Tuesday, October 12, 2021

Coursera's IBM AI Engineering Professional Certificate Review [2021] - Is it worth it?

 Artificial intelligence nowadays is revolutionized almost every industry, from youtube recommendations detecting fraudulent transactions in banks and showing ads in your Facebook feed. Companies need qualified artificial intelligence engineers to stay competitive in this industry and make a better user experience. According to glassdor.com, the average salary for an AI engineer is more than $118k a year.

There are a lot of programming languages used in artificial intelligence. Still, python, without a doubt, is the best choice for you to start your career in this industry. 


You also need to learn some framework written in python and has some built-in algorithms to perform these actions like Tensorflow developed by google and Pytorch developed by Facebook and used by Tesla in their self-driving cars.

Many courses are available online to learn these skills, but there is one created by experts I recommend to you, which is the IBM AI Engineering Professional Certificate specialization.



1. The Instructors Review

This specialization is created by experts working in the IBM company. Most of them received a Ph.D. in their expertise like software engineers or data scientists. All of them are created the program from their experience over the years, and that’s why I’ve recommended this course over hundred other available courses created by normal people.

Coursera's IBM AI Engineering Professional Certificate Review [2021]



2. The Course Content

Now let's which courses are part of this popular IBM AI Engineer Professional certificate and what topics are covered, how they are structure, and what are you going to learn in these courses. 

2.1. Machine Learning with Python

Starting the course by overviewing the machine learning concepts such as the differences between supervised & unsupervised learning and how to use the algorithms. Next, you will learn about the different regression models and apply them to the lab. 

Later, you will learn about the classification and algorithms, such as KNN, then move to cluster and create a recommendation system.




2.2. Introduction to Deep Learning & Neural Networks with Keras

You will learn about the deep learning models and how they mimic the human’s brains to perform their functions. Next, you will learn how neural networks work in real life and learn from the data and its concepts like backpropagation. 

Later you will see the different available deep learning frameworks and build a simple deep learning model using Keras. Finally, build a convolutional neural network (CNN) using Keras and a recurrent neural network (RNN).




2.3. Introduction to Computer Vision and Image Processing

You will learn about the computer vision field and its applications like diagnosing diseases. Next, you will use the pillow and OpenCV libraries to start working with images and perform some actions like pixel transformation. Later, you will use the different machine learning algorithms to classify images such as KNN and support vector machine and building a model to classify images using deep learning and object detection.




2.4. Deep Neural Networks with PyTorch

This section will introduce you to the PyTorch library, widely used among companies to create deep neural networks. You will start by learning the tensors of PyTorch and how they work, then move to create a linear regression model and understand its concepts like the loss function and how to optimize your model in PyTorch, and how to create a multiple linear regression model. Finally, deep more into the CNN models and create one using PyTorch.



2.5. Building Deep Learning Models with TensorFlow

Tensorflow is also a good framework for deep learning, and you will learn in this section what and how to use TensorFlow to create deep learning models. Next, you move into supervised learning, classify the mnist dataset, learn about the recurrent neural network, and apply it to language modeling. Later, you will understand what is unsupervised learning is and what restricted Boltzmann machines and apply them to create a recommendation system and learn the autoencoder.



2.6. AI Capstone Project with Deep Learning

This last section in the specialization will require you to complete first all the previous courses and will ask you to use what you’ve learned to solve the real problem following the steps which is loading the data first and prepare the data using PyTorch and build a linear classifier using this framework and also build an image classifier using some pre-trained models.



Conclusion

You can take this course if you want to have a career as an AI engineer, and you will learn three of the most used deep learning frameworks, which are Keras, TensorFlow, and PyTorch, so you can then pick one of these frameworks and learn in-depth about all of its capabilities and algorithms.


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