Pickl AI

APRIL 9, 2025

AI is a broad field focused on simulating human intelligence, encompassing techniques like decision trees and rule-based systems. In contrast, DL is a specialized subset of Machine Learning (ML) that uses multi-layered neural networks to recognize patterns and automate complex tasks.

Deep Learning 52

Deep Learning Deep Learning Artificial Intelligence Artificial Intelligence 52

Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

Artificial Intelligence 52

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Pickl AI

SEPTEMBER 12, 2024

Association Rule Learning: A rule-based Machine Learning method to discover interesting relationships between variables in large databases. B Big Data : Large datasets characterised by high volume, velocity, variety, and veracity, requiring specialised techniques and technologies for analysis.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

MAY 31, 2023

Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Support Vector Machines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Heartbeat

OCTOBER 11, 2023

Model Complexity Machine Learning : Traditional machine learning models have fewer parameters and a simpler structure than deep learning models. They typically rely on simpler algorithms like decision trees, support vector machines, or linear regression.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Mlearning.ai

MAY 23, 2023

Overfitting: The model performs well only for the sample training data. If any new data is given as input to the model, it fails to provide any result. Decision trees are more prone to overfitting. Some algorithms that have low bias are Decision Trees, SVM, etc. Variance: Variance is also a kind of error.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

ODSC - Open Data Science

JANUARY 7, 2025

Scala is worth knowing if youre looking to branch into data engineering and working with big data more as its helpful for scaling applications. Data Engineering Data engineering remains integral to many data science roles, with workflow pipelines being a key focus.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

Pickl AI

SEPTEMBER 23, 2024

Several technologies bridge the gap between AI and Data Science: Machine Learning (ML): ML algorithms, like regression and classification, enable machines to learn from data, enhancing predictive accuracy. Big Data: Large datasets fuel AI and Data Science, providing the raw material for analysis and model training.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

DrivenData Labs

MAY 1, 2025

Some participants combined a transformer neural network with a tree-based model or support vector machine (SVM). For more practical guidance about extracting ML features from speech data, including example code to generate transformer embeddings, see this blog post !

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