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Updated in [February 21st, 2023]

Unlock the Exciting World of Learning! Here's What Awaits You:

This course will teach you how to build a fully connected neural network with Keras, the most basic type of neural network. You will learn how to configure and evaluate the accuracy of the network, save the model, and use it to make predictions. You will also learn how to expose the model as part of a web application that can be used to make predictions.

This course is perfect for those who want to learn the fundamentals of neural networks and how to use them in Python applications. You don't need to know a lot of Python for this course, but some basic knowledge will be helpful. By the end of the course, you will have a better understanding of how neural networks work and how to use them to make predictions.

[Applications]
After completing this course, students should be able to apply the knowledge they have gained to create fully connected neural networks with Keras. They should be able to build and configure the network, evaluate and test the accuracy of each, save the model, and use it to make predictions in the future. Additionally, they should be able to expose the model as part of a web application that can be used to make predictions.

[Career Paths]
1. Machine Learning Engineer: Machine learning engineers are responsible for developing and deploying machine learning models. They use a variety of tools and techniques to build, train, and deploy models that can be used to solve real-world problems. They must have a strong understanding of mathematics, statistics, and computer science, as well as a good grasp of the latest machine learning technologies. The demand for machine learning engineers is growing rapidly, and salaries are increasing accordingly.

2. Data Scientist: Data scientists are responsible for analyzing large datasets and extracting meaningful insights from them. They use a variety of tools and techniques to uncover patterns and trends in data, and then use those insights to inform decisions. Data scientists must have a strong understanding of mathematics, statistics, and computer science, as well as a good grasp of the latest data science technologies. The demand for data scientists is growing rapidly, and salaries are increasing accordingly.

3. Artificial Intelligence Engineer: Artificial intelligence engineers are responsible for developing and deploying AI-based systems. They use a variety of tools and techniques to build, train, and deploy AI-based systems that can be used to solve real-world problems. They must have a strong understanding of mathematics, statistics, and computer science, as well as a good grasp of the latest AI technologies. The demand for AI engineers is growing rapidly, and salaries are increasing accordingly.

4. Deep Learning Engineer: Deep learning engineers are responsible for developing and deploying deep learning models. They use a variety of tools and techniques to build, train, and deploy models that can be used to solve real-world problems. They must have a strong understanding of mathematics, statistics, and computer science, as well as a good grasp of the latest deep learning technologies. The demand for deep learning engineers is growing rapidly, and salaries are increasing accordingly.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path focuses on the fundamentals of computer science, such as programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and neural networks. This degree is becoming increasingly popular as the demand for data scientists and machine learning engineers grows.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of artificial intelligence systems and their applications. It covers topics such as natural language processing, computer vision, robotics, and machine learning. This degree is becoming increasingly popular as the demand for AI engineers grows.

3. Master of Science in Data Science: This degree path focuses on the analysis and interpretation of large datasets. It covers topics such as data mining, machine learning, and statistical analysis. This degree is becoming increasingly popular as the demand for data scientists grows.

4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of machine learning algorithms and their applications. It covers topics such as deep learning, reinforcement learning, and natural language processing. This degree is becoming increasingly popular as the demand for machine learning experts grows.