How to Learn Machine Learning

APRIL 26, 2025

Data Storage and Management Once data have been collected from the sources, they must be secured and made accessible. The responsibilities of this phase can be handled with traditional databases (MySQL, PostgreSQL), cloud storage (AWS S3, Google Cloud Storage), and big data frameworks (Hadoop, Apache Spark).

Data Science 52

Data Science Data Analyst Data Scientist Machine Learning 52

Pickl AI

APRIL 21, 2025

This is where Big Data often comes into play as the source material. Cleaning and Preparing the Data (Data Wrangling) Raw data is almost always messy. This often takes up a significant chunk of a data scientist’s time. Think graphs, charts, and summary statistics.

Big Data 52

Big Data Big Data Data Science Machine Learning 52

Mlearning.ai

JULY 9, 2023

Goal The objective of this post is to demonstrate how Polars performance is much better than other open-source libraries in a variety of data analysis tasks, such as data cleaning, data wrangling, and data visualization. ? Contributions welcome ! ?Acknowledgments

Data Analysis 52

Data Analysis Data Analysis Python Data Scientist 52

Pickl AI

NOVEMBER 21, 2024

Step 2: Numerical Computation in MATLAB Once the data is cleaned, you can use MATLAB for heavy numerical computations. You can load the cleaned data and use MATLAB’s extensive mathematical functions for analysis. Load the cleaned data from the CSV file, and perform statistical tests or models like linear regression.

Python 52

Python Cloud Computing Machine Learning Machine Learning 52

Pickl AI

JULY 20, 2023

Here are some project ideas suitable for students interested in big data analytics with Python: 1. Kaggle datasets) and use Python’s Pandas library to perform data cleaning, data wrangling, and exploratory data analysis (EDA). Analyzing Large Datasets: Choose a large dataset from public sources (e.g.,

Analytics 40

Analytics Analytics Big Data Big Data 40