sql practitioner

SQL Practitioner

SQL Practitioner

 

Due to the COVID-19 outbreak our training courses will be taught via an online classroom.

With this training you will receive in-depth knowledge from industry professionals, test your skills with hands-on assignments & demos, and get access to valuable resources and tools.

Even after 45+ years SQL still remains a crucial tool. During this training you will get an understanding of SQL, ETL and BI, so you can integrate your work with the rest of your organization.

The SQL classes are perfect for companies of all sizes that want to close the data gap and train their employees. You can follow the schedule below in our offices or contact us for a tailor-made program that meets your needs.

Are you interested? Contact us and we will get in touch with you.

Close the Gap with this SQL Training

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What you will learn

Participants in this training typically have roles like or comparable to Junior/trainee software engineer or data engineerIn order to get most value out of the training, participants should have a basic knowledge of Python and PySpark

During this training you will learn:

  • How to structure data and the techniques;
  • About data warehouses and the ETL processes;
  • Work with PowerBI and visualisation.

After the training you receive a Certificate of Completion. 

Download flyer SQL Practitioner training

Training Dates

This training consists of 4 Classes and the content of the classes is connected. Contact us for a tailored training.

Description of the Classes

  • Relational Databases (RDBMS) I

    In this training we will discuss basic concepts behind relational databases and learn how to structure data in a relational manner. We will take an in-depth look into the techniques of creating a well-defined relational data model. We will then learn how to create these data structures in a relational database. Data Manipulation, transactions and access management is also discussed in this training.

    The training includes theory, demos, and hands-on exercises. After this training you have gained knowledge about:

    • Relational databases in general
    • Relational Data Modeling & Normalization
    • SQL
    • Data Definition language (postgres as an example)
    • Data Manipulation language (postgres as an example)
    • Data Control language (postgres as an example)
    • Transactions
  • Relational Databases (RDBMS) II

    This training will teach you how to retrieve data using SQL. Among other things, it will cover SQL joins and aggregations, which participants must use to answer questions about a sample database containing DVD rental data during the practice session.

    The training includes theory, demos, and practical exercises. After this training you will have gained knowledge about:

    • SQL SELECT statements with filters, sorting, etc.
    • SQL joins, left, right, inner, outer (why do all of these exist?)
    • SQL aggregations
    • Indexes used to speed up query execution
    • SQL Views
    • Database connectivity (JDBC and ODBC)
  • ETL & Data Warehouses

    This training introduces participants to data warehousing and ETL (extract, transform, load) processes. Typical data warehouse schema’s such as the star and snowflake model are discussed. ETL goals and data quality concerns are covered and illustrated via hands-on exercises.

    The training includes theory and hands-on exercises. After completing this course, you will be able to do the following:

    • Understand what ETL is and what purpose it serves
    • Describe the various stages in the ETL process
    • Understand what data quality dimensions exist
    • Understand what metadata is and how it relates to the ETL process
    • Have some basic understanding of what a Data warehouse is
    • Describe what data modelling techniques are used in a typical Data warehouse
  • PowerBI

    PowerBI is a Microsoft business analytics service that delivers insights to enable fast and informed decisions.

    In this lesson, we will present the main BI tools and some data visualization essentials. Furthermore, we will work on several hands-on exercises using the PowerBI Software.

    After this training you will have gained knowledge about:

    • BI Tools
    • Data visualizations essentials
    • Power BI installation
    • Power BI demo example in detail, including:
      • General overview of PowerBI
      • Data connections
      • Pie Charts
      • Bar diagrams
      • Bubble plots
      • Time series plots
      • Data on maps
      • Drilling vs Next level
      • New column vs measure
    • Custom Visuals
    • Running R code in PowerBI
    • Running Python code in PowerBI

devops training

Applied DevOps Training

Applied DevOps  Training

Training Applied DevOps

Due to the COVID-19 outbreak our training courses will be taught via an online classroom.

With this training you will receive in-depth knowledge from industry professionals, test your skills with hands-on assignments & demos, and get access to valuable resources and tools.

During the DevOps Training you will learn about Git, CI/CD, Docker, and Kubernetes. These classes are a great way to bring you up to speed with the standards in the field. 

These classes are perfect for companies of all sizes that want to close the data gap and train their employees. You can follow the schedule below in our offices or contact us for a tailor-made program that meets your needs.

Are you interested? Contact us and we will get in touch with you.

Close the Gap with this DevOps Training

Fill in the form and we will contact you:

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What you will learn

This Applied DevOps training is a great fit if you have a background in software engineering, DevOps, data engineering or machine learning engineering, for example as starting data engineers. In order to get most value out of the practical part of the training, you should have some command line and Docker experience. Basic familiarity with Linux and the Linux command line is recommended, but not required. 

