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What is Machine Learning?
Machine learning is a variation on artificial intelligence (AI) technique that allows computers to acquire knowledge without manually programming them in their system. Machine learning algorithms are trained on data, and they use this data to make predictions or decisions.

Machine learning has applications across many areas, such as:
Predictive analytics: Machine learning can be used to predict future events, such as customer churn or product demand.
Fraud detection: Machine learning technologies and algorithms can be utilized to identify bogus transactions in the banking and retail sectors.
Risk management: Machine learning can be used to assess risk, such as the risk of a loan defaulting.
Medical diagnosis: Machine learning can be used to diagnose diseases.
Self-driving cars: Machine learning is used to help self-driving cars navigate the road safely.

There are numerous types of machine learning techniques, such as:
Supervised learning: In the context of supervised learning, an approach used by making the algorithm have training on previously annotated or labeled data. For example, the algorithm might be trained on a dataset of images that have been labeled as «cat» or «dog.»
Unsupervised learning: In unsupervised learning, the algorithm is trained on data that has not been labeled. For example, the algorithm might be trained on a dataset of images without any labels.
Reinforcement learning: In reinforcement learning, the algorithm learns by trial and error. The algorithm is given a reward for taking actions that lead to desired outcomes.
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Semi-supervised machine learning is a hybrid approach that combines elements of both supervised and unsupervised learning. It utilizes a small amount of labeled data along with a larger amount of unlabeled data during the training process. This method is particularly useful in scenarios where acquiring labeled data is expensive or time-consuming, while unlabeled data is abundant.

Key Concepts
Labeled vs. Unlabeled Data:

Labeled Data: Data that has been annotated with the correct output. For example, in a spam detection task, emails marked as «spam» or «not spam» are labeled.
Unlabeled Data: Data without any annotations. For instance, a collection of emails that haven't been classified.
Benefits of Semi-Supervised Learning:

Efficiency: Reduces the need for large labeled datasets, which can save time and resources.
Improved Performance: By leveraging both labeled and unlabeled data, models can often achieve better performance than using only labeled data.
Better Generalization: The model can learn the underlying structure of the data more effectively, which helps in generalizing to unseen examples.
Common Techniques:

Self-training: The model is trained on the labeled data first, then it makes predictions on the unlabeled data. The most confident predictions are added to the training set for further training.
Co-training: Two or more models are trained simultaneously on different views of the data, helping to label unlabeled examples and improve each other’s performance.
Graph-based Methods: Create a graph where nodes represent data points, and edges represent similarities. These methods propagate labels through the graph to assign labels to unlabeled points.
Applications:

Natural Language Processing: Text classification, sentiment analysis, and named entity recognition often utilize semi-supervised methods to improve results with limited labeled data.
Image Classification: In tasks like object recognition, where labeling images can be labor-intensive, semi-supervised learning can enhance model performance using abundant unlabeled images.
Bioinformatics: Analyzing gene sequences or protein structures, where only a small subset of data may be labeled.