Acing the Machine Learning Engineering Interview
4.4 (13 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
144 students enrolled

Acing the Machine Learning Engineering Interview

A Real-World Guide to Acing the Rigorous Machine Learning Engineering Interview
4.4 (13 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
144 students enrolled
Created by Mike West
Last updated 5/2020
English
English [Auto]
Current price: $13.99 Original price: $19.99 Discount: 30% off
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This course includes
  • 2.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • 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
Requirements
  • A basic understanding of programming in Python
  • Familiarity with the machine learning process
Description

The machine learning engineer is the single most in-demand job on earth, according to top job board indeed. 

My name is Mike West and I'm a machine learning engineer in the applied space. I've worked or consulted with over 50 companies and just finished a project with Microsoft. I've published over 50 courses and this is 50 on Udemy. If you're interested in learning what the real-world is really like then you're in good hands.

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
Course content
Expand all 54 lectures 02:21:34
+ Introduction
6 lectures 13:00

This lecture is the course introduction.

Preview 01:30

Let's discuss what this course is about.

Preview 01:34

In this lecture let's define the AI Hierarchy.

Preview 03:34

In this lecture let's discuss the two types of interviews. The phone screen and the in-person interview.

Preview 01:36

Let's define the core careers in this space. There are only three of them.

The Core Careers in Artificial Intelligence
02:41

In this lecture let's cover some interview questions specific to the AI hierarchy.

Interview Questions (The AI Hierarchy and Definitions)
02:05
+ Machine Learning Concepts
10 lectures 32:00

In this lesson let's discuss what this section will cover.

Preview 01:30

There are 5 core themes in most machine learning jobs. Let's tackle them here.

Five Common Job Themes
03:23

Python is the gold standard in applied machine learning.

Python
01:28

Let's cover the very basic nomenclature in this space.

Basic Data Terminlogy
02:01

In this lesson we will cover the core machine learning types.

Types of Machine Learning
03:12

Machine learning is very process oriented. Let's learn about that process.

Machine Learning Process
04:35

Let's cover some interview questions.

Interview Questions (Core Vernacular and the Machine Learning Process)
08:37

Let's learn about data wrangling.

Data Wrangling Process
02:34

The array is the core data object in machine learning.

The Array
01:28

Let's cover a few interview questions specific to the array and imputation.

Interview Questions (Imputation and Arrays)
03:12
+ Python and the Core Machine Learnng Libraries
7 lectures 21:11

In this lesson let's discuss what will be covered in this section.

Preview 01:08

Let's tackle a few Python questions.

Interview Questions (Python)
05:41

Let's tackle eve more Python questions.

Interview Questions (More Python Questions)
03:27

Let's learn about frameworks and libraries.

No Deep Learning Frameworks or Libraries
02:18

Let's cover a few interview questions specific to the core libraries.

Interview Questions - Core Library - Pandas
03:24

This lesson is all about SciKit-Learn.

Interview Questions - Core Library - SciKit-Learn
02:58

Let's tackle a few array questions.

Interview Questions - Core Library - NumPy
02:15
+ Working With Data
7 lectures 17:42

Let's discuss what will be covered in this section.

Preview 01:36

In this lecture let's learn about the two core types of data.

Two Types of Data
02:05

Currently, most models are being sourced from relational databases.

Databases
02:41

Let's learn about keys.

Table Relationships
03:47

What is DDL and DML? 

Manipulating Data
02:44

Let's learn how tables are joined together.

Table Joins
03:06

This course is free. Please take it. Study it. Learn how to navigate relational databases.

Free Transact-SQL Course
01:43
+ Statistics in Machine Learning
13 lectures 29:07
Section Introduction
01:35

Let's learn about statistics for machine learning.

Statistis and Machine Learning
03:55

Let's tackle a few interview questions specific to basic statistics in machine learning.

Interview Questions (Basic Statistics)
02:08

What are the measures of central tendency?

Measures of Central Tendency
01:29

More data is almost always better. Why is that? 

Law of Large Numbers
01:01

Let's cover some measures of variation in your data.

Measure of Variability
02:09

Let's tackle some interview questions specific to the measure of variability and the measure of central tendency.

Interview Qustions (MOCT,MOV)
02:02

Let's learn about outliers and replace missing values in our data.

Outliers and Imputation
06:28

Let's tackle some interview questions specific to imputation.

Interview Quetions (Imputation)
01:27

What is recalling and why is it important.

Recaling
01:42

Let's cover a few questions specific to scaling our data.

Interview Questions (Recaling)
02:07

Let's learn about one-hot encoding in this lesson.

One-Hot Encoding
00:55

What is the bias variance trade off? It's become a standard interview question.

Bias-Variance Tradeoff
02:09
+ Modeling
11 lectures 28:34
Section Introduction
01:23

What are machine learning models. Let's find out in this lesson.

Machine Learning Models
02:37

Let's cover a few core modeling problems.

Common Modeling Problems
01:59

What is a metric and what are some metrics for one of the most common problems in applied machine learning? 

Classification Metrics
03:59

Let's tackle a few questions specific to classification metrics. 

Interview Questions (Classificaton Metrics)
02:34
Interview Questions (Regression Metrics)
02:55

What is bagging and boosting and why is boosting so important? 

Bagging and Boosting
03:56

What is XGBoost? Let's find out in this lesson.

What is XGBoost?
02:13

Let's cover some bagging and boosting interview questions.

Interview Questions (Bagging, Boosting and XGBoost)
02:11

Let's define ANNs and deep learning models.

Artificial Neural Networks
03:22
Interview Questions (ANNs and Deep Learning)
01:25