During this training you will learn:

  • The fundamentals of Git, Docker and Kubernetes;
  • DevOps practices and terminology;
  • Build, manage and integrate environments.

After the training you receive a Certificate of Completion. 

Download flyer Applied DevOps training

Training Dates

This training consists of 4 Classes and the content of the classes is connected. Contact us for a tailored program.

Detailed description of the Classes

  • Git, GitFlow & CI/CD

    During this lesson you will learn the fundamentals of Git, e.g. what makes up a commit, how Git stores files. You learn about branches, merging and rebases. These fundamentals will allow you to deal with conflicts in a more profound way. Furthermore, this lesson is filled with best practices and Git goodies like amending commits, committing single lines and cherry-picking. Understanding how Git works, allows you to fully unlock its potential, making you more productive at writing code.

    We will also look at a way of working with Git in a production environment through the method of GitFlow. This paradigm for handling releases allows teams to grow bigger and isolates features, thereby reducing interdependency. Following GitFlow your team can release faster and more often. Finally, we will dive into Continuous Integration / Continuous Deployment, which connects to GitFlow, for always be releasing updates to your project in a controlled manner.

    The training includes theory and hands-on exercises. After this training you will have gained knowledge about: 

    • Fundamentals of Git
    • Branches, merging, and rebases
    • Git best practices
    • GitFlow
    • Continuous Integration / Continuous Deployment
    • Lab session to get hands-on experience with these tools
  • Docker

    During this training you will become familiar with the world’s leading container platform – Docker. You will learn how Docker bridges the gap between operations and development teams and how Docker works ‘under the hood’. During the practical session you will build a small API that uses several Dockerized components. This practical session will teach you the basics of building Docker images and cover the most used Docker command line tools. During the training recommendations and best practices concerning using Docker for development and operations will be provided.

    The training includes theory and hands-on exercises. After this training you will have gained knowledge about: 

    • Basic introduction to Docker
    • What Docker offers developers, operations and the enterprise as a whole
    • A technical breakdown of Docker internals
    • Managing and building Docker containers
    • Docker best practices
  • Kubernetes I

    This training aims to give an overview of what Kubernetes does, how it works and how to use it in practice. It is part 1 of 2 courses on this topic.

    First, we will discuss the core principles such as containers, clusters, container dependencies, image registries, deployments, load balancing, scaling, etc before going in more detail into the main components of Kubernetes clusters, i.e. (master) nodes, pods, services and replication controllers. We then move to more practical information about how to use the kubectl command line tool, writing specification files and the Kubernetes Dashboard.

    After this theoretical overview we gain hands-on experience in a two-part lab session, in which we learn how to set up a local Kubernetes cluster and deploy applications in practice.

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

    • Recap Docker and Docker Compose
    • Container automation & orchestration
    • Container dependencies
    • Image registries
    • Automated deployments
    • Features such as load balancing, health checks, scaling and rolling updates
    • Cluster components: master, nodes, pods, services, replication controllers, labels
    • Kubectl command line
    • Specification files (yaml, json)
    • Kubernetes Dashboard
    • Lab session to get hands-on experience deploying apps in a Kubernetes Cluster
  • Kubernetes II

    In the second training we aim to go deeper into specific tools that build on top of Kubernetes.

    First, we discuss Package Management in Kubernetes using HELM. We explain the concepts of Charts, HELM Client / Tiller Server architecture and how to use them, together with Chart Repositories. Then, we go into Monitoring using Prometheus. Here, the concepts of Alerts and Targets are explained, the Prometheus architecture, its query language (PromQL) and how to use it in conjunction with the Grafana analytics platform. Next, we cover Istio, a Service Mesh, i.e. a distributed system of microservices. We delve into the fallacies of distributed computing that it aims to solve, its architecture, and core features. Finally, we will focus on Kubeflow, the Machine Learning toolkit for Kubernetes.  Each of these tools is accompanied by a lab exercise that will give us some hands-on experience.

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about the architecture, usage and (dis)advantages of:

    • HELM: package management for Kubernetes
    • Prometheus: monitoring
    • Istio: service meshes
    • Kubeflow: machine learning toolkit
    • Lab session to get hands-on experience with these tools

big data training

Applied Big Data Training

Applied Big Data Training

Training Applied Big Data

Due to the COVID-19 outbreak our training courses will be taught via an online classroom.

Not sure how to start your Big Data journey or you just want to solidify your skills? With this training you will receive in-depth knowledge from industry professionals, test your skills with hands-on assignments & demos, and get access to valuable resources and tools.

Learn how to utilize the power of Spark, Kafka and Hadoop. We will give a deep dive into the Big Data skills and technology that any Data Engineer should possess. After finishing the training program you will have an overview of and hands on experience with different data architectures. 

