About the course

Machine Learning

Machine learning is “ a branch of artificial intelligence, is about the construction and study of systems that can learn from data. For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders.

The core of machine learning deals with representation and generalization. Representation of data instances and functions evaluated on these instances are part of all machine learning systems. Generalization is the property that the system will perform well on unseen data instances; the conditions under which this can be guaranteed are a key object of study in the subfield of computational learning theory.”

Overview of Machine Learning Techniques

Machine Learning typically involves two phases: a training phase where a quantitative model is built algorithmically based on training data; and a further evaluation/benchmark step where the model is applied to the testing data (a holdout sample that is not touched when training) to evaluate its performance. Once the model performance meets certain criteria: e.g., 99% accurate when predicting the category of an image, it can then be put into production.

In general: machine learning problems fall into the following categories: supervised learning, unsupervised learning, and reinforcement learning (not discussed in this course).  

0. What is a machine learning interview?

Generally speaking, there are two types of Machine Learning interviews:

• 1. Machine Learning Fundamentals interview focuses on basic machine learning concepts, knowledge, and theories. For example: how do you handle missing data? What are the common splitting criteria in a decision tree model?
• 2. Machine Learning System Design (also called Machine Learning Practical Design or Machine Learning Application) will test your capabilities in building an end-to-end machine learning system. For example: how do you design a Facebook newsfeed? How do you make a system that recommends people you may know on LinkedIn? 

This course focuses on the first type: machine learning fundamentals interview. If you would like to see another course on ML system design, please leave a comment in our forum and let us know.

1. Who is this course for?

People who have already taken a machine learning course and want to brush up their ML knowledge/skills for an incoming machine learning interview and get comfortable with concepts like overfitting and regularization.

2. Why choose this course?

We've coached and helped many candidates land their dream data scientists and machine learning engineers job over the last 2 years. And we've summarized and categorized most of the top interviewing questions in this course.

It will get you 100% ready for any ML fundamental (sometimes referred to as ML concept/knowledge) interviews.

3. What is the goal of this course?

This course's goal is not to teach machine learning but to help you stay laser-focused on preparing you for the machine learning fundamental interviews.

4. What are the different types of machine learning interviews? And what does this course cover?

Great question: generally speaking, there are two rounds of machine learning interviews. Those are the concepts-related, knowledge-based interviews commonly referred to as machine learning fundamentals or machine learning concept interviews.

5. How to use this course?

• You can safely skip the deep learning and natural language chapters for data scientist roles on the product and marketing sides. The same applies to the following companies/roles.
  • Amazon's Business Intelligence Engineer: focus on the first 3 chapters
  • Facebook/Meta's data scientist with analytics track: focus on the first 3 chapters
• Machine learning engineers: every chapter except Natural Language Processing (except for the job description mentioned NLP)
• Amazon's research scientist or applied scientist roles: entire course.

Machine learning is probably the most challenging area to prepare for your data scientist interview because the field is changing quickly and broadly.

I recommend you focus on the fundamentals, master a few key machine learning algorithms, and understand and remember all the nitty-gritty inside and out.