math topics. I remember thinking to myself that I cannot let this opportunity go by, I was in a position where I had all the time in the world and unlimited resources (thanks to the internet!). In addition to that, I was going to enter a field that is being transformed by Data Science very rapidly and I needed to dip my toes into it.

在查看我未来的研究生学校正在进行的一些研究时，我发现机器学习和深度学习已在许多与运输工程相关的研究项目中实施。 当时，我不知道什么是ML，DL甚至整个数据科学！ 因此，我开始研究该主题。 我与一些计算机和软件工程师的朋友交谈过，我从他们那里了解到，机器学习植根于统计，微积分和线性代数，所有这些都是我最喜欢的数学主题。 我记得自己以为自己不能放过这个机会，当时我处于世界上所有时间无穷无尽的资源(感谢互联网！)中。 除此之外，我打算快速进入一个由Data Science转变的领域，我需要全神贯注。

I have always struggled with programming languages, I once started to learn Java but gave it up within 5 hours of starting. It was quite embarrassing as I had high hopes of developing android apps. Now that I think about it, I was just being lazy and impatient. But this time was different, I had to learn Python which is easier to grasp than Java and I found myself truly fascinated by this new field.

我一直在努力学习编程语言，我曾经开始学习Java，但是在开始学习后的5个小时内就放弃了。 由于我对开发android应用程序寄予厚望，这非常令人尴尬。 现在，我开始思考，我只是懒惰而急躁。 但是这次不一样，我不得不学习比Java更容易掌握的Python，我发现自己对这个新领域非常着迷。

So, let’s jump right in! In this article, I will take you through the journey of how I went from being a complete newbie to a Google Certified TensorFlow Developer in less than 5 months.

所以，让我们跳进去吧！ 在本文中，我将带领您完成我如何 在不到5个月的时间内从完全的新手变成了Google认证的TensorFlow开发人员。

1.学习Python (1. Learn Python)

There are a lot of resources available to learn Python from, both free tutorials as well as paid courses which give you a certificate for completion. I personally chose a certificate course as that provided me with a tangible form of credibility and kept me accountable. Coming from a non-coding background this was important to me. Here are some of the resources available;

免费教程和付费课程(可为您提供结业证书)提供了大量学习Python的资源。 我个人选择了证书课程，因为它为我提供了切实的信誉形式，并让我负责。 来自非编码背景，这对我很重要。 以下是一些可用资源；

CERTIFICATE COURSES

证书课程

• This is the course I took to learn Python. I recommend this to anyone who does not have a coding background as this course covers all the fundamentals and is constantly updated. The best part is that you get lifetime access to all the materials and a certificate to prove that you have completed the entire course.这是我学习Python的课程。 我推荐给没有编码背景的任何人，因为本课程涵盖了所有基础知识并且会不断更新。 最好的部分是您可以终生使用所有材料和证书，以证明您已完成整个课程。

Link: Python Bootcamps: Learn Python Programming and Code Training

链接： Python训练营：学习Python编程和代码培训

• This is another wonderful specialization offered by the University of Michigan on coursera.com. It consists of 4 different courses covering a wide range of topics starting from writing your first “Hello World!” code to working with databases. You can either buy it to get the certificate or audit it for free.

  这是密歇根大学在coursera.com上提供的另一个出色的专业。 从撰写第一个“ Hello World！”开始，它包含4个不同的课程，涵盖了广泛的主题。 使用数据库的代码。 您可以购买该证书以获得证书，也可以免费对其进行审核。

Link: Python for Everybody

链接： 适用于所有人的Python

List of more courses here

此处有更多课程列表

FREE TUTORIALS

免费教学

• The wonderful people at freeCodeCamp.org regularly post quality coding tutorials on YouTube.

  freeCodeCamp.org的精彩人士定期在YouTube上发布高质量的编码教程。

Link: Learn Python — Full Course for Beginners [Tutorial]

链接： 学习Python —初学者完整课程[教程]

• Another great tutorial video on Python另一个关于Python的精彩教程视频

Link: Python Tutorial — Python for Beginners [Full Course]

链接： Python教程—面向初学者的Python [完整课程]

Alright, now you have learnt the basics of Python, great job and congratulations!!

好了，现在您已经学习了Python的基础知识，出色的工作和恭喜您！！

But, do not expect yourself to be an expert, just because you have a certificate or you sat through a 5 hour long tutorial video. The work is far from done! It is going to be a gradual process and there are some great tools to help you practice and improve your coding skills.

