Pickl AI

AUGUST 9, 2024

Summary: A comprehensive Big Data syllabus encompasses foundational concepts, essential technologies, data collection and storage methods, processing and analysis techniques, and visualisation strategies. Fundamentals of Big Data Understanding the fundamentals of Big Data is crucial for anyone entering this field.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

JANUARY 12, 2025

4 Steps to Combine Both Approaches Data-driven and AI-driven modelling involves integration in well-defined, structured steps where each surely can assure a mix of efficiency and insight with a broader view. Unify Data Sources Collect data from multiple systems into one cohesive dataset.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

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. They are handy for high-dimensional data.

Machine Learning 52

Machine Learning Machine Learning ML ML 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

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