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.
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.
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
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
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.
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
- 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.
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.
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.