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Updated in [March 06th, 2023]

This course, Deep Learning for Business, focuses on future business strategy based on DL and ML technology, including details on new cutting-edge products/services and open source DL software, which are future enablers. NN (Neural Network), CNN (Convolutional NN), and RNN (Recurrent NN) systems are among the core technologies of DL and ML systems. The course also focuses on four TensorFlow Playground projects where experience in designing DL NNs can be gained using the TensorFlow Playground, an easy and fun yet very powerful application. This course is designed to assist learners in developing business strategies and conducting technical planning for new DL and ML services and products.

[Applications]
The application of this course can be seen in the development of business strategies and technical planning for new DL and ML services and products. It provides an understanding of the core technologies of DL and ML systems, such as NN (Neural Network), CNN (Convolutional NN), and RNN (Recurrent NN). Additionally, it provides an opportunity to gain experience in designing DL NNs through four TensorFlow Playground projects. This course is beneficial for those looking to gain a better understanding of the potential of DL and ML technology for future business strategies.

[Career Paths]
1. Data Scientist: Data Scientists are responsible for analyzing large datasets to uncover trends and insights that can be used to inform business decisions. They use a variety of tools and techniques, including machine learning, deep learning, and natural language processing, to uncover patterns and insights from data. Data Scientists are in high demand as businesses increasingly rely on data-driven decision making.

2. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They use a variety of tools and techniques, including deep learning, to build and deploy models that can be used to make predictions and automate tasks. Machine Learning Engineers are in high demand as businesses increasingly rely on automated systems to make decisions.

3. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for developing and deploying AI systems. They use a variety of tools and techniques, including deep learning, to build and deploy AI systems that can be used to automate tasks and make decisions. AI Engineers are in high demand as businesses increasingly rely on AI systems to make decisions.

4. Business Intelligence Analyst: Business Intelligence Analysts are responsible for analyzing data to uncover trends and insights that can be used to inform business decisions. They use a variety of tools and techniques, including machine learning, deep learning, and natural language processing, to uncover patterns and insights from data. Business Intelligence Analysts are in high demand as businesses increasingly rely on data-driven decision making.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path focuses on the fundamentals of computer science, including programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and deep learning. With the increasing demand for AI and ML professionals, this degree path is becoming increasingly popular.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of AI systems, including deep learning, natural language processing, and computer vision. It also covers topics such as robotics, machine learning, and data mining. This degree path is becoming increasingly popular as AI technology is becoming more widely used in various industries.

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 deep learning. This degree path is becoming increasingly popular as data science is becoming more widely used in various industries.

4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of machine learning algorithms and systems. It covers topics such as deep learning, natural language processing, and computer vision. This degree path is becoming increasingly popular as machine learning is becoming more widely used in various industries.

Course Syllabus

Deep Learning Products & Services

Business with Deep Learning & Machine Learning

Deep Learning Computing Systems & Software

Basics of Deep Learning Neural Networks

Deep Learning with CNN & RNN

Deep Learning Project with TensorFlow Playground