Pickl AI

NOVEMBER 4, 2024

Statistics Understand descriptive statistics (mean, median, mode) and inferential statistics (hypothesis testing, confidence intervals). Scikit-learn Scikit-learn is the go-to library for Machine Learning in Python. These concepts help you analyse and interpret data effectively.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Pickl AI

FEBRUARY 12, 2024

Here are some key areas often assessed: Programming Proficiency Candidates are often tested on their proficiency in languages such as Python, R, and SQL, with a focus on data manipulation, analysis, and visualization. Differentiate between supervised and unsupervised learning algorithms.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 28, 2024

Concepts such as probability distributions, hypothesis testing , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions. These techniques span different types of learning and provide powerful tools to solve complex real-world problems.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

SEPTEMBER 12, 2024

Boosting: An ensemble learning technique that combines multiple weak models to create a strong predictive model. C Classification: A supervised Machine Learning task that assigns data points to predefined categories or classes based on their characteristics.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. Students should learn about data wrangling and the importance of data quality. Students should learn how to train and evaluate models using large datasets.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Becoming Human

MAY 15, 2023

In Inferential Statistics, you can learn P-Value , T-Value , Hypothesis Testing , and A/B Testing , which will help you to understand your data in the form of mathematics. Unsupervised Learning In this type of learning, the algorithm is trained on an unlabeled dataset, where no correct output is provided.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

Pickl AI

DECEMBER 4, 2024

Concepts such as probability distributions, hypothesis testing, and regression analysis are fundamental for interpreting data accurately. Machine Learning Understanding Machine Learning algorithms is essential for predictive analytics. Ensuring data quality is vital for producing reliable results.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52