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. It forms the basis for many statistical tests and estimators used in hypothesis testing and confidence interval estimation.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Dataconomy

AUGUST 22, 2023

And lastly, integrating Bayesian techniques with deep learning, which has gained tremendous popularity, presents additional challenges. Combining the flexibility of deep learning architectures with Bayesian updating can be intricate and require specialized knowledge.

Machine Learning 91

Machine Learning Machine Learning Algorithm Deep Learning 91

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 about neural networks and their architecture.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Mlearning.ai

MAY 23, 2023

What is deep learning? What is the difference between deep learning and machine learning? Deep learning is a paradigm of machine learning. In deep learning, multiple layers of processing are involved in order to extract high features from the data. What is a computational graph?

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

APRIL 26, 2024

Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data. Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. In my previous role, we had a project with a tight deadline.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52