In recent years, machine learning has received more and more attention in the field of academic research and practical applications. But building a machine learning model is not a simple matter. It requires a lot of knowledge and skills and rich experience to make the model work in a variety of scenarios. The correct machine learning model should be data-centric and based on an understanding of business problems. And data and machine learning algorithms must be applied to solve problems in order to build a machine learning model that can meet the needs of the project.
When building a machine learning model, we should avoid the following 6 mistakes:
The first stage of any machine learning project is to develop an understanding of business needs. When building a machine learning model, you need a clearly defined strategy. When training a model, obtaining the correct labeled data is another challenge facing developers. This not only helps you get the best results but also makes machine learning models appear more reliable among end-users.
Using unverified unstructured data may cause problems in the operation of the machine learning model. Because unverified data may have errors, such as duplication, data conflicts, lack of classification, etc. Using unverified unstructured data is one of the most common mistakes made by machine learning engineers in AI development. Therefore, before using the data for machine learning training, you need to carefully check the original data set and eliminate unnecessary or irrelevant data to help the AI model perform its functions with higher accuracy.
If the data is insufficient, it will reduce the probability of success of the AI model. Therefore, before starting to build a machine learning model, we need to prepare sufficient training data according to the type of AI model or industry.
If it is deep learning, more qualitative and quantitative data sets are needed to ensure that the model can run with high precision.
The machine learning model is constructed by learning and generalizing training data, and then applying the acquired knowledge to new data that has never been seen before to make predictions and achieve its goals. Therefore, we should avoid reusing the data that has been used to test the model. When testing the function of the AI model, it is very important to use a new data set that has not been used for machine learning training before.
When training a machine learning model, if we repeat it all the time, we will not know whether there are any differences between real-world data and training data, as well as test data and training data, and what methods the organization will take to verify and evaluate the performance of the model. Therefore, developers need to ensure that the AI model learns with the correct strategy. To ensure this, you must regularly check the AI training process and its results to get the best results.
The data used in training the machine learning model may make the model biased due to various factors such as age, gender, orientation, and income level, which can affect the results in some way. Therefore, you need to find out how each individual factor affects the processed data and AI training data by using statistical analysis to minimize this phenomenon.
To succeed in the construction of machine learning models, the most important thing is to be prepared in the early stage, avoid mistakes, and constantly look for improvements and better ways to meet the evolving business needs of the organization.
Machine Learning (ML) in simple terms can be defined as the science of getting computers to act and learn without explicit programming to perform those actions. It has become quite popular in recent years, however, the term itself was coined in 1959 by Arthur Samuel who defined Machine Learning as ‘the field of study that gives computers the ability to learn without being explicitly taught’.
A more recent and formal definition of Machine Learning was created by Tom Mitchell and describes it as a well-defined learning problem – ‘A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.’
Machine Learning usage has become so common that you probably use it many times a day without even knowing it. For example, when you search on Google or Bing or any other search engine, in the background there is a learning algorithm that has learned how to rank pages based on user queries. Similarly, when you see the photodetection feature on different social media applications or see spam filter filtering out bogus/unwanted emails in your mailbox, behind the scene is a Machine learning algorithm that learns and detect faces or spams emails respectively. A more recent technology use case is the advent of self-driving cars.
Machine learning is a multidisciplinary interdisciplinary major, covering probability theory, statistics, approximate theory, and complex algorithms. It uses computers as a tool and is committed to simulating human learning in real-time, and divides the existing content into knowledge structures to effectively improve learning efficiency.
Over the past few decades, there have been many types of machine learning methods published in research and publications, and there can be multiple classification methods based on the emphasis on different aspects.
The wave of artificial intelligence is sweeping the world, and many words linger in our ears all the time: artificial intelligence(AI), machine learning, and deep learning. Many people always seem to understand the meaning of these high-frequency words and the relationship behind them.
In order to better understand artificial intelligence(AI), this article explains the meaning of these words in the simplest language and clarifies the relationship between them, hoping to be helpful to people who are just getting started.
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