TRULY PRACTICAL DATA SCIENCE TRAINING WITH REAL-LIFE CASES
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WHY THIS TRAINING?
Are you willing to gain practical skills in Data Science to tackle
business tasks? Seek theoretical knowledge to be delivered in a
structured way? During this course, attendees will proceed from theory
to expert-led hands-on practice that encompasses a set of real cases
to solve. In addition, you can submit a use case of choice to develop
the expertise needed for your current business concerns.
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WHO SHOULD ATTEND?
* Engineers who want to gain expertise in machine learning tools and
frameworks
* Everyone willing to move from theory to applied knowledge across
challenging business tasks
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COURSE OBJECTIVES
* Get the structured information you would otherwise have to look
for in different sources
* Explore the machine learning–related issues the practitioners
face and the best practices to address them
* Get ready-to-use scripts as the basis for creating algorithms of
your own to solve business-specific problems
* Collect valuable insights on the complete development life cycle
of an ML solution
* Get a fully-applicable template of the development life cycle, as
well as recommendations for its subsequent adaptation to a changing
business environment
* Each trainee will have 16 hours of online Machine learning
practice with a personal trainer on the project of your choice.
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PROGRAM
DAY 1
CORE CONCEPTS AND TECHNIQUES
Comprehensive review of the concepts, methods and models on which
machine learning is based. In this module you'll learn:
*
Formal notation about ML tasks and definitions
*
Core principles of building an ML algorithms
*
Whole set ML algorithms, from Linear Regression to Random Forest
*
Introduction to core Python packages for ML
We'll cover the algorithms:
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Linear and Logistic Regression
*
kNN and k-Means
*
Decision Trees and Random Forest
We'll show how to handle classification, regression and clustering
tasks.
DAY 2
FEATURE ENGINEERING AND DEVELOPMENT METHODOLOGY
Proven to work recipes and methods that help build better models and
develop whole solution. We'll get a hold on a wide range of questions
related to building ML models, such as:
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Feature Engineering
*
Dealing with Missing Data and Outliers
*
Dealing with Imbalanced Classification
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Advanced Validation Schemes
*
Handling of Versioning of models
*
CRISP-DM as main ML development methodology
DAY 3
TABULAR DATA
Transactional data and structured data sources in general are largely
prevalent types of datasets, especially in telecom/banking. Purpose of
this module is to show an approach for this data to retrieve useful
insights.
*
Data preparation of transactional data
*
Time series specific family of algorithms
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Statistical and Neural Network approaches for this task
PRACTICE
16 HOURS OF HANDS-ON PRACTICE
*
REAL ESTATE PRICE FORECASTING. Using the historical data of the
Russian housing market along with demographic data, we will learn how
to build a model for forecasting a house price.
*
CUSTOMER INCOME PREDICTION. We propose to analyze the customer data
set in the Google Merchandise Store (also known as GStore, where
Google Swag is sold). The goal is to create a model that predicts
store revenue per customer.
*
ASSESSMENT OF LOAN APPLICATIONS. This is a classic banking task to
minimize financial risks. Using the client’s historical data, we
will build a model that predicts the probability with which the client
will return a bank loan.
*
YOUR OWN PROJECT. Each trainee can propose a project they'd like to
work on.
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_At the end of the course, all participants receive a certificate of
attendance. This certificate includes the training duration and
contents, and proves the attendee’s knowledge of the emerging
technology._
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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 [https://pandas.pydata.org/pandas-docs/stable/10min.html].
- 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.
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MEET THE TRAINER
SERGEY SINTSOV, DEVELOPER
_Bio: _Sergey is a tech-savvy software engineer with hands-on
experience in full cycle of software development.
Sergey has a Master Degree in Computer Science with specialization in
Artificial Intelligence and Theoretical Computer Science. He is
proficient in a wide range of Database Management Systems (DBMS)
architectures, participated in R&D activities related to Big Data,
DBMS and can easily operate this information in implementing databases
into complex solutions. Also, Sergey is experienced with Knowledge
bases, Knowledge processing, Knowledge-based systems, Ontological
modeling, Semantic networks and parallel computing on GPU.
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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 5 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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27/10/2019 Last update