Buzz
Machine learning (ML or machine learning) is a realm within algorithms that enables software applications to predict outcomes more accurately without direct programming. The premise of machine learning is to construct algorithms capable of receiving information and utilizing statistical analysis to predict outcomes, while updating results with new data.
1. What is Machine Learning?
Exploring Machine Learning: The Art of AI
There are two primary types of machine learning: prediction and classification. Prediction tasks involve predicting house or car prices, while classification tasks involve handwriting or object recognition, such as identifying objects from images.
2. Basic Concepts in Machine Learning
- Dataset: This is the raw data set, unprocessed, collected by programmers in the initial stage of data collection.
- Data Point (Data Element): A fundamental unit of the Dataset, representing a specific observation. Each data point comprises various attributes or features. Data points are typically represented as rows in a data table, with each row containing one or more data features.
- Training Data and Test Data: Training data is utilized to train machine learning models, while Test data is employed to evaluate the model. Typically, the split ratio between these two data types is 80% for training data and 20% for test data.
- Model: The culmination of the training process on the training data. The model utilizes algorithms to make predictions or decisions based on the knowledge it has acquired from the training data.
Utilizing machine learning aids in predicting and analyzing data with higher accuracy, particularly beneficial when dealing with large datasets. Machine learning helps reduce human effort and errors, with numerous applications in modern life such as:
- Computer Networks: Used in error detection, network security, and optimizing network performance.
3. What are Machine Learning Algorithms?
Machine learning comprises two main types:
- Supervised Learning: In supervised learning, computers learn from labeled data, meaning each input data point (Xi) has a corresponding label (Yi).
- Unsupervised Learning: In unsupervised learning, computers learn from data without labels. Algorithms autonomously discover correlations in the data, create models for the data, or assist in reducing the dimensionality of the data.
Additionally, machine learning is categorized into other types such as:
- Semi-supervised Learning: Combining supervised and unsupervised learning approaches.
- Deep Learning: Involves the use of multi-layer neural networks to tackle complex problems.
- Reinforcement Learning: Computers learn through experimentation and errors, often used in making continuous decisions in a changing environment.
Although machine learning algorithms have been around for a few decades, they only became popular as artificial intelligence (AI) advanced. 'Deep learning' models are prevalent in most advanced AI applications today.
The foundation of machine learning is one of the most competitive fields among technology companies like Amazon, Google, Microsoft, IBM, etc. These companies are racing to capture customers for platform services covering various machine learning activities, such as data collection, data preprocessing, model building, application deployment, and training. As the importance of machine learning and AI in business establishment and management continues to grow, the platform machine learning battleground will become increasingly fierce.
In-depth research into deep learning and AI will focus on developing common applications. Today's AI models demand deep expertise to produce the most optimal algorithms. However, scientists are discovering methods to make models more flexible, capable of applying knowledge learned from one model to another in the future. After reading this article on Mytour, we hope readers will gain valuable insights into this algorithmic domain and effectively apply it in their work.
