About this course: If you want
to break into competitive data science, then this course is for you!
Participating in predictive modelling competitions can help you gain
practical experience, improve and harness your data modelling skills in
various domains such as credit, insurance, marketing, natural language
processing, sales’ forecasting and computer vision to name a few. At the
same time you get to do it in a competitive context against thousands
of participants where each one tries to build the most predictive
algorithm. Pushing each other to the limit can result in better
performance and smaller prediction errors. Being able to achieve high
ranks consistently can help you accelerate your career in data science.
In this course, you will learn to analyse and solve competitively such
predictive modelling tasks. When you finish this class, you will: –
Understand how to solve predictive modelling competitions efficiently
and learn which of the skills obtained can be applicable to real-world
tasks. – Learn how to preprocess the data and generate new features from
various sources such as text and images. – Be taught advanced feature
engineering techniques like generating mean-encodings, using aggregated
statistical measures or finding nearest neighbors as a means to improve
your predictions. – Be able to form reliable cross validation
methodologies that help you benchmark your solutions and avoid
overfitting or underfitting when tested with unobserved (test) data. –
Gain experience of analysing and interpreting the data. You will become
aware of inconsistencies, high noise levels, errors and other
data-related issues such as leakages and you will learn how to overcome
them. – Acquire knowledge of different algorithms and learn how to
efficiently tune their hyperparameters and achieve top performance. –
Master the art of combining different machine learning models and learn
how to ensemble. – Get exposed to past (winning) solutions and codes and
learn how to read them. Disclaimer : This is not a machine learning
course in the general sense. This course will teach you how to get
high-rank solutions against thousands of competitors with focus on
practical usage of machine learning methods rather than the theoretical
underpinnings behind them. Prerequisites: – Python: work with DataFrames
in pandas, plot figures in matplotlib, import and train models from
scikit-learn, XGBoost, LightGBM. – Machine Learning: basic understanding
of linear models, K-NN, random forest, gradient boosting and neural
networks.
Size: 2.02G
Created by: National Research University Higher School of Economics
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Taught by: Dmitry Ulyanov, Visiting lecturer
HSE Faculty of Computer Science -
Taught by: Alexander Guschin, Visiting lecturer at HSE, Lecturer at MIPT
HSE Faculty of Computer Science -
Taught by: Mikhail Trofimov, Visiting lecturer
HSE Faculty of Computer Science -
Taught by: Dmitry Altukhov, Visiting lecturer
HSE Faculty of Computer Science -
Taught by: Marios Michailidis, Research Data Scientist
H2O.ai
Basic Info |
Course 2 of 7 in the Advanced Machine Learning Specialization
|
Level | Advanced |
Commitment | 6-10 hours/week |
Language |
English
|
How To Pass | Pass all graded assignments to complete the course. |
User Ratings |
4.7 stars
Average User Rating 4.7See what learners said
|