Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
Have you ever wondered how to properly prepare for a Machine Learning Interview? In this course, you will prepare answers for 15 common Machine Learning (ML) in Python interview questions for a data scientist role.
These questions will revolve around seven important topics: data preprocessing, data visualization, supervised learning, unsupervised learning, model ensembling, model selection, and model evaluation.
You’ll start by working on data pre-processing and data visualization questions. After performing all the preprocessing steps, you’ll create a predictive ML model to hone your practical skills.
Next, you’ll cover some supervised learning techniques before moving on to unsupervised learning. Depending on the role, you’ll likely cover both topics in your machine learning interview.
Finally, you’ll finish by covering model selection and evaluation, looking at how to evaluate performance for model generalization, and look at various techniques as you build an ensemble model.
By the end of the course, you will possess both the required theoretical background and the ability to develop Python code to successfully answer these 15 questions.
The coding examples will be mainly based on the scikit-learn package, given its ease of use and ability to cover the most important machine learning techniques in the Python language.
The course does not teach machine learning fundamentals, as these are covered in the course's prerequisites.
Have you ever wondered how to properly prepare for a Machine Learning Interview? In this course, you will prepare answers for 15 common Machine Learning (ML) in Python interview questions for a data scientist role.
These questions will revolve around seven important topics: data preprocessing, data visualization, supervised learning, unsupervised learning, model ensembling, model selection, and model evaluation.
You’ll start by working on data pre-processing and data visualization questions. After performing all the preprocessing steps, you’ll create a predictive ML model to hone your practical skills.
Next, you’ll cover some supervised learning techniques before moving on to unsupervised learning. Depending on the role, you’ll likely cover both topics in your machine learning interview.
Finally, you’ll finish by covering model selection and evaluation, looking at how to evaluate performance for model generalization, and look at various techniques as you build an ensemble model.
By the end of the course, you will possess both the required theoretical background and the ability to develop Python code to successfully answer these 15 questions.
The coding examples will be mainly based on the scikit-learn package, given its ease of use and ability to cover the most important machine learning techniques in the Python language.
The course does not teach machine learning fundamentals, as these are covered in the course's prerequisites.
