Truly Practical Data SCIENCE TRAINING WITH REAL-Life CASES 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. 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 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.
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: 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: Feature Engineering Dealing with
Missing Data and Outliers Dealing with Imbalanced Classification
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 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. 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. 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 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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15/10/2019 Last update