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

Pickl AI

JANUARY 12, 2025

Develop Hybrid Models Combine traditional analytical methods with modern algorithms such as decision trees, neural networks, and support vector machines. Clustering algorithms, such as k-means, group similar data points, and regression models predict trends based on historical data.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

Dataconomy

FEBRUARY 26, 2025

This capability bridges various disciplines, leveraging techniques from statistics, machine learning, and artificial intelligence. Some key areas include: Big Data analytics: It helps in interpreting vast amounts of data to extract meaningful insights.

Support Vector Machines 91

Support Vector Machines Machine Learning Machine Learning Decision Trees 91