Gain Expertise in TensorFlow with this Coursera Program

Visist Tallam

09 June 2023

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Course Overview

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning is an online, self-paced certification course developed by Coursera. This course teaches best practices for using TensorFlow, a popular open-source machine learning framework. It is part of the upcoming Machine Learning in Tensorflow Specialization course.

Andrew Ng's Machine Learning course and Deep Learning Specialization teaches fundamental Machine Learning and Deep Learning principles. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to put those principles into practice, allowing you to begin building and applying scalable models to real-world problems.

The course was informative and was taught by Laurence Moroney.

"With its extensive coverage of the various nuances of TensorFlow, this course improves one’s chances of cracking the TensorFlow certification exam and that of landing a job."

- Visist Tallam

Course Structure

It is an intermediate level course that takes approximately 18 hours to complete. In this 4-week course, you will:

Use TensorFlow to build a single-layer neural network for fitting linear models
Analyze housing price predictions that come from a single-layer neural network
Build a multilayer neural network for classifying the Fashion Modified National Institute Of Standards And Technology Dataset (MNIST) image dataset
Execute image preprocessing with the Keras ImageDataGenerator functionality

We can initially analyze housing price predictions from a single-layer neural network and use TensorFlow to build a single-layer neural network for fitting linear models. The curriculum helps learn how to:

Use callback functions for tracking model loss and accuracy during training and making predictions on how the layer size affects network predictions and training speed
Build a multilayer neural network for classifying the Fashion MNIST image dataset
Test the effect of adding convolution and MaxPooling to the neural network for classifying Fashion MNIST images on classification accuracy
Execute image preprocessing with the Keras ImageDataGenerator functionality
Carry out real-life image classification by leveraging a multilayer neural network for binary classification

The notes are mostly provided in the discussion forum and enable us to attempt assessments.

Insider Tips

To get the best out of this course, , I have included some important tips that you might find useful.

Use Flashcards

Create flashcards based on the study material provided. For example, take some tool like MNIST, gather information on it, and create one flashcard.Flashcards are a powerful tool for learning and retaining information. They are a set of small cards, typically made of cardboard or paper, that contain brief information or questions on one side and the corresponding answers on the other side.

Prerequisites

Python coding experience and high school-level math are required to do this course. Prior knowledge of machine learning or deep learning is advantageous but not necessary.

Assessments

The assignments namely,

Housing Prices.
Implementing Callbacks in TensorFlow using the MNIST Dataset
Improve MNIST with convolutions
Handling Complex Images

enable us to implement the concepts learnt.

Final Take

I am currently pursuing a data science degree at IIT Madras. Doing this course helped me crack the TensorFlow certification through various programming assignments. I got this course as a reference from my Bootcamp mentor.

After completing the course, the prospects of cracking the TensorFlow certification and landing a good job improves considerably.