Applied Data Science

AUGUST 2, 2021

They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. The most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data.

Data Science 130

Data Science Data Scientist ML ML 130

Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Tools and frameworks like Scikit-Learn, TensorFlow, and Keras are often covered.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Pickl AI

NOVEMBER 28, 2024

Understanding various Machine Learning algorithms is crucial for effective problem-solving. Familiarity with cloud computing tools supports scalable model deployment. Continuous learning is essential to keep pace with advancements in Machine Learning technologies.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Dataconomy

MAY 2, 2023

What do machine learning engineers do: ML engineers design and develop machine learning models The responsibilities of a machine learning engineer entail developing, training, and maintaining machine learning systems, as well as performing statistical analyses to refine test results.

ML 110

ML ML Machine Learning Machine Learning 110

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. Things to be learned: Ensemble Techniques such as Random Forest and Boosting Algorithms and you can also learn Time Series Analysis.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

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

Pickl AI

SEPTEMBER 23, 2024

AI, particularly Machine Learning and Deep Learning uses these insights to develop intelligent models that can predict outcomes, automate processes, and adapt to new information. Deep Learning: Advanced neural networks drive Deep Learning , allowing AI to process vast amounts of data and recognise complex patterns.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52