Part 1 - Truly Practical Computer Vision Course with Real-Life Cases





Why this training?

Already familiar with classic machine learning and now ready to move to the next step? The Practical Computer Vision course will provide you with practical means of solving business-specific tasks. During this course, attendees will proceed from theory to expert-led hands-on practice that encompasses a set of real-life use cases. In addition, you can submit a use case of choice to develop the expertise needed for your current business concerns.

Who should attend?

Everyone willing to improve knowledge and learn how to resolve issues challenging for classic machine learning;

Engineers who want to gain practical expertise in computer vision and apply it to real-life business tasks.

Course objectives

Find out how powerful some of the machine learning techniques might be;

Explore the mechanisms for learning neural networks means of neural network learning process management;

Learn how convolutional neural networks enhance machine learning for spatial data processing;

Get familiar with the architectures that solve basic computer vision tasks;

Get a sample code for training your own models to fit the needs of the business.

Each trainee will have 16 hours of online Computer Vision practice with a personal trainer on the project of your choice.

Program

DAY 1

Intro to Deep Learning

Explanation of a machine learning technique that is proven to be surprisingly powerful for a wide margin of tasks. We’ll look at a surprisingly strong machine learning techniques that have become really popular recently and will cover the following topics:

Structure of neural networks, feedforward neural networks



A mechanism for learning neural networks



Means of neural network learning process control

DAY 2

Convolutional Neural Networks

Neural network architecture for image processing. Successes of convolutional neural networks were the reason for a new wave of interest in machine learning. Convolution as the core of the neural network layer for spatial data processing. Topics for the day:

Image features and representation learning



A convolution layer and a deep convolutional network



Supporting layers for convolutional neural networks



State-of-the-art architectures for image processing



Transfer learning and fine-tuning

DAY 3



Computer Vision

Computer vision drastically changed after the introduction of neural networks. In this module, we'll try to cover the basic tasks of computer vision using neural networks. During the lectures we'll cover the architectures that solve the basic of the Computer Vision tasks and cover the following topics:

Image-specific data transformations



Architectures for Object Detection tasks



Architectures for Semantic Segmentation tasks

PRACTICE

16 hours of hands-on practice

Prediction on photo data set. We will learn how to build models for detecting objects on images. Using satellite images, we’ll create a model to detect: trace segmentation, roadmap mining, ship detection.

Prediction on video data set. In this task, video material will be used to build the model. Based on a set of video clips from fishing vessels, we’ll create a fish detection model.

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.

Meet the trainer

Vladimir Starostenkov, Machine Learning Architect

Bio: Vladimir has 10+ years of experience in software development. Over the course of his career, he has been part of 15 successful project implementations. Vladimir specializes in artificial intelligence and machine learning, distributed systems design, NoSQL and Hadoop-based systems benchmarking, permissioned blockchains, data engineering, and development of data-centric apps. As an expert in NoSQL databases, he has authored a number of research papers, comparing the performance of Apache Cassandra, Redis, MongoDB, and Couchbase. Vladimir also serves as a trainer and a data evangelist. He is responsible for analyzing requirements, preparing training materials, and conducting training sessions. Vladimir is an active member of the Open Data Science Community.





Part 2 - Truly Practical Data Science Course with Real-Life Cases

Why enroll? To get:





the structured information you would otherwise have to look for in different sources;

a personal mentoring during 16 hours to solve a practical AI issue;

ready-to-use scripts as the basis for creating algorithms of your own to solve business-specific problems;

a fully-applicable template of the development life cycle, as well as recommendations for its subsequent adaptation to a changing business environment;

practical experience of building and adapting your AI models;

insights from experts who are 10+ years in AI and ready to ease your career path with their own life-hacks;





Program Details

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. 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 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 7,5 hours of practice with our Experts during the 3-Days course Each day you'll spend at least 2,5 hours practicing to solidify knowledge and polish skills.

16 hours of hands-on online practice provided by our Instructors Real Estate Price Forecasting. Using the historical data of the 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 attendee can propose a project they'd like to work on.

Prerequisites

To participate in Practical Data Science Course at least minimum experience in programming and understanding of Data Structure is enough! Although many of our attendees come with a slight experience in programming. And that's not a problem. To be confident you are ready to successfully pass the Course, we'll share some of the useful links in advance, right after the ticket purchase will be completed. So, even if not, you'll be!





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 your understanding.













