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Camacho Reflection#12

Machine learning (ML) is a subset of artificial intelligence that allows systems to learn from data without explicit programming, utilizing algorithms to analyze datasets and make predictions. Its effectiveness hinges on data quality and model accuracy, with challenges like overfitting and bias. The future of ML is promising, with advancements in data availability and computing power leading to applications in healthcare, finance, and autonomous systems.

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23 views1 page

Camacho Reflection#12

Machine learning (ML) is a subset of artificial intelligence that allows systems to learn from data without explicit programming, utilizing algorithms to analyze datasets and make predictions. Its effectiveness hinges on data quality and model accuracy, with challenges like overfitting and bias. The future of ML is promising, with advancements in data availability and computing power leading to applications in healthcare, finance, and autonomous systems.

Uploaded by

camacho.zheandrex
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© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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NCP 4201 – Emerging Technologies in CpE

Name: Camacho, Zheandrex N. Reflection #: 12

Student Number: 20211116551 Topic: Machine Learning

Machine learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn and improve
from experience without being explicitly programmed. It relies on algorithms and statistical models to analyze
large datasets, identify patterns, and make predictions or decisions. ML models can be categorized into three main
types: supervised learning, where the model is trained on labeled data; unsupervised learning, which identifies
hidden patterns in unlabeled data; and reinforcement learning, where the model learns through trial and error by
receiving feedback. This technology is widely used in applications such as recommendation systems, fraud
detection, and language translation.

The effectiveness of machine learning depends heavily on data quality and model accuracy. During the
training process, models learn from historical data, adjusting their parameters to minimize errors. Techniques such
as cross-validation, regularization, and hyperparameter tuning help improve model performance. However, ML
models can face challenges like overfitting, where the model memorizes training data instead of generalizing, and
bias, which can lead to inaccurate or unfair predictions. Despite these limitations, ML continues to advance with
deep learning, a subset of ML that uses neural networks to process complex data, making significant strides in
fields like image recognition and natural language processing.

The future of machine learning is being shaped by growing data availability, increased computing power,
and improved algorithms. As models become more sophisticated, they will play a key role in autonomous systems,
predictive analytics, and personalized services. In healthcare, ML is being used to develop diagnostic tools and
personalized treatment plans. In finance, it enhances fraud detection and algorithmic trading. With the integration
of AI and edge computing, machine learning is expected to become even more efficient, enabling real-time
decision-making and driving innovation across various industries.

Computer Engineering Department Page 1 of 1

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