Machine learning (ML) is a rapidly growing field that has gained widespread adoption across various industries, from healthcare to finance and beyond. However, for beginners, understanding the terminology used in machine learning can be daunting. In this article, we will provide a beginner's guide to some of the most common machine learning terms used in Epanchin AI.

1. Supervised Learning Supervised learning is a type of machine learning where the model is trained on labeled data. The algorithm learns from the labeled data to predict the outcomes of new data. For example, a supervised learning model can predict the prices of houses based on the features of the house.

Three articles to read on Supervised Learning:

• Understanding Supervised Learning Algorithms
• How to Train a Supervised Learning Model in Epanchin AI
• 5 Applications of Supervised Learning in Business

1. Unsupervised Learning Unsupervised learning is a type of machine learning where the model is trained on unlabeled data. The algorithm learns to identify patterns and relationships within the data without being given specific labels. For example, an unsupervised learning model can group customers based on their purchase history.

Three articles to read on Unsupervised Learning:

• Introduction to Unsupervised Learning Algorithms
• How to Cluster Data in Epanchin AI
• Real-World Applications of Unsupervised Learning

1. Deep Learning Deep learning is a subset of machine learning that uses neural networks to solve complex problems. It involves training a model on a large amount of data and allowing it to learn on its own by adjusting the weights of the neural network. Deep learning is used in many applications, such as image and speech recognition.

Three articles to read on Deep Learning:

• What is Deep Learning and How Does it Work?
• Building a Deep Learning Model in Epanchin AI
• Applications of Deep Learning in Healthcare

1. Data Preprocessing Data preprocessing is the process of cleaning and transforming raw data into a format suitable for machine learning models. It involves tasks such as removing missing values, scaling features, and encoding categorical variables.

Three articles to read on Data Preprocessing:

• Introduction to Data Preprocessing Techniques
• How to Prepare Data for Machine Learning in Epanchin AI
• Common Data Preprocessing Challenges and Solutions

1. Model Evaluation Model evaluation is the process of assessing the performance of a machine learning model. It involves measuring metrics such as accuracy, precision, recall, and F1 score to determine how well the model is performing.

Three articles to read on Model Evaluation:

• Understanding Model Evaluation Metrics
• How to Evaluate a Machine Learning Model in Epanchin AI
• Common Model Evaluation Techniques and Best Practices

In conclusion, this beginner's guide to machine learning terminology provides an overview of some of the most common terms used in Epanchin AI. By understanding these terms, you will be better equipped to use the platform and start building your own machine learning models.