These classes are perfect for companies of all sizes that want to close the data gap and train their employees. You can follow the schedule below in our offices or contact us for a tailor-made program that meets your needs.

Are you interested? Contact us and we will get in touch with you.

Get in touch for more information

Fill in the form and we will contact you about the Applied Big Data training:

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What you will learn

This Applied Big Data training is a great fit if you work as software, data, or BI engineer. In order to get most value out of the training, participants should have very basic knowledge of Python and SQL.

During this training you will learn:

  • How to use Big Data processing and analytics;
  • Understanding Big Data platforms and the setup;
  • The strengths and weaknesses of the different technologies.

After the training you receive a Certificate of Completion. 

Download flyer Applied Big Data training

Training Dates

This Applied Big Data training consists of 9 classes which are spread over a couple of months to ensure the maximum learning curve. The content of the classes is connected, and in general we advise to attend all classes. In case you would like to attend (a) single class(es), contact us so we can give you the right advice about a tailored training course. 

Detailed description of the Classes

Click below to open a detailed description of the class: 

  • Hadoop Essentials

    The creation of Hadoop is often considered as the start of the Big Data ‘movement’. What problems does Hadoop tackle and how? This training provides insights into the software and its most important technologies.

    This training includes theory and practical exercises. After this training you will have gained knowledge about:

    • The Hadoop ecosystem
    • Data storage (HDFS)
    • Cluster resource management (YARN)
    • Data processing (MapReduce and Spark).
    • Data pipelines in Hadoop (Oozie)
  • Spark I

    In this first Apache Spark training we will introduce basic Spark concepts and the Resilient Distributed Datasets (RDD) API that is core to Apache Spark. During the practical session participants will use RDD API from Python to analyze a MovieLens dataset.

    The training includes theory and hands-on exercises. After this training you will have gained knowledge about:

    • Spark concepts, roots and history

    • How Spark relates to Hadoop

    • How Spark solves challenges in concurrent and parallel programming

    • Spark RDDs and the RDD API

    • Spark deploy modes

  • Spark II

    In the second Apache Spark training you will be introduced to Spark’s Dataframe API and Spark SQL. These APIs are optimized for dealing with structured data, tabular data, and allow SQL access to very large datasets. During the practical session participants will be introduced to the APIs and then work on analyzing MovieLens dataset using Spark SQL. 

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge on 

    • Spark’s Dataframe API 
    • Spark SQL 
    • The Parquet storage format  
  • Spark III

    In the third Apache Spark training you will be introduced to Machine Learning concepts with Spark’s MLlib API as well as how to apply them at scale. During the practical sessions participants will work on a Recommender System and on predicting airplane delays.

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

    • Basic machine learning concepts
    • Spark MLlib
    • Pipelines in Spark
    • Building a basic Recommender System in Spark
    • Using Spark and machine learning for predictions
  • Spark IV

    In the fourth Spark training you will be introduced to Spark’s structured streaming APIs. Participants are introduced to streaming concepts such as event time, late data, windowing, and watermarking. During the practical session participants will solve several streaming queries regarding order (sales) data using Spark and Kafka. 

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:  

    • Previous and current streaming APIs in Spark 
    • Spark structured streaming data model 
    • Considerations concerning streaming query output modes 
    • Event time and late data 
    • Windowing and watermarking to solve late data issues 
    • Hands-on solving structured streaming queries 
  • Kafka

    The Kafka training aims to provide an overview of the Apache platform. Participants will learn about Kafka terminology and how Kafka provides a scalable solution for decoupling data streams. Topics such as partitioning and message guarantees will be addressed. During the practical session participants will use a Dockerized Kafka broker to explore basic consuming and producing followed up by a more complex change data capture (CDC) scenario.

    The training introduces Kafka concepts and theory followed up by hands-on exercises. After this training you will have gained knowledge about:

    • The problems Kafka solves
    • Kafka terminology and internals
    • Partitioning and scaling Kafka
    • The various message guarantees provided by Kafka
    • Kafka security and ACL options
    • Schemas and schema registry
    • Basic Kafka consuming and producing
    • Change data capture and Kafka
  • Streaming

    The (software) world is becoming more and more event based, which translates into data processing moving from a batch to a streaming paradigm. This class covers several platforms that deal with streaming data.

  • Apache Hive

    Apache Hive allows you to use SQL to query files in (distributed) file systems which is a cornerstone of many big data platforms. This training provides insights into the different Hive components and how to use them to query JSON, CSV, Parquet (…) files. 

