Pickl AI

JULY 12, 2024

Jupyter notebooks are widely used in AI for prototyping, data visualisation, and collaborative work. Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. There are three main types of Machine Learning: supervised learning, unsupervised learning, and reinforcement learning.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Data Science Blog

OCTOBER 20, 2024

Algorithmic Attribution using binary Classifier and (causal) Machine Learning While customer journey data often suffices for evaluating channel contributions and strategy formulation, it may not always be comprehensive enough. Moreover, random forest models as well as support vector machines (SVMs) are also frequently applied.

Machine Learning 130

Machine Learning Machine Learning Deep Learning Deep Learning 130

Flipboard

JULY 31, 2023

Importance and Role of Datasets in Machine Learning Data is king. Algorithms are important and require expert knowledge to develop and refine, but they would be useless without data. Datasets are to machine learning what fuel is to a car: they power the entire process.

Machine Learning 123

Machine Learning Machine Learning Deep Learning Deep Learning 123

Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Heartbeat

OCTOBER 25, 2023

Next-generation sequencing (NGS) platforms have dramatically increased the speed and reduced the cost of DNA sequencing, leading to the generation of vast amounts of genomic data. Integrating and analyzing data from multiple platforms and experiments pose challenges due to data formats, normalization techniques, and data quality differences.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Algorithm 52

Pickl AI

AUGUST 9, 2024

Students should learn about data wrangling and the importance of data quality. Statistical Analysis Introducing statistical methods and techniques for analysing data, including hypothesis testing, regression analysis, and descriptive statistics. Students should learn how to apply machine learning models to Big Data.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

AWS Machine Learning Blog

JANUARY 17, 2024

The first step is to use density-based spatial clustering of applications with noise (DBSCAN) algorithm to cluster stops from pings. Data quality check We can check the daily incoming data feed for quality and detect anomalies using QuickSight dashboards and data analyses.

Clustering 126

Clustering AWS ML ML 126

Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

OCTOBER 15, 2024

These models do not rely on predefined labels; instead, they discover the inherent structure in the data by identifying clusters based on similarities. Popular clustering algorithms include k-means and hierarchical clustering. Quality data is essential, as poor or incomplete data can lead to inaccurate models.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52