$1,400 – $2,000

[3 Day Training Course] Machine Learning: Dubai

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Date and Time

Location

Location

Venue is being confirmed. Stay tuned!

Dubai, 73301

United Arab Emirates

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Refund Policy

Refund Policy

Refunds up to 7 days before event

Event description

Description

Why this training?

This 3-day course will give you a comprehensive overview of various tools, frameworks, and concepts behind machine learning.

In just three days, you will get a clear understanding of the core machine learning techniques, mathematical concepts, and engineering solutions for daily usage. You will go through the complete process of building machine learning systems, from data understanding to modelling.

During hands-on labs, accompanying each theoretical unit, you will see the inner workings of a machine learning model and will reproduce the stages of its development life cycle.

At the end of the course, the participants will be able to design working scripts that can be used as a basis for creating algorithms to address business-specific challenges.


Who should attend?

This training is a good fit for anyone who has a basic understanding of Python and wants to acquire new skills in just 3 days


Course objectives

  • Gain a basic understanding of machine learning concepts

  • Learn how to use main troubleshooting techniques of machine learning

  • See the inner workings of a machine learning model and reproduce the stages of its development life cycle


Program

Day 1. Core Concepts and Techniques

Theory

  • An introduction to machine learning tasks and definitions

  • Core principles of building machine learning algorithms

  • A diversity of machine learning algorithms: from linear regression to random forest

  • Core Python packages for machine learning

Practice

  • Linear and logistic regressions

  • k-nearest neighbors and k-means

  • Decision trees and random forest

  • Handling classification, regression, and clustering tasks

*Packages of choice are Pandas/NumPy/scikit-learn

Day 2. Advanced Algorithms

Theory

Day 2 will cover the use of advanced theoretical concepts underlying such complex models as:

  • LASSO/Ridge (regularization)

  • PCA/SVD (dimensionality reduction)

  • Advanced clustering algorithms, such as DBSCAN, expectation-maximization (different similarity approaches to data)

  • Naive Bayes (The Bayes theorem)

  • Complex ensembling schemes, gradient boosting, stacking (iterative refinement)

  • Algorithmic hyperparameter tuning

Practice

  • LASSO

  • PCA

  • DBSCAN, expectation-maximization, agglomerative clustering, mean shift

  • Naive Bayes

  • Gradient boosting machine, stacking

  • Tree-structured Parzen estimator

*Packages of choice are Pandas/NumPy/scikit-learn/HyperOpt/XGBoost

Day 3. Feature Engineering and Development Methodology

Theory

A wide range of topics related to building ML models will be covered:

  • Feature engineering

  • Dealing with missing data and outliers

  • Dealing with imbalanced classification

  • Advanced validation schemes

  • Handling of model versioning

  • CRISP-DM as a major machine learning development methodology

Practice

Feature engineering:

  • Polynomial and logarithmic features, combinations of features

  • Periodic feature encoding

  • Target encodings

Imbalanced classification:

  • Advanced metrics for classification

  • Threshold tuning

  • Over- and undersampling (SMOTE)

Missing data handling:

  • Imputation of missing values using k-nearest neighbors or decision trees

Advanced validation:

  • Cross-validation for time series

*Packages of choice are Pandas/NumPy/scikit-learn



                After participating in the course you will get a certificate of completion!


                Prerequisites

                Altoros recommends that all students have:

                - Basic Python programming skills, a capability to work effectively with data structures

                - Experience with the Jupyter Notebook applications

                - Basic experience with Git

                - A basic understanding of matrix vector operations and notation

                - Basic knowledge of statistics

                - Basic knowledge of command line operations

                All code will be written in Python with the use of the following libraries:

                - Pandas/NumPy are the libraries for matrix calculations and data frame operations. We strongly recommend to browse through the available tutorials for these packages, for instance, the official one.

                - scikit-learn

                - Matplotlib

                All these libraries will be installed using Anaconda.

                Requirements for the workstation:

                - A web browser (Chrome/Firefox)

                - Internet connection

                - A firewall allowing outgoing connections on TCP ports 80 and 443

                The following developer utilities should be installed:

                - Anaconda

                - Jupyter Notebook (will be installed using Anaconda)

                If software requirements cannot be satisfied due to the security policy of your employer, please inform us about the situation to find an appropriate solution for this issue.


                Payment info:

                If you would like to get an invoice for your company to pay for this training, please email to training@altoros.com and provide us with the following info:

                • Name of your Company/Division which you would like to be invoiced;

                • Name of the person the invoice should be addressed to;

                • Mailing address;

                • Purchase order # to put on the invoice (if required by your company).

                Please note our classes are contingent upon having 7 attendees. If we don't have enough tickets sold, we will cancel the training and refund your money one week prior to the training.Thanks for the understanding.



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                Date and Time

                Location

                Venue is being confirmed. Stay tuned!

                Dubai, 73301

                United Arab Emirates

                View Map

                Refund Policy

                Refunds up to 7 days before event

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