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

NOVEMBER 4, 2024

Here are three critical areas worth exploring: Machine Learning, Data Visualisation, and Big Data. Machine Learning with Python Machine Learning is a vital component of Data Science, enabling systems to learn from data and make predictions.

Data Science 52

Data Science Python Machine Learning Machine Learning 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

These techniques span different types of learning and provide powerful tools to solve complex real-world problems. Supervised Learning Supervised learning is one of the most common types of Machine Learning, where the algorithm is trained using labelled data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

APRIL 2, 2024

Understanding Data Structured Data: Organized data with a clear format, often found in databases or spreadsheets. Unstructured Data: Data without a predefined structure, like text documents, social media posts, or images. Data Cleaning: Process of identifying and correcting errors or inconsistencies in datasets.

Data Science 52

Data Science Machine Learning Machine Learning Predictive Analytics 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

DagsHub

NOVEMBER 11, 2024

This metadata will help make the data labelling, feature extraction, and model training processes smoother and easier. These processes are essential in AI-based big data analytics and decision-making. Data Lakes Data lakes are crucial in effectively handling unstructured data for AI applications.

AI 52

AI AI Data Lakes Database 52