Machine Learning – Howard Nguyen

Challenges of Data Science

admin — Sat, 13 Jun 2026 16:35:30 +0000

Data science is inherently difficult because it requires bridging advanced math, software engineering, and specific business domains. The greatest obstacles involve dirty or scarce data, misalignment between technical models and business goals, and the constant need to adapt to rapidly evolving technologies and algorithms. [1, 2, 3, 4] The primary difficulties in data science span several…

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The AI-Era Choice: Orchestrator, System Builder, or Domain Translator

admin — Tue, 24 Mar 2026 06:15:54 +0000

Orchestrator, System Builder, or Domain Translator Artificial intelligence is no longer affecting only software developers and data scientists. It is reshaping work across product management, operations, marketing, finance, healthcare, customer support, compliance, consulting, and leadership. Recent workforce research shows that many organizations are already redesigning work around AI…

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SMOTE and GAN: Similarities, Differences, and Applications

admin — Fri, 22 Nov 2024 04:41:13 +0000

SMOTE vs GAN Synthetic Minority Oversampling Technique (SMOTE) is a method designed to address class imbalance in machine learning (ML) and deep learning (DL) models. Class imbalance occurs when one class is significantly underrepresented compared to others, leading models to favor the majority class during training. SMOTE generates synthetic data points for the minority class by…

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A Brief of Generative AI

admin — Tue, 27 Aug 2024 20:58:41 +0000

Key features and its applications Generative AI refers to a class of AI models that can generate new, synthetic data resembling the data they were trained on. Unlike traditional AI models that are primarily focused on classification or prediction, generative models create new data, such as images, text, or even tabular data. These models learn the underlying distribution of the…

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Google Colab vs. Jupyter vs. Visual Studio Code

admin — Sun, 04 Aug 2024 16:00:12 +0000

Which one you should choose? The choice between Google Colab, Jupyter Notebook, and Visual Studio Code (VS Code) for running Python code depends on your specific needs and preferences. Here’s a detailed comparison to help you decide which one might be best for you: Advantages: Disadvantages: Advantages: Disadvantages: Advantages: Disadvantages: Each tool has its…

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How do you evaluate the performance of a machine learning model?

admin — Sun, 30 Jun 2024 18:15:33 +0000

How do you evaluate? Evaluating the performance of a machine learning model is a crucial step in the model development process. The evaluation methods depend on the type of problem you are dealing with (classification, regression, clustering, etc.). Here are some common evaluation techniques and metrics used for different types of machine learning problems: For classification tasks…

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What is regularization and why it is important?

admin — Sun, 30 Jun 2024 17:50:21 +0000

Why it is important? Regularization is a technique used in machine learning and statistics to prevent overfitting, which occurs when a model learns the noise in the training data instead of the actual underlying patterns. Regularization adds a penalty to the model’s complexity, discouraging it from fitting too closely to the training data. This helps improve the model’s generalization to new…

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How do you handle missing data?

admin — Sun, 30 Jun 2024 17:18:53 +0000

Here are how we handle Handling missing data is a crucial step in data preprocessing, as it can significantly affect the performance of machine learning models. Here are some common techniques to handle missing data: Using pandas and scikit-learn: import pandas as pd from sklearn.impute import SimpleImputer from sklearn.impute import KNNImputer # Sample data data…

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What’s the difference between supervised and unsupervised learning?

admin — Sun, 30 Jun 2024 17:12:51 +0000

What are the differences? Supervised and unsupervised learning are two fundamental types of machine learning, each with its own characteristics and applications. In summary, supervised learning is about making predictions with labeled data, whereas unsupervised learning is about finding hidden patterns in unlabeled data.

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Gradient Boosting Machine (GBM)

admin — Sat, 29 Jun 2024 20:56:17 +0000

GBM, applications, and other MLs’ comparisons Gradient Boosting Machine (GBM) is an ensemble learning technique that builds a model in a stage-wise fashion from multiple weak learners, typically decision trees, and optimizes for a loss function. The basic idea is to combine the predictions of several base estimators to improve robustness over a single estimator.

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