Here are 5 essential skills you need for machine learning!

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24 Jan 2023
43

Machine learning, it sounds cool. This name can't help but think that it is like a row of robots sitting and learning knowledge, but it is actually a high-end technology that allows robots to learn like humans, so that they can find useful things from data efficiently and quickly. Cloud computing services support various functions for building and deploying AI and machine learning applications. In addition to professionalism, IT team members must learn specific machine learning and other aspects of knowledge, and they must also understand the available cloud computing tools that currently support their team's plans.

1. Data Engineering

The idea of ​​machine learning is actually very simple, it is to simulate a process of human learning, and the most important thing in this process is data! When an IT professional wants to implement all types of AI strategies on the cloud, the premise is to understand data engineering. Data engineering includes a series of skills, which are related to the field of workflow development and data management, as well as software architecture knowledge.

IT professional knowledge can be decomposed to people in different fields to complete different tasks. For example, data sorting generally involves data source identification, data quality evaluation, data extraction and data integration, as well as channel development for these actions in the production environment.

Data-related tasks are usually the highest priority, and engineers should be accustomed to using various databases. Python is a universal programming language, and there are many ways to use it. Even if you are not a professional Python programmer, you need to have some language knowledge to improve your skills from various open source tools related to data engineering and machine learning.

2. Model building

Machine learning is a subject with a bright future . Research and development of machine learning algorithms can develop your career. The IT team builds the model by using the data provided by the engineer, then creating the software, giving suggestions, estimating the value, and categorizing items. The key is to clearly understand the basic knowledge of machine learning technology, even if the process of building many models is done automatically in the cloud.

If you are a model builder, you need to understand the data and goals. You need to think about the solution before the problem arises and know how to integrate it with the existing system.

3. Algorithmic fairness

Different models have different algorithms. Although it seems simple, there are actually many pitfalls. Decisions made by algorithms directly affect individuals. Some people have trained biased algorithms with their short-sightedness, and there are many tools for testing and removing bias on the Internet. Detecting deviations in the model probably requires accurate statistics and machine learning skills, which is an inevitable problem for AI and machine learning models.

4. Understand pseudo code

A good way to learn how to write machine learning algorithms is to better understand and recognize pseudo-code, which will also help train our logical thinking. Then we have to understand how to calculate them, and there are many different indices that can be converted to each other. Normally, the pseudo code is very clear.

How to turn pseudo code into a programming language you are interested in, learning it is the most important thing. In fact, there is no one-take-all neural network, so when you learn how to write neural networks from various teachers, you must pay attention, they will not all use the same number of inputs, hidden layer nodes, etc., maybe even the terminology is different . Although the pseudo-code ignores the details to a certain extent, it can let you clarify your thinking in the early stage.

5. Field knowledge

Every industry has its own knowledge system, so related industries also need to study and understand it, especially when building algorithmic decision-making tools. The data used to train machine learning models limits them, but engineers with domain knowledge can understand where to apply AI and evaluate its effectiveness.

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