    The training includes theory and hands-on exercises. After this training you will have gained knowledge about:

    • Hive Metastore – holds table definitions
    • Hive Query Engine – used to perform SQL queries
    • Ways to optimize table layout using data partitioning and clustering.
    • Spark integration with Hive
    • Impala – the SQL engine in Cloudera Hadoop
  • Data Architectures

    Learn how to setup different (big) data architectures and the design principles behind them and the trade-offs between them.

cloud platform fundamentals

Cloud Platform Fundamentals

Cloud Platform Fundamentals

 

Due to the COVID-19 outbreak our training courses will be taught via an online classroom.

Are you wondering which is the best Cloud Platform for the data strategy of your business? Join our 3-day Cloud Platform training in which we cover the big three.

As datasets have grown more rapidly than (local) computing power and storage during the past decade, cloud infrastructure has become more and more important for scalability. This is where Amazon (AWS), Microsoft (Azure) and Google (GCP) come in. But which one is best for your needs?

These classes are perfect for companies of all sizes that want to close the data gap and train their employees. You can follow the schedule below in our offices or contact us for a tailor-made program that meets your needs.

Are you interested? Contact us and we will get in touch with you.

Close the Gap with this Cloud Platform training

Fill in the form and we will contact you:

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What you will learn

Participants in this training typically have a background in software or data engineering or IT architect. In order to get most value out of the practical part of the training, you should understand the fundamentals of file storage, databases and virtual machines and have some command line experience (ssh connections, keys, etc). Basic knowledge about machine learning workflow comes in handy. 

During the training you will learn: 

  • The foundation to build a data platform in AWS, Azure and GCP;
  • Setting up storage, access management and monitoring; 
  • Apply Data and Machine Learning technologies in public cloud. 

After the training you receive a Certificate of Completion. 

Download Flyer Cloud Platform Training

Training Dates

This training consists of 3 Classes. Join the Cloud Platform Training by contacting us for a tailored training.

Detailed description of the Classes

  • AWS Cloud

    This training focuses on the Amazon corner of the cloud universe and aims to give an overview of their most important services and their relevance for Data Science and Machine Learning.

    We start the theoretical part of this training by going into the history and background of Cloud Infrastructure in general, and Amazon Web Services in specific. Then, we discuss their solutions for access management, storage, compute, monitoring, etc before moving to Machine Learning services such as Sagemaker (ML), Rekognition (Image and Vision) and Comprehend (NLP) and concluding by mentioning some interesting others.

    Having learned about this ecosystem of services we then gain hands-on experience during the lab session, in which we tie multiple components together and eventually train a prediction model for beer preferences based on a dataset of customer reviews.

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

    • Cloud Infrastructure history and background
    • Amazon Web Services background
    • Data center regions
    • IAM: Access management
    • S3: Simple Storage Service
    • EC2: Elastic Compute Cloud
    • Lambda: Serverless Compute
    • Cloudwatch: Monitoring
    • API gateway
    • Sagemaker: Machine Learning
    • Rekognition: Image and Video
    • Comprehend: Insights and relationships in text
    • Other services such as: Polly (Text to Speech), Transcribe (Speech Recognition) and Translate
    • Lab session to get hands-on experience with Amazon Cloud infrastructure
  • Google Cloud Platform GCP

    This training focuses on the Google corner of the cloud universe and aims to give an overview of their most important services and their relevance for Data Science and Machine Learning.

    We start the theoretical part of this training by going into the history and background of Cloud Infrastructure in general, and Google Cloud Platform in specific.  During this lesson on Google Cloud Platform you will learn about several topics that touch upon what a Machine Learning Engineer needs. We will do some data processing with Apache Beam, train and deploy a model via Kubeflow and publish the results on Pub/Sub.

    Having learned about this ecosystem of services we then gain hands-on experience during the lab session, in which we tie multiple components together and eventually train a prediction model for beer preferences based on a dataset of customer reviews.

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

    • Cloud Infrastructure history and background
    • GCP background
    • Apache Beam
    • Kubeflow
    • Pub/Sub
    • Lab session to get hands-on experience with Google Cloud Platform infrastructure
  • Microsoft Azure

    Microsoft Azure is one of the most popular cloud computing services today, offering dozens of capabilities ranging from storage and databases to scalable data processing. This training provides an overview of the Azure landscape with a focus on IaaS, PaaS and Serverless services like VM’s, Networking (IaaS), storage, databases, serverless functions, and more. During the training, participants set up their own Virtual Machine and build a small data pipeline which automatically rescales images put in storage.

    The training includes theory and practical exercises. After this training you will have gained knowledge about:

    • Azure datacenters (regions and availability zones)
    • Azure portal
    • Virtual Machines (VM’s) and virtual networks
    • Storage accounts with redundancy, tiering, and pricing options
    • Databases as a service (DBaaS), both SQL and NoSQL
    • Azure (serverless) apps
    • Azure container services

recommender systems

Recommender Systems Specialization

Recommender Systems Specialization

Due to the COVID-19 outbreak our training courses will be taught via an online classroom.