但是，不要仅仅因为拥有证书或坐在5个小时的教程视频中就期望自己成为专家。 工作还远远没有完成！ 这将是一个循序渐进的过程，其中有些很棒 帮助您练习和提高编码技能的工具。

I used two websites,

我使用了两个网站

• Codewars.com is just amazing! they have figured out a way to gamify the process of practicing to code. DO CHECK IT OUT

  Codewars.com真是太神奇了！ 他们找到了一种方法，可以将练习编码的过程进行游戏化。 检查一下

Link: Codewars: Achieve mastery through challenge

链接： Codewars：通过挑战实现精通

• Leetcode.com is another great website. They have coding interview style questions in order of increasing difficulty and is another wonderful place to practice and improve your coding skills.

  Leetcode.com是另一个很棒的网站。 他们有按难度递增的编码面试风格问题，是练习和提高编码技能的另一个好地方。

I would recommend to keep practicing and polishing up your Python skills on the side at regular intervals. Remember: spaced repetition works!

我建议您定期定期练习和完善您的Python技能。 请记住：间隔重复有效！

2.学习机器学习理论 (2. Learn Machine Learning Theory)

As I mentioned before, Machine Learning is rooted in Statistics, Calculus and Linear Algebra and hence you do not need to be able to code in order to understand and learn Machine Learning concepts. The Machine Learning course on coursera.com taught by Andrew Ng is an absolute gem in my opinion. The course is old and uses Matlab over Python but the way the concepts are introduced and explained is very relevant, and will prep you well. For a complete newbie like myself, it was extremely frustrating at times but I am grateful now that I completed the course even though I didn’t fully understand some of the topics at the time. I find myself referring to material in the course all the time even while preparing for the TensorFlow Developer Exam.

如前所述，机器学习植根于统计，微积分和线性代数，因此您无需为了理解和学习机器学习概念而进行编码。 在我看来， Coursera.com上的机器学习课程由Andrew Ng教授，绝对是一门瑰宝。 该课程虽然很老，并且在Python上使用Matlab，但是引入和解释概念的方式非常相关，可以为您做好准备。 对于像我这样的完整新手，有时会感到非常沮丧，但是即使我当时还不完全了解某些主题，我也很高兴能完成本课程。 即使在准备TensorFlow开发人员考试时，我也总是在参考课程中的内容。

Link: Coursera | Online Courses & Credentials From Top Educators. Join for Free | Coursera

链接： Coursera | 来自顶尖教育家的在线课程和资格证书。 免费加入| Coursera

Note: This course can be taken even before learning Python, but I would recommend learning Python first so that you can practice it while learning Machine Learning concepts.

注意：本课程甚至可以在学习Python之前进行，但是我建议您首先学习Python，以便您可以在学习机器学习概念时进行实践。

3.学习数据科学图书馆 (3. Learn Data Science Libraries)

There are specific libraries within Python that make Data Science related tasks much simpler and efficient. Some of these libraries are Pandas (data manipulation and analysis), Numpy (support for multi-dimensional arrays and matrices), Matplotlib (plotting) and Scikitlearn (creating ML models). There are countless resources available online, here are some of the ones I used -

Python中有特定的库，这些库使与数据科学相关的任务更加简单和高效。 这些库中有一些是Pandas(数据处理和分析)，Numpy(支持多维数组和矩阵)，Matplotlib(绘图)和Scikitlearn(创建ML模型)。 网上有无数可用资源，以下是我使用过的一些资源-

• Pandas — I went over a lot of videos, tutorials and even audited a certificate course. This YouTube playlist by codebasics is hands down one the best resources on the internet.熊猫-我浏览了许多视频，教程，甚至审核了证书课程。 这个基于codebasics的YouTube播放列表是互联网上最好的资源之一。

Link: Pandas Tutorial (Data Analysis In Python)

链接： Pandas教程(Python中的数据分析)

• Numpy — As usual freeCodeCamp.org for the win!!

  Numpy —像往常一样免费获胜！

Link: Python NumPy Tutorial for Beginners

链接： Python NumPy初学者教程

• Matplotlib — This particular playlist on YouTube is easy to follow and explains tricky topics very well.Matplotlib-YouTube上的此特定播放列表易于遵循，并且很好地解释了棘手的主题。

Link: Matplotlib Tutorial Series — Graphing in Python

链接： Matplotlib教程系列— Python图形

• Scikitlearn — I took a course offered on udemy.com which covered almost all ML model and explained it’s implementation using real real world data sets. There is also a free 3 hour long tutorial on YouTube which can be found here.

