Human beings are the epitome of perfection among all species. It is because we have our fully functioning nervous system that makes every other species inferior. Although, can anyone else reach such levels of intelligence? What about robots? Scientists are currently working on awarding human-level intelligence to machines. The basis of all these depends on artificial intelligence and machine learning.
These studies aim to use human-level intelligence to build new methods for predictive analysis. Deep learning is another form of machine learning, and it has many implementations in the modern world. As a data scientist, you can gain maximum career benefits from a deep learning specialization. Here are three of Coursera’s best courses for you.
Deep Learning Specialization
Andrew Ng, Kian Katanforoosh, and Younes Bensouda Mouri
It is one of Coursera’s most popular courses right now. It is in your best interest to opt for this one because it will teach you many skills. It has a 4.9 rating and many outstanding reviews. It is a Coursera specialization that will help you to completely master deep learning. Moreover, you will have to complete your hands-on project. It is mandatory with every specialization course. When we focus on this project, you will explore many things.
This specialization will allow you to learn about deep learning and different types of neural networks. There are plenty of neural networks like,
- Deep learning neural networks
- Vectorized neural networks
- Recurrent neural networks
You will learn about all these types and how to implement them strategically. Moreover, you will get better at machine learning. Because deep learning is an integral part of machine learning, you will learn to reduce errors in machine learning systems. We will start by identifying key architectural parameters. We will apply different algorithms to images and data. The software we are using is TensorFlow. You will learn to build neural networks through it.
Neural network and Deep learning
Andrew NG, Kian Katanforoosh, and Younes Bensouda Mouri
It is an introductory course about neural networks and deep learning, but it is not a beginner-level course. These two are advanced topics, and you must have a basic understanding of programming languages including python. Also, we appreciate algebra and concepts of machine learning. You can grasp new concepts from this course. This course is a part of deep learning specialization, and it will take approx. 23 hours to complete. The rating is 4.9 stars, and more than 100,000 people have taken this course.
We will start with the introduction and history of neural networks. This course will allow you to participate in leading AI technology. It is a pathway to advance your career in this field. You will become familiar with neural network architecture and how to identify it. Furthermore, you will learn backpropagation and advanced python programming.
DeepLearning.AI TensorFlow Developer Professional Certificate
It is the third one from our series today. Coursera doesn’t call it a course, but a professional certificate. The rating is 4.7 stars. TensorFlow is an essential software to work on deep learning projects. Learning TensorFlow and its components will benefit you greatly. This course focuses on the best practices for TensorFlow. How to use it? And how to use its maximum potential? You will learn to build natural language processing systems using TensorFlow. You will get a chance to handle data and explore different strategies. Such scenarios prepare you for the future. You will encounter common problems daily, and you must learn how to tackle them.
It will take almost four months to complete. This course can prepare you for the Google TensorFlow exam as well. It is a part of applied learning projects which encourage practice during learning.
We all know that Coursera provides a certificate of completion, and you can add it to your resume. Many employers prefer people who take extra courses besides their degrees. Deep learning AI has provided the above three courses. They cover different aspects of the same topics. The first one covers the majority, the second introduction, and the third covers the software we need to apply deep learning and neural networks.