Machine Learning Technical Interview
4.0 (64 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.
3,859 students enrolled

Machine Learning Technical Interview

Get ready for a technical Machine Learning interview / Data Science interview by mastering commonly asked questions!
4.0 (64 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.
3,859 students enrolled
Created by Vladimir Poliakov
Last updated 2/2020
English
English [Auto-generated]
Current price: $83.99 Original price: $119.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 5 hours on-demand video
  • 50 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
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What you'll learn
  • Prepare for machine learning technical questions
  • Improve or refresh knowledge in machine learning
  • Get a great intuition of the machine learning topics
  • Recall fundamental aspects of data processing
  • Know variety of feature engineering methods
  • Handle dimensionality reduction questions
  • Recall many classification and regression models
  • Understand the pros and cons between machine learning methods
  • Handle advanced questions on supervised learning
  • Discuss hyperparameters and how to apply cross-validation
  • Build an understanding of good experiment design
  • Recall the concepts of feature selection
  • Describe different types of dataset balancing methods
  • Have an intuition of main сlustering algorithms
  • Get practice with model evaluation questions
Course content
Expand all 51 lectures 05:10:49
+ Data Mining
10 lectures 59:42
What is data normalization
07:02
Tailed feature distribution
04:39
Box-Cox transformation
03:33
Handling outliers
05:54
The art of feature engineering (PART 1)
05:54
The art of feature engineering (PART 2)
05:51
Time series feature extraction
08:17
Curse of dimensionality
05:55
10 questions on the material just studied.
Let's recap what you've learned!
10 questions
+ Supervised Learning Algorithms
20 lectures 02:07:59
Linear regression pros & cons
08:33
Ridge vs Lasso
07:01
Multicollinearity
08:46
Maximum Likelihood Estimation (PART 1)
03:46
Maximum Likelihood Estimation (PART 2)
05:34
MLE for Linear regression
05:59
Logistic regression intuition
05:53
Naive Bayes naivety (PART 1)
07:01
Naive Bayes naivety (PART 2)
07:49
SVM – a large margin classifier
06:55
SVM and regularization
05:07
Logistic regression and SVM
04:52
kNN pseudocode
06:42
Bagging explanation
06:11
Random forest randomness
05:12
Extremely randomized trees
04:44
Ensembling intuition
07:41
Adaboost
06:52
GBM and RF difference
05:18
10 questions on the material just studied.
Let's recap what you've learned!
10 questions
+ Domain Expertise
10 lectures 01:07:10
Model overfitting and underfitting
09:35
Cross-validation
06:43
Attributes selection
09:47
Hyperparameter optimization
09:18
Time series cross-validation
06:42
Feature selection methods (PART 1)
06:08
Feature selection methods (PART 2)
05:16
Sampling and splitting
04:47
Handling imbalanced dataset (PART 2)
05:25
5 questions on the material just studied.
Let's recap what you've learned!
5 questions
+ Unsupervised Learining Algorithms
5 lectures 25:26
Mean-shift clustering
04:10
DBScan clustering algorithm
05:10
Gaussian mixture algorithm
04:48
Agglomerative hierarchical clustering
03:53
3 questions on the material just studied.
Let's recap what you've learned!
3 questions
+ Model Evaluation
5 lectures 26:48
ROC curve explanation
03:45
RMSE vs MAE
05:13
R squared and Adjusted R squared
03:11
Unsupervised learning evaluation
06:23
3 questions on the material just studied.
Let's recap what you've learned!
5 questions
Requirements
  • Some high school mathematics level
  • Basic knowledge in probability theory and statistics
  • Basic understanding of data science concepts
  • Basic understanding of machine learning algorithms
  • Some prior computer science experience
Description

This course is designed to become a convenient resource for preparing for a technical machine learning interview. It helps you to get ready for an interview with 50 lectures covering questions and answers on a varied range of topics. The course is intended not only for candidates with a full understanding of possible questions but also for recalling knowledge in data science.

We will systematically cover the data preparation methods including data normalization, outliers handling, feature engineering, and dimensionality reduction techniques.

After processing the data in the next section, we will move on to the supervised machine learning methods. We will consider simple linear algorithms, regularization, maximum likelihood method. Besides, we will also talk about the Bayes theorem and the naive Bayes classifier. Several lectures in this section are devoted to the support vector machine model. Most of the lectures after this will be dedicated to algorithms based on decision-making trees: we will consider bagging algorithm, random forest, AdaBoost, and gradient boosting.

Having finished reviewing the interview questions on algorithms, we will move on to the subject area of machine learning and discuss such topics as good experiment design, cross-validation methods, overfitting and underfitting, feature selection methods, unbalanced data problem.

This course also includes several lectures on clustering algorithms, covering the most well-known methods and their concepts. In addition, as part of this course, we will consider various metrics for assessing the quality of supervised and unsupervised models.

In summary, this course will help you to recall the methods used by real machine learning experts and prepare you for this hot data scientist career path.

Who this course is for:
  • Anyone who wants to prepare for a Machine Learning interview
  • Anyone who wants to improve or recall Machine Learning skills
  • Anyone who wants to start or switch their career to Data Science