Jan 28

8 min read

Review — Is TensorFlow Developer Professional Certificate by DeepLearning.ai on Coursera Worth it?

Are you looking for a TensorFlow certificate on Coursera? This TensorFlow Developer Professional Certificate by DeepLearning.ai is definitely worth looking

Hello guys, if you are learning TensorFlow for AI and Machine Learning and looking for best resources to learn TensorFlow in depth then you have come to the right place.

Earlier, I have shared best TensorFlow courses for beginners and in this article, I am going to review, one of the top TensorFlow Course from Coursera, DeepLearning.AI TensorFlow Developer Professional Certificate. This is one of the popular and well structure Coursera course to learn TensorFlow and earn a certificate and we will review this to find out whether its really worth it or not.

Nowadays, Artificial intelligence is dominating every industry and almost every device we use, from IoT to phones. If you are curious about this science and machine learning, data science, then you probably heard of python languages and their AI frameworks like PyTorch Keras and Tensorflow.

Most people use TensorFlow to build machine learning and deep learning models for object detection, object recognition, language translation, and more. Google also uses it to develop RankBrain, which is responsible for the search process in Google.

Tensorflow is probably the most popular framework for building artificial intelligence software and was developed by Google and is available for people to use even for commercial purposes.

There are many courses online to learn this framework. Still, I recommend one called DeepLearning.AI TensorFlow Developer Professional Certificate because it is easy for beginners to understand and create by experts.

Is DeepLearning.AI TensorFlow Developer Professional Certificate on Coursera Worth it?

So far, we have looked that how TensorFlow certification can help you to outshine your competition and learn TensorFlow in depth. Now let’s deep dive into this course to find out more.

We will review this course on three parameters, instructor quality, course content and public opinion. These are my three pillars to find good course and It almost always work.

1. The Instructors Review

This course’s instructor is Laurence Moroney, one of the most popular Coursera instructor for AI And Machine Learning. Lawrence have taught more than 318k learners in the Coursera platform with 15 courses.

Laurence Moroney also leads AI Advocacy at Google, with a vision to make AI easy for developers and to widen access to Machine Learning careers for everyone.

Laurence Moroney has also written many books about programming and machine learning, and artificial intelligence. he most recent being ‘AI and ML for Coders’ at O’Reilly, which is also one of the most recommended book to learn AI and Machine Learning.

In short, you are in good hands as you will learn TensorFlow from an expert who really knows what is talking about and have tested the battlefield himself.

2. Course Content and Structure

Now that you know that you will learn from a Google AI expert, its time to find out what exactly are you going to learn? which topics are covered and in what depth.

If you know Coursera Professional certificate are collection of courses and normally each covers one topic in depth and this TensorFlow Specialization is no exception.

There are 4 courses in this course to learn TensorFlow in depth.

2.1. Introduction to TensorFlow for Artificial Intelligence

You will start this section by understanding machine learning and deep learning and how they offer a new programming paradigm by learning from the data without explicitly being programmed every time to solve a problem.

Next, you will learn how to use deep learning and machine learning to solve computer vision problems with just a few lines of code and code a computer vision neural network.

You’ve seen how to create a basic neural network for computer vision, but it wasn’t that accurate. You will now learn how to optimize this neural network and implement pooling and convolutional layers. Finally, learn about how to deal with larger datasets and use the image Generator function.

Here is the link to join this course — ntroduction to TensorFlow for Artificial Intelligence

2.2. Convolutional Neural Networks in TensorFlow

After you learned so much about the convolutional neural networks (CNN) and how to use them, you will dive deeper into this CovNet model and how to e its performance, especially when performing classification.

Next, you will learn a technique called augmentation that is used to expand the size of your training dataset, meaning creating other images based on the images you already have.

Later, you will learn about transfer learning, how to use pre-trained models on an extensive dataset to solve your deep learning problems, and how to code your own model using transfer learning. Finally, learn the multi-class classification instead of classifying two objects like cats and dogs in most previous examples.

Here is the link to join this course — Convolutional Neural Networks in TensorFlow

2.3. Natural Language Processing in TensorFlow

Natural language processing (NLP) is the science that lets computers interact with the human language like reading the text, translating the text, talking to humans, measure sentiment, and more and you will start this section by understanding the sentiment in text and the word base encoding and working with tokenization.

Next, you will learn about word embedding, which is a word representation, and the same word that shares the same meaning will have the same representation and apply this concept on the IMBD reviews dataset.

Later, you will need the sequence model that will make the sentiment analysis more accurate. You will implement LSTM models in the code and use convolutional networks. Finally, use what you’ve learned to generate poetry.

Here is the link to join this course — Natural Language Processing in TensorFlow

2.4. Sequences, Time Series, and Prediction

This time you will dive more into the sequences and predictions and see some considerations when working with sequential models like the values that often change like the temperature and make forecasting.

Next, create a deep neural network to recognize and predict time series using a single-layer neural network.

Later, you will use the recurrent neural network (RNN) and long-short term memory (LSTM) to classify and predict sequential data. Finally, use what you have learned in this specialization to make a model that predicts the sunspot using real data.

Here is the link to join this course — Sequences, Time Series, and Prediction

3. People’s Review

Talking about social proof, this is one of the most popular Coursera course to learn TensorFlow. More than 146,000 people have already joined this course and out of them 32% people have started a new career after completing this specialization.

It has also got excellent reviews with on average rating of 4.7 from close to 18K participants which is amazing but not surprising given the quality of course and Deeplearning.AI reputation.

This course is really helpful in bridging the gap between theory and implementation part of TensorFlow as its not that easy to learn. Overall a great course to learn TensorFlow online on Coursera.

Here is the link to join this course — DeepLearning.AI TensorFlow Developer Professional Certificate

By the way, If you are planning to join multiple Coursera courses or specializations, then consider taking a Coursera Plus subscription which provides you unlimited access to their most popular courses, specialization, professional certificate, and guided projects.

Coursera Plus | Unlimited Access to 7,000+ Online Courses

Get unlimited access to over 90% of courses, Projects, Specializations, and Professional Certificates on Coursera…

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It costs around $59/ per month but is worth it because you get access to more than 7000+ courses and projects, and you can also get unlimited certificates.

That’s all on this review of TensorFlow Developer Professional Certificate on Coursera. TensorFlow gained too much popularity among companies and researchers since it is open-source.

Hence, all people worldwide contribute to the development and its easiness to be deployed and use on the web. This course can be your start in the journey of learning artificial intelligence using the TensorFlow framework.

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