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Machine Learning Career Guide – Technical Interview

Get ready for a technical Machine Learning interview by mastering commonly asked interview questions.
Instructor:
Vladimir Poliakov
2,618 students enrolled
English [Auto-generated]
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

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 machine learning.

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 career path.

Fresh May 2019 update! Devote the whole night before the interview with fully downloadable lecture slides.

Welcome

1
Course overview

Data Mining

1
Missing or corrupted data handling
2
What is data normalization
3
Tailed feature distribution
4
Box-Cox transformation
5
Handling outliers
6
The art of feature engineering (PART 1)
7
The art of feature engineering (PART 2)
8
Time series feature extraction
9
Curse of dimensionality
10
Principal Component Analysis

Supervised Learning Algorithms

1
Linear regression pros & cons
2
Ridge vs Lasso
3
Multicollinearity
4
Maximum Likelihood Estimation (PART 1)
5
Maximum Likelihood Estimation (PART 2)
6
MLE for Linear regression
7
Logistic regression intuition
8
Naive Bayes naivety (PART 1)
9
Naive Bayes naivety (PART 2)
10
SVM – a large margin classifier
11
SVM and regularization
12
Logistic regression and SVM
13
kNN pseudocode
14
Decision tree splitting
15
Bagging explanation
16
Random forest randomness
17
Extremely randomized trees
18
Ensembling intuition
19
Adaboost
20
GBM and RF difference

Domain Expertise

1
Model overfitting and underfitting
2
Cross-validation
3
Attributes selection
4
Hyperparameter optimization
5
Time series cross-validation
6
Feature selection methods (PART 1)
7
Feature selection methods (PART 2)
8
Sampling and splitting
9
Handling imbalanced dataset (PART 1)
10
Handling imbalanced dataset (PART 2)

Unsupervised Learining Algorithms

1
K-means clustering and kNN
2
Mean-shift clustering
3
DBScan clustering algorithm
4
Gaussian mixture algorithm
5
Agglomerative hierarchical clustering

Model Evaluation

1
Classification evaluation
2
ROC curve explanation
3
RMSE vs MAE
4
R squared and Adjusted R squared
5
Unsupervised learning evaluation
You can view and review the lecture materials indefinitely, like an on-demand channel.
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5 hours on-demand video
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Certificate of Completion