  Scikitlearn-我参加了udemy.com上提供的一门课程，该课程涵盖了几乎所有的ML模型，并解释了它是使用真实世界的数据集实现的。 YouTube上还有一个3小时免费的教程，可以在这里找到。

Link: Machine Learning A-Z (Python & R in Data Science Course)

链接： 机器学习AZ(数据科学课程中的Python和R)

4.深度学习理论 (4. Deep Learning Theory)

At this point, it is safe to say that you know most of the things you need to know to become a successful Data Scientist, except Deep Learning. DL is complicated enough that it requires a separate course and even a separate library to implement it. The Machine Learning course by Andrew Ng on Coursera mentions Neural Networks and Deep Learning but there is much more to it than covered in that particular course and so Andrew Ng made another course that just goes deeper into Neural Networks and Deep Learning. Deeplearning.ai created a specialization which consists of 5 courses that cover different topics such as Convolutional Neural Networks, Hyperparameter Tuning, Sequence Models and more. This course focuses on the theory and the under-the-hood working of different Neural Network models. I believe that completing this course is essential even though you don’t absolutely need to in order to successfully build and deploy deep learning models. However, it makes your life much simpler when you have to tune a model or create a model from scratch in TensorFlow.

在这一点上，可以肯定地说，除了深度学习之外，您已经了解成为一名成功的数据科学家所需的大多数知识。 DL非常复杂，因此需要单独的课程，甚至需要单独的库来实现。 吴安德(Andrew Ng)在Coursera上的机器学习课程提到了神经网络和深度学习，但是它所涉及的内容远远超出了该特定课程，因此吴安德(Andrew Ng)开设了另一门课程，它更深入地介绍了神经网络和深度学习。 Deeplearning.ai创建了一个由5门课程组成的专业课程，涵盖了不同主题，例如卷积神经网络，超参数调整，序列模型等。 本课程侧重于不同神经网络模型的理论和后台工作。 我相信，即使您并非一定要成功构建和部署深度学习模型，也必须完成本课程。 但是，当您必须在TensorFlow中调整模型或从头开始创建模型时，它会使您的工作变得更加简单。

Link: Deep Learning Specialization

链接： 深度学习专业化

Note: This course can be audited for free.

注意：本课程可以免费审核。

I wrote an article which explains the inner workings of a Deep Neural Network by practical implementation and can be found here

我写了一篇文章，通过实际实现解释了深度神经网络的内部工作原理，可以在这里找到

5. TensorFlow (5. TensorFlow)

TensorFlow is a free and open-source software library used for machine learning applications such as neural networks. Other similar libraries are PyTorch and Theano, but I decided to go forward with TensorFlow as it is supposedly much better for production models and scalability especially since Keras is now completely integrated into TensorFlow. The people at Deeplearning.ai have released another specialization on Coursera which is a continuation of the Deep Learning course mentioned above. It uses the concepts taught in the DL course and implements them using TensorFlow. Another reason I chose to take the TensorFlow in Practice Specialization was that it covered all the pre-requisites required for the TensorFlow Developer Certification Exam by Google.

TensorFlow是一个免费的开源软件库，用于机器学习应用程序(例如神经网络)。 其他类似的库是PyTorch和Theano，但我决定继续使用TensorFlow，因为它对于生产模型和可伸缩性据称要好得多，尤其是因为Keras现在已完全集成到TensorFlow中。 Deeplearning.ai上的人员已经发布了Coursera上的另一个专业化课程，这是上述深度学习课程的延续。 它使用DL课程中讲授的概念，并使用TensorFlow实施它们。 我选择参加TensorFlow实践专业化的另一个原因是，它涵盖了Google TensorFlow开发人员认证考试的所有先决条件。

Link: TensorFlow in Practice

链接： TensorFlow实践

DO I NEED TO TAKE THE TensorFlow DEVELOPERS EXAM?

我需要参加TensorFlow开发人员考试吗？

This particular certificate exam is fairly new, about 5 months old at this point in time, and thus it is not certain right now how valuable it is going to be in the industry with regards to improving job prospects. Coming from a non-coding background this exam acted as a way to validate my skills in Deep Learning.

这项特殊的证书考试还很新，大约五个月之久，因此目前尚不确定它在改善工作前景方面在行业中有多有价值。 来自非编码背景，此考试是一种验证我的深度学习技能的方法。

This exam needs a certain level of preparation and coding ability to be able to even attempt it successfully. More than anything else it gave me a set-in-stone goal and acted as motivation to get through all the courses.

该考试需要一定水平的准备和编码能力，才能成功尝试。 最重要的是，它给了我一个坚定的目标，并成为我完成所有课程的动力。

Here is a great article talking about the exam in detail.

这是一篇很棒的文章，详细讨论了考试。

These are the steps I followed and I intend to continue learning. There is still a lot more to learn especially since Data Science is such a rapidly developing field.

这些是我遵循的步骤，我打算继续学习。 特别是因为数据科学是一个发展Swift的领域，所以还有很多东西要学习。