Practicing Machine Learning Interview Questions in Python

Overview

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.

Syllabus

Data Pre-processing and Visualization

In the first chapter of this course, you'll perform all the preprocessing steps required to create a predictive machine learning model, including what to do with missing values, outliers, and how to normalize your dataset.

Supervised Learning

In the second chapter of this course, you'll practice different several aspects of supervised machine learning techniques, such as selecting the optimal feature subset, regularization to avoid model overfitting, feature engineering, and ensemble models to address the so-called bias-variance trade-off.

Unsupervised Learning

In the third chapter of this course, you'll use unsupervised learning to apply feature extraction and visualization techniques for dimensionality reduction and clustering methods to select not only an appropriate clustering algorithm but optimal cluster number for a dataset.

Model Selection and Evaluation

In the fourth and final chapter of this course, you'll really step it up and apply bootstrapping and cross-validation to evaluate performance for model generalization, resampling techniques to imbalanced classes, detect and remove multicollinearity, and build an ensemble model.