Boosting, part 2: Gradient Boosting, XGboost

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Fee: 650 UAH

Online

Gradient Boosting is another technique for performing supervised machine learning tasks, like classification and regression. XGBoost stands for Extreme Gradient Boosting; it is a specific implementation of the Gradient Boosting method which uses more accurate approximations to find the best tree model. It employs a number of nifty tricks that make it exceptionally successful, particularly with structured data. Let's talk in more details.

Reinforcement Learning

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Fee: 650 UAH

Online

When it comes to explaining machine learning to those not concerned in the field, reinforcement learning is probably the easiest sub-field for this challenge. Let's talk in more details.

Git/Git Hub + Pipelines

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Fee: 650 UAH

Online

Pipeline can be used to chain multiple estimators into one. This is useful as there is often a fixed sequence of steps in processing the data, for example feature selection, normalization and classification. Let's talk in more details.

Recommendation systems

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Fee: 650 UAH

Online

Recommender systems aim to predict users’ interests and recommend product items that quite likely are interesting for them. They are among the most powerful machine learning systems that online retailers implement in order to drive sales. Let's talk in more details.

Classification and Regression Trees

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Fee: 650 UAH

Online

A decision tree is a supervised machine learning model used to predict a target by learning decision rules from features. Classification and Regression Trees is a term to refer to Decision Tree algorithms that can be used for classification or regression predictive modelling problems. Let's talk in more details.

Neural networks

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Fee: 650 UAH

Online

This webinar will be on 8th of May and on 12th of May. A neural network is a statistical technique that calculates weights for predictor characteristics by self-learning from data examples. Let's talk in more details.

Logistic Regression on Python

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Fee: 650 UAH

Online

Logistic regression is a statistical method for predicting binary classes. The outcome or target variable is dichotomous in nature. Dichotomous means there are only two possible classes. Let's talk in more details.

Tree-based models

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Fee: 650 UAH

Online

Decision trees are supervised learning models used for problems involving classification and regression. Tree models present a high flexibility that comes at a price: on one hand, trees are able to capture complex non-linear relationships; on the other hand, they are prone to memorizing the noise present in a dataset. Let's talk in more details.

Loss Functions

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Fee: 650 UAH

Online

The loss function is the bread and butter of modern machine learning; it takes your algorithm from theoretical to practical and transforms neural networks from glorified matrix multiplication into deep learning. Let's talk in more details.