Machine Learning Career Guide - Technical Interview

[mitsumi]

Machine Learning Career Guide - Technical Interview
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 1 GB
Duration: 5 hours | Genre: eLearning Video | Language: English
Get ready for a technical Machine Learning interview by mastering commonly asked interview questions.

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

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 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.

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
         

               
 

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