Intro to Machine Learning with TensorFlow Nanodegree Program

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Introducing the Intro to Machine Learning with TensorFlow Nanodegree Program

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The field of machine learning continues to boast incredible job growth, salaries, and skill sets that can be used in many different industries. Google utilizes this technology in their Cloud product to allow startups to build machine learning models that work on data of any size, while GE utilizes IoT to help detect and prevent anomalies and crashes in their products. These are just a snapshot of the numerous applications of machine learning in the market today that display the potential for an exponential amount of professional expansion. Currently, just in the US alone, there are over 50,000 open roles for machine learning professionals, so now is the time to develop machine learning expertise!

In LinkedIn’s 2020 Emerging Jobs report, AI Specialist, a role that includes machine learning, deep learning, TensorFlow, and Python as key skills, boasts 74% annual growth. All of the above skills are incorporated into Udacity’s new Intro to Machine Learning with TensorFlow Nanodegree program, which is a great way to get introduced to the fundamentals of machine learning, including areas like manipulating data, supervised & unsupervised learning, and deep learning.

So what is TensorFlow, and how is it being utilized today? TensorFlow is a deep learning framework made by Google for creating machine learning (ML) models that use multi-layer neural networks. The TensorFlow library allows users to perform functions by creating computational graphs. AirBnB utilizes TensorFlow to improve the guest experience to categorize listing photos by classifying images and detecting objects at scale. Coca-Cola uses TensorFlow to enable mobile proof-of-purchase at scale, while PayPal uses TensorFlow to detect fraud, and Twitter uses TensorFlow to rank tweets, highlighting the broad and powerful range of applications.

Intro to Machine Learning with TensorFlow Nanodegree Program

Deep Learning – TensorFlow vs. PyTorch

In the area of deep learning, there are different frameworks that machine learning engineers may use to help build, train, and deploy their models. As such, Udacity is now releasing the second of two versions of the Intro to Machine Learning Nanodegree – one with the PyTorch deep learning framework, and the other with the TensorFlow deep learning framework. Both Nanodegree programs begin with the scikit-learn machine learning library, before pivoting to either PyTorch or TensorFlow in the Deep Learning sections. 

>> Learn More about Intro to Machine Learning with TensorFlow

>> Learn More about Intro to Machine Learning with PyTorch

While they each have their own different syntax, they are both popular frameworks used by many developers around the world. TensorFlow, created by Google, has been around slightly longer – V1 came out in early 2017, while PyTorch V1, created by Facebook, was first released in 2018. Both have continued to be iterated on to be more efficient, gain more features, and become even easier to use. TensorFlow 2.0, that just released in late 2019, will be used in this version of the Nanodegree program. 

So, how do you choose between the two frameworks? There are a number of items to consider here. First, if you know the framework used by the company you either work for or want to work for, you should go that route. Second, consider the developer communities for each. TensorFlow’s community is currently bigger, but you may want to check out what different developers are working on in each and choose accordingly. Lastly, you may even just check out a demo or two of each and check out some of the syntax and documentation – is one more intuitive for you? PyTorch originally seemed a little more native to Python, while TensorFlow has been closing that gap both through further Keras integration (a higher-level library built on top of TensorFlow, simplifying its API) and the further improvements made in TensorFlow 2.0. 

Learning one deep learning framework can help you work with others faster as well – you’ll be searching for “How do I do this in the other framework?” instead of the bigger challenge of “How do I do this at all?” 

Key Projects in Intro to Machine Learning with TensorFlow

In addition to learning from experienced machine learning professionals, you will also get the opportunity to apply your skills to hands-on projects that will display your expertise to employers. Real-world projects allow you to apply your new skills on projects that are relevant to the key industries that are hiring for professionals with your machine learning experience.

Project 1  – Find Donors for CharityML

To expand their potential donor base, CharityML has decided to send letters to residents of California, but to only those most likely to donate to the charity. You will evaluate and optimize several different supervised learning algorithms to determine which algorithm will provide the highest donation yield while operating under specific marketing constraints

Project 2 – Create an Image Classifier

You will implement an image classification application using a deep neural network. This application will train a deep learning model on a dataset of images and then use the trained model to classify new images. 

Project 3 – Creating Customer Segments

In this project, you will apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data. You will first clean the customer spending data, apply PCA transformations to the data, and then implement clustering algorithms to segment the transformed customer data. Finally, you will compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes.

Machine Learning Success Stories

To demonstrate the career growth possible in the field of machine learning, hear from some of our Intro to Machine Learning with PyTorch Nanodegree program graduates to see what you can achieve with a Udacity Nanodegree, and see what you will be able to do with the Intro to Machine Learning with TensorFlow Nanodegree program.

“Because of this program I can code and understand Machine Learning Algorithms properly, and the best part is I can even revise all the concepts on the go. I landed up in my dream internship abroad in Taipei and I am doing well there, all thanks to Udacity.”  – Ekansh Gaykwad

“Today I work as a machine learning engineer, it’s like a dream come true. At the start of the career achieving something like this boosted my passion to set higher career goals for myself. Thanks to Udacity for making me believe and providing such support on all fronts.” – Omkar Sahasrabudhe

“I started applying for jobs after graduating from the Intro to Machine Learning with PyTorch Nanodegree program and was soon contacted by a medical device company. I wasn’t sure if I had the necessary experience, but the company seemed to really value the skills I’d built, and they offered me a full-time role.” – Jeremy Jordan

Master Machine Learning Today

Sign up for one of the Intro to Machine Learning with Tensorflow Nanodegree programs today to get started on your journey towards becoming a machine learning expert!

>> Learn More about Intro to Machine Learning with TensorFlow

>> Learn More about Intro to Machine Learning with PyTorch

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