With this training you will receive in-depth knowledge from industry professionals, test your skills with hands-on assignments & demos, and get access to valuable resources and tools.

Amazon works with it on a daily basis for years: “Customers who bought this, also bought….”. Facebook and LinkedIn give you friend suggestions. Netflix recommends you what to watch. Closer to home, Bol.com and Coolblue try to tempt you into buying similar products when you browse their web shops. All of this is the work of Recommender Systems. An essential tool for companies that strive to offer personalization on a global scale. A good Recommender System will improve engagement, make people feel at home on your website and help them shop more.

The Recommender System training is perfect for companies of all sizes that want to close the data gap and train their employees. You can follow the schedule below in our offices or contact us for a tailor-made program that meets your needs.

Close the Gap with the Recommender Systems training

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Who should take this course?

Participants in this training typically have a background in a quantitative subject, analytics or are junior/medior data scientists, who have the ambition to dive deeper into specific machine learning algorithms and techniques.

In order to get most value out of the training, you have basic experience with machine learning in Python. You should have knowledge about the machine learning workflow and familiarity with concepts such as overfitting/underfitting, scoring metrics and parameter tuning. If you previously followed the Basic Machine Learning training these prerequisites are satisfied.

After the training you receive a Certificate of Completion. 

Training Dates

This training consists of 1 lesson. Contact us for a tailored training.

Description of the training

We will start with a general introduction, potential applications, and prerequisites for Recommender Systems.  We then delve into the different types of models – Popularity-based baseline models, Content-based models, Collaborative Filtering models, and Hybrid models.

After that, we will do several lab exercises where we will apply two different types of Recommender Systems to the MovieLens dataset with movie ratings and compare how they perform.

The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

  • Applications of Recommender systems
  • Required data and common considerations
  • Types of Recommender systems
  • Popularity model
  • Content-based models
  • Collaborative filtering
  • Hybrid models
  • Matrix factorization methods
  • Dealing with changing contexts
  • Exploitation vs exploration trade-off
  • Performance evaluation: offline & A/B testing
  • Evaluation metrics such as precision@k and recall@k
  • Training and evaluating Recommender systems with LightFM

After this training you receive a Certificate of Completion. 

nlp specialization

NLP Specialization

Natural Language Processing Specialization

Due to the COVID-19 outbreak our training courses will be taught via an online classroom.

With this training you will receive in-depth knowledge from industry professionals, test your skills with hands-on assignments & demos, and get access to valuable resources and tools.

Natural Language Processing (NLP) has grown into a large subfield of Machine Learning encompassing a rich set of useful techniques. This training aims to give a good introduction of this field, its techniques and common language models.

The NLP training is perfect for companies of all sizes that want to close the data gap and train their employees. You can follow the schedule below in our offices or contact us for a tailor-made program that meets your needs.

Are you interested? Contact us and we will get in touch with you.

Close the Gap with this NLP Specialization

Fill in the form and we will contact you:

* These fields are required.

Who should take this course?

Participants in this training typically have a background in a quantitative subject, analytics or are junior/medior data scientists. Additionally, they should have the ambition to dive deeper into specific machine learning algorithms and language-based techniques. 

In order to get most value out of the training, you have basic experience with machine learning in Python. You should have knowledge about the machine learning workflow and familiarity with concepts such as overfitting/underfitting, scoring metrics and parameter tuning. If you previously followed the Basic Machine Learning training these prerequisites are satisfied.

After the training you receive a Certificate of Completion. 

Training Dates

This NLP specialization consists of 1 full day of theory and training. This training is given in our offices on specific dates. We can also tailor the training and dates if you have specific needs. 

Description of the training

We start off by giving a broad introduction on NLP and its potential applications, before describing the most important preprocessing and feature extraction techniques, such as tokenization, stemming, N-grams, tf-idf and Part Of Speech tagging. This gives us a good foundation before discussing several common models for NLP that incorporate relationships between words, such as Bayesian Models and Word Embeddings.

The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

  • NLP and potential applications, such as sentiment analysis, machine translation and author profiling
  • Feature extraction
  • Several Models for NLP
  • Hands-on experience with NLP basic feature extraction techniques and building an Author Profiling model

After the training you receive a Certificate of Completion. 

computer vision specialization

Computer Vision Specialization

Computer Vision Specialization

Due to the COVID-19 outbreak our training courses will be taught via an online classroom.

With this training you will receive in-depth knowledge from industry professionals, test your skills with hands-on assignments & demos, and get access to valuable resources and tools.

Computer Vision has traditionally played a large role in applications such as image segmentation, compression, feature extraction, denoising and OCR. More recent developments in Deep Learning, and specifically in Convolutional Neural Networks, have been inspired by Computer Vision, thereby taking it to the next level. As part of a module on machine learning algorithms, this training will provide a thorough foundation of Computer Vision techniques.

