Machine Learning Engineering Interview Preparation Course
A Real-World Guide to Acing the Rigorous Machine Learning Engineering Interview
Development,Data Science and AI ML,Machine Learning
Lectures -49
Duration -2 hours
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Course Description
** Mike's courses are popular with many of our clients." Josh Gordon, Developer Advocate, Google **
Recent Reviews
"Excellent course. I highly recommend it. Very clear, succinct and to the point. Will help all machine learners tremendously. Great job Mike!" - Diana
"Great course, can highly recommend this to anyone that wants to do well in their technical interview. Mike West is a great teacher." - Sanmari
"Good overview of concepts that could be asked in an interview. The overview can serve as a guide to which concepts you might need to revisit." -- Jonathan
Course Description
The machine learning engineer is the single most in-demand job on earth, according to top job board indeed.
If you want to land a job as a machine learning engineer, you’ll need to pass a rigorous and competitive interview process. Most top companies will have a phone screen and at least two to three rounds of in-person interviews.
Listen, I'm not going to lie to you. The interview process is comprehensive and knowledge intensive. Our interview process is over 100 questions. Big salaries come with big skills. You either have the skills and knowledge necessary to succeed in the real-world or you don't.
While it’s impossible to provide all the answers to questions you’ll see in the applied space, this course is a comprehensive look into many of the questions you’ll see in real-world interviews.
In this course you’ll learn how to answer many questions specific to machine learning in the applied space. You’ll learn about concept reductionism and how to apply it in an interview setting.
In the Artificial Intelligence space, role confusing is rampant. Many working in applied careers can’t provide you with a clear definition of the roles in this space. This course will define the trends and the data behind the top jobs.
You’ll learn to analyze job posts you are interested in and tailor your interview preparation based on each individual role.
You’ll start by learning how to handle the most basic questions about machine learning. For example, what is machine learning and how does it fit into the Artificial Intelligence Hierarchy? If you can’t answer the most basic questions, you certainly won’t be able to tackle the more difficult ones.
In this course, I'm not going to list out a bunch of questions. I'm going to answer them and show you the applied aspect to the answer.
For example, I'll define hyperparameter tuning then I'll show you how these parameters are passed into a model.
Thanks for your interest in my course.
Who this course is for:
- If you want to become a machine learning engineer then this course is for you.
- If you want to impress perspective employers with your data science acumen this course is for you
- If you want to truly understand what applied machine learning is in the applied space this course is for you
- If you want to make it past the technical phone screen and in-person interview this course is for you
Goals
What will you learn in this course:
- You'll learn how to properly prepare for the real-world machine learning interview
- You'll learn questions taken from real-world interviews
- You'll learn how to read job requirements specific to machine learning
- You'll learn why the most important skill for a machine learning engineer is SQL
Prerequisites
What are the prerequisites for this course?
- A basic understanding of programming in Python
- Familiarity with the machine learning process
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
5 Lectures
- Course Introduction 01:30 01:30
- The AI Hierarchy 03:34 03:34
- The Two Types of Interviews 01:36 01:36
- The Core Careers in Artificial Intelligence 02:41 02:41
- Interview Questions (The AI Hierarchy and Definitions) 02:05 02:05
Machine Learning Concepts
10 Lectures
Python and the Core Machine Learning Libraries
6 Lectures
Working With Data
6 Lectures
Statistics in Machine Learning
10 Lectures
Modeling
11 Lectures
Instructor Details
Mike West
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