This series of articles aims to provide machine learning knowledge to beginners, avoiding jargon and complex concepts for better understanding and application.
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machine learning
Here is a brief introduction to machine learning: it utilizes real-world or generated data and automatically discovers patterns and patterns in it to enable prediction of future situations.
Machine Learning (ML), an important subfield of Artificial Intelligence, focuses on the use of specific algorithms to discover meaningful information and reveal hidden patterns from sensory data to support rational decision-making processes.
The learning process involves several key elements, starting with understanding the key vocabulary: the training dataset, the model, the training method, and the tuning process.
Typically, you need an initial model. This initial model can be any pre-existing model, either one that someone else has already trained, or a simple model with nothing in it.
You then need access to various historical datasets to train your model. Just as people may not all choose to run when choosing how to train, it's vital to find the right training method.
Then, if there are still problems during the training process, the model needs to be gradually tuned and then continue training. Training the model is not a quick fix, so the key is to find a method that fits the current scenario.
Getting Started
Learning Python is essential, and mastering the basics is key. You don't need to dive into too many frameworks, and mastering the basic syntax is enough.
Learning and JavaScript is a must, as subsequent courses involve some web application development and these skills are critical.
It is very important to be familiar with Scikit-learn, which is an open source machine learning library that supports both supervised and unsupervised learning. In addition to this, Scikit-learn provides a variety of useful features such as model fitting, data preprocessing, model selection, model evaluation, and more.
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Here, we'll be using the CloudStudio platform for learning, so you don't need to worry too much about setting up and configuring your learning environment.
Key Steps in Machine Learning
- Raising issues for decision-making: In machine learning, decision problems typically involve learning patterns and regularities from data and then making predictions or decisions based on the learned knowledge. For example, spam filter, suppose we want to build a spam filter, which is a typical classification problem. We have a large dataset of emails, and each email has many characteristics, such as sender, subject, body content, etc. For each email, we need to decide whether it is spam or normal email.
- Data collection and preparation: In order to be able to answer your question, you need data. Collect a large amount of labeled email data, both spam and normal emails. At this point the data should be divided into a dataset dedicated to training and a portion for testing.
- Training methods: Choose a suitable machine learning model, such as a plain Bayesian classifier or a support vector machine, and train the model using a prepared dataset.
- Model Evaluation: Use another portion of the data (the test set) to evaluate the performance of the model, e.g., metrics such as accuracy, precision, recall, etc.
- parameterization: This process involves the gradual adjustment of variables and parameters in conjunction with the performance of the model to achieve the ultimate goal of optimizing model performance.
- anticipate: Finally, we have the option of deploying our model to a real-world application in order to make data predictions and validate their accuracy.
summarize
In this series of articles, we try to introduce the basic concepts and key steps of machine learning to beginners in a concise and easy-to-understand way, avoiding jargons and complex theories, and aiming to help readers better understand and apply this technology.
Machine Learning, a key component of Artificial Intelligence, automatically discovers patterns and regularities in data through algorithms that enable us to make predictions and decisions. We highlight important key terms and steps in the learning process, including training datasets, models, training methods, and tuning processes.
To help readers take their learning a step further, I recommend a number of quality resources and tools, such as the CloudStudio platform and the Scikit-learn library, that can facilitate and support learning and practice.