The Computer Vision training is perfect for companies of all sizes that want to close the data gap and train their employees. You can follow the schedule below in our offices or contact us for a tailor-made program that meets your needs.

Close the Gap with this Computer Vision training

Fill in the form and we will contact you:

* These fields are required.

Who should take this course?

Participants in this training typically have a background in a quantitative subject, analytics or are junior/medior data scientists, who have the ambition to dive deeper into specific machine learning algorithms and techniques. In order to get most value out of the training, you should have basic experience with programming in Python. Furthermore, basic knowledge of mathematics is desiredspecifically on calculus (derivatives). Some experience in dealing with git repositories can come in handy for obtaining the course materials. 

After the training you receive a Certificate of Completion. 

Training Dates

This training consists of 1 lesson. Contact us for a tailored training. 

Description of the training

This training starts off by giving a good introduction on what Computer Vision is, its applications and its relation to the human visual system. We then move to the theoretical part in which we learn how digital images are represented and processed, using basic operations such as thresholding, convolutions and filters.

These concepts will come together in the second part of the training, in which we discuss Canny Edge Detection and its relation to concepts such as Gaussian kernels, gradient computation, non-max-suppression and hysteresis. We then dive into Scale Invariant Feature Transformation (SIFT), a powerful technique for identifying key feature points, often used in image stitching, and conclude by providing a list of commonly used Computer Vision tools and resources.

The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

  • Computer Vision and its applications
  • Reading digital images and their representation
  • Transformation operations such as thresholding
  • Filters and convolutions
  • Gradients computation
  • Non-max-suppression
  • Canny Edge Detection
  • Scale Invariant Feature Transform (SIFT)
  • Computer Vision tools

After this training you receive a Certificate of Completion. 

Basic Machine Learning Training

Basic Machine Learning Training

Basic Machine Learning Training

 

Training Basic Machine Learning

Due to the COVID-19 outbreak our training courses will be taught via an online classroom.

Not sure how to start your Machine Learning journey or you just want to solidify your skills? With this training you will receive in-depth knowledge from industry professionals, test your skills with hands-on assignments & demos, and get access to valuable resources and tools.

The Basic Machine Learning Training consists of 7 classes which cover Machine Learning fundamentals in Python with scikit-learn, Bash scripting with Linux, Data Handling & Visualization with R, and Statistics. 

These classes are perfect for companies of all sizes that want to close the data gap and train their employees. You can follow the schedule below in our offices or contact us for a tailor-made program that meets your needs.

Are you interested? Contact us and we will get in touch with you.

Get in touch for more information

Fill in the form and we will contact you about the Basic Machine Learning training:

* These fields are required.

What you will learn

This Basic Machine Learning training is a great fit if want to learn the basics to work as Data/Machine Learning Engineer or Data Scientist. Participants get most value out of the training when they have a background in analytics, mathematics and statistics.

During this training you will learn:

  • The leading Machine Learning technologies;
  • How to work with Python, R, Bash, scikit-learn;
  • The strenghts and limitations of the different technologies.

After the training you receive a Certificate of Completion. 

Download Flyer Basic Machine Learning Training

Training Dates

The Basic Machine Learning training consists of 7 classes which are spread over a couple of months to ensure the maximum learning curve. The content of the classes is connected, and in general we advise to attend all classes. In case you would like to attend (a) single class(es), contact us so we can give you the right advice about a tailored training course. 

Detailed description of the Classes

Click below to open a detailed description of the class: 

  • Linux CLI

    Regardless of your OS of choice, knowing how to deal with Linux through the command line is a valuable skill to have for any engineer or scientist. To look under the hood of the application your deployed, to debug that job you had running on one of those nodes that keep crashing, or to simply prepare this dataset that will take longer to download, transform and upload again, being able to utilize the power of Bash will not only often save you, it will actually speed your work up! As with any power tool, it is of course also very easy to cut off your own foot, so join us on this journey towards getting to know Bash and unlocking its power.

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

    • Some concepts behind Linux
    • Everyday Bash tools
    • Tricks that will make Bash use easier
    • Basic Bash scripting
  • Data Preprocessing

    As Data Scientists and Machine Learning experts spend a decent amount of time preprocessing, this topic is a necessary part in their toolkit. In this training we specifically focus on the pandas library, which has grown into one of the main tools for data preprocessing and exploration in Python, with many capabilities.

    We start off with an introduction to preprocessing, the concept of tidy data and some useful techniques such as pivoting and missing value imputation. Then, we go into the pandas library, its background, data structures, and basic features. In a demo we get to see concrete ways to handle data sets, from loading, subsetting, merging, etc to (re)sampling, applying grouped transformations and saving results.

