Data Science Dojo

JUNE 1, 2023

The course covers topics such as linear regression, logistic regression, and decision trees. Machine Learning for Data Science by Carlos Guestrin This is an intermediate-level course that teaches you how to use machine learning for data science tasks.

Machine Learning 195

Machine Learning Machine Learning Natural Language Processing Deep Learning 195

Data Science Dojo

JUNE 9, 2023

Data science boot camps are intensive, short-term programs that teach students the skills they need to become data scientists. These programs typically cover topics such as data wrangling, statistical inference, machine learning, and Python programming.

Data Science 370

Data Science Machine Learning Machine Learning Data Scientist 370

ODSC - Open Data Science

AUGUST 15, 2023

Build Classification and Regression Models with Spark on AWS Suman Debnath | Principal Developer Advocate, Data Engineering | Amazon Web Services This immersive session will cover optimizing PySpark and best practices for Spark MLlib. Finally, you’ll explore how to handle missing values and training and validating your models using PySpark.

Machine Learning 52

Machine Learning Machine Learning Data Science Data Scientist 52

Pickl AI

MARCH 20, 2024

R, with its robust statistical capabilities, remains a popular choice for statistical analysis and data visualization. Data wrangling and preprocessing Data seldom comes in a pristine form; it often requires cleaning, transformation, and preprocessing before analysis.

Data Science 52

Data Science Data Scientist Data Wrangling Data Visualization 52

Pickl AI

SEPTEMBER 12, 2024

D Data Mining : The process of discovering patterns, insights, and knowledge from large datasets using various techniques such as classification, clustering, and association rule learning. Data Wrangling: The cleaning, transforming, and structuring of raw data into a format suitable for analysis.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

MAY 31, 2023

Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines. Machine Learning: Data Science aspirants need to have a good and concise understanding on Machine Learning algorithms including both supervised and unsupervised learning.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Pickl AI

FEBRUARY 12, 2024

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52