Posted on 2019-04-27, by fubber.
mp4 | AVC: 1280x720 G_P| Audio:AAC LC 128 Kbps | Total files:105 | mp4, txt, vtt | 1.02 GB
Genre: eLearning | Language: English
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
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
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
Width: 1280 pixels
Height: 720 pixels
Duration: 9min 18s
Bit rate: 546 Kbps
Frame rate: 30.000 fps
Aspect ratio: 16:9
Bit depth: 8 bits
Color space: YUV
Audio track: 1
Codec: AAC LC
Bit rate: 128 Kbps N/A
Sampling rate: 48 Khz
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