    The training includes theory, demos, and hands-on exercises. After this training you have gained knowledge about:

    • The pandas library
    • Data structures: dataframes, series
    • Tidy data
    • Loading and saving data
    • Data exploration
    • Plotting time series
    • Useful transformation techniques
    • Merging, selecting, sorting, sampling
    • Missing value imputation
    • Grouped operations
    • Long/wide conversions
    • Advantages and limitations of pandas
  • Python ML Basics I

    This training provides a theoretical introduction into the basics of Machine Learning and its different sub-fields, as well as a hands-on way of seeing how it is applied in practice. At the core of this training is the scikit-learn library, one of the most powerful and versatile tools for Machine Learning in Python. 

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

    • Machine Learning, its goals and potential applications
    • Different types of Machine Learning: supervised, unsupervised and reinforcement learning
    • Classification and regression problems
    • Techniques such as clustering and dimensionality reduction
    • A minimal example workflow of a prediction model in scikit-learn
    • Splitting datasets into training and test sets
    • How to train, predict and score a classification prediction model
    • The standard interfaces of scikit-learn classes
    • Transformers and estimators in scikit-learn
    • Some of the machine learning algorithms you’ll have at your disposal, such as k-nearest neighbors, logistic regression, support vector machines, neural networks, etc.
  • Python ML Basics II

    In this training, we build upon what we have learned previously, and expand our workflow by showing how to optimize prediction models using Parameter Tuning. We discuss how and why to perform Cross-Validation and how to prevent Information Leakage. Bringing everything together, we finally show how to combine multiple steps of a machine learning workflow into Pipelines, thereby making the process more organized, efficient and less error-prone.

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

    • Cross-Validation
    • Commonly used Cross-Validation strategies
    • The importance of a validation set
    • Information leakage
    • Workflow of grid search and cross validation
    • Standard interfaces of the GridSearchCV class
    • Pipelines and their role in combining transformers and estimators

     

  • Python ML Basics III

    In this training, we build upon what we have learned previously, and expand our knowledge of how to score machine learning models, discuss common pitfalls and show how to deal with them. We will do this by first examining the concepts of bias, variance, overfitting and underfitting, followed by diving into important performance metrics such as accuracy, precision, recall, F1 scores, ROC curves, etc for classification problems and elaborating on commonly used metrics for regression. This last part in our basic toolkit allows us to properly assess a prediction model that we train to recognize images of handwritten digits during the hands-on lab session.

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

    • Overfitting, underfitting. bias-variance tradeoff
    • Model evaluation in practice using sci-kit learn
    • Evaluation metrics for classification, such as accuracy, precision, recall, F1, area under curve
    • Interpreting confusion matrices, classification reports and ROC curves
    • Decision function and classification probabilities
    • Dealing with unbalanced datasets
    • Evaluation metrics for regression, such as MAE, RMSE, R^2
  • Data Handling & Visualization with R

    R has grown into a well developed ecosystem with powerful packages for data analysis, data visualization, in-depth statistics, time series forecasting and machine learning, to mention a few. This training aims to give a quick-paced introduction of R, its most relevant features and basic workflow, including understanding how to apply them.

    We start the training by discussing the basics of the R Programming language and its RStudio IDE, to understand its logic operations, data structures, workflow, etc. We then delve into a number of powerful packages such as dplyr, ggplot2, readr and other tidyverse packages and show how they are used for data preprocessing, analysis and visualization.

    Finally, we apply these concepts and tools in practice during a hands-on lab session. We implement a complete data analysis workflow in R, from retrieving realtime earthquake data from a webservice to preprocessing, analyzing and eventually visualizing this data on an interactive map.

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

    • R Programming Basics
    • Packages: dplyr, ggplot2, readr, tidyverse, etc.
    • Working with Rmarkdown notebooks
    • Tips & conventions
    • Lab session to get hands-on experience with a complete data analysis workflow in R
  • Basic Statistics & Math

    This training serves as a basic introduction to statistics.

    We will first discuss a number of core concepts of statistics, from random variables, probabilities and distributions to expectation values, variance and conditional probabilities. We will show a couple of common distributions and examples to clarify these concepts. Then, we will go into statistical modelling with a  focus on linear regression. We conclude with some common metrics for regression and by talking about uncertainties in estimates.

    In the lab exercises we then get to apply these concepts and do some modelling ourselves. The training includes theory, demos, and hands-on exercises. After this training you have gained knowledge about:

    • Basics of statistics and statistical modelling
    • Random variables
    • Probabilities, probability density functions, probability mass functions
    • Standard distributions such as Normal Distribution, Student’s T Distribution
    • Expectation and variance
    • Conditional probabilities
    • Statistical modelling definition and notations
    • Linear regression
    • Least squares estimate
    • Metrics: R^2, adjusted R^2, residual standard error
    • Confidence intervals, statistical tests
    • Lab sessions to get hands-on experience applying this knowledge

     

Advanced Machine Learning Training

Advanced Machine Learning Training

Advanced Machine Learning Training

Training Advanced Machine Learning

Due to the COVID-19 outbreak our training courses will be taught via an online classroom.

