Learning IndustrialIntelligence (IndustrialIntelligence) requires certain skills and knowledge, the following are some of the special preoccupations:

1. Mathematical foundations: Learning IndustrialIntelligence requires a solid mathematical foundation, including linear algebra, probability theory, statistics, calculus and so on. This mathematical knowledge is critical to understanding and implementing various algorithms.

2. Programming skills: proficiency in at least one programming language (e.g., Python, R, Java, etc.) and understanding of related libraries and frameworks (e.g., TensorFlow, Keras, Scikit-learn, etc.).

3. Machine Learning and Deep Learning: Learning Industrial Intelligence requires an in-depth understanding of Machine Learning and Deep Learning, including methods such as Supervised Learning, Unsupervised Learning, Reinforcement Learning, and commonly used neural network architectures (e.g. Convolutional Neural Networks, Recurrent Neural Networks, etc.).

4. Data processing and analysis: familiar with data processing and analysis methods, including data cleaning, feature engineering, data visualization, etc.. At the same time, you need to understand commonly used database technologies (e.g. SQL, NoSQL, etc.) and big data processing frameworks (e.g. Hadoop, Spark, etc.).

5. Sensors and Actuators: Understanding different types of sensors (e.g., temperature sensors, pressure sensors, etc.) and actuators (e.g., motors, servo systems, etc.) and how to integrate them with industrial intelligence systems.

6. Communication Technology: Familiarize yourself with industrial communication protocols and technologies, such as Modbus, Profibus, EtherCAT, etc., in order to achieve data exchange and communication between devices.

7. Security and Reliability: When learning industrial intelligence, you need to consider the security and reliability of the system, including data security, device security, system stability and other aspects.

8. Industry knowledge: to understand the relevant knowledge of the industry, such as manufacturing processes, types of equipment, process requirements, etc., in order to better apply industrial intelligence technology to solve problems.

9. Practical experience: Accumulate practical project experience and participate in actual industrial intelligence projects to continuously learn and improve your skills.

10. Continuous learning: the field of industrial intelligence is rapidly updating technology, you need to maintain a continuous learning attitude, pay attention to industry trends, learn new technologies and methods.