Receive in-depth knowledge from industry professionals, test your skills with hands-on assignments & demos, and get access to valuable resources and tools.

The Advanced Machine Learning training provides a deep dive into several Machine Learning topics and methods. After this training you will be able to use ML methods and models, assess in which situation they can be applied, and understand the power and limitations of each of them. 

These classes are perfect for companies of all sizes that want to close the data gap and train their employees. You can follow the schedule below in our offices or contact us for a tailor-made program that meets your needs.

Are you interested? Contact us and we will get in touch with you.

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What you will learn

The Advanced Machine Learning training is a great fit for people with a good foundation and experience with data science and/or machine learning who want to dive deeper into advanced ML technologies. 

During this training you will learn:

  • how to put machine learning methods and models into practice;
  • which method or model to use in any situation;
  • the strengths, weaknesses, and limitations of ML technologies.

After the training you will receive a Certificate of Completion. 

Download flyer Advanced Machine Learning Training

Training Dates

This Advanced Machine Learning training consists of 7 classes which are spread over a couple of months to ensure the maximum learning curve. The content of the classes is connected, and in general we advise to attend all classes. In case you would like to attend (a) single class(es), contact us so we can give you the right advice about a tailored training course. 

Detailed description of the Classes

Click below to open a detailed description of the class: 

  • Decision & Regression trees

    With this training you will be introduced to Decision Trees, how they are constructed, and what algorithms you need. Furthermore, you will learn how to optimize them and why this is necessary. Finally, we will explain how these concepts are extended to their regression equivalents, using regression trees. In the end, we will implement a decision tree algorithm from scratch and then apply a scikit-learn decision tree to the Shuttle dataset provided by NASA.

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

    • Understanding high dimensional data
    • Decision Tree basics
    • Decision nodes, leaf nodes
    • Entropy, Information Gain and Gini impurity
    • ID3, CART algorithms
    • Optimizing decision trees
    • Regression trees
    • Advantages and disadvantages
    • Lab sessions to get hands-on experience applying this knowledge
  • Neural Networks

    This training will provide a thorough foundation of the topic of Neural Networks. We will start off with an intuitive explanation of neural networks by drawing parallels to neurons and the brain, before moving to a theoretical explanation of the structure of Neural Networks. From their simplest building blocks called perceptrons, to multi-layer neural networks and gradually explaining additional components we gain a good conceptual understanding of how a signal travels forward through the network. Then, we dive deeper into how the network is trained to learn an objective, using techniques such as gradient descent and backpropagation. Furthermore, practical considerations are discussed, such as network design choices, tackling common issues and ways to optimize the network.

    This theory will be interspersed with three hands-on lab sessions, in which we first implement a perceptron building block from scratch, then train a neural network to recognize handwritten digits using the Keras library, and finally learn how to implement backpropagation in Numpy.

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

    • Perceptrons, the building blocks of neural networks
    • Neural Networks as Multi Layer Perceptrons
    • Activation functions
    • Feed forward mechanism
    • Training: minimizing the loss function using gradient descent and backpropagation
    • Neural network design: architectures, loss functions, learning rate
    • Common issues such as over/underfitting and ways to counteract them
    • Optimizers such as Stochastic Gradient Descent and ADAM
    • Potential applications
    • Lab sessions to get hands-on experience on perceptrons, Keras models and backpropagation
  • Bayesian Learning

    Bayesian Learning takes a statistical approach towards modelling, putting emphasis on probability distributions and uncertainty of outcomes. This training is more statistics focused and forms a good basis for understanding Generative Algorithms such as Naïve Bayes, in which prior beliefs are updated based on observed data (in contrast to Discriminative Algorithms).

    We start with an introduction on the frequentist versus the Bayesian approach in determining probabilities and motivate the use of prior beliefs in the Bayesian way of thinking. Revisiting Bayes Theorem and probability distributions we explain Bayesian parameter estimation and finally arrive at the Naïve Bayes algorithm, as a classifier based on Bayesian Learning.

    Having learned the theoretical foundation, we combine these concepts in the hands-on lab session by implementing a Naïve Bayes classifier in Python and using it on the Pima Nation diabetes dataset in order to predict proneness to diabetes.

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

    • Frequentist vs Bayesian thinking
    • Bayes’ theorem
    • Prior and posterior probability distributions
    • Parameter estimation
    • Naïve Bayes algorithm
    • Lab sessions to get hands-on experience on Bayesian Estimation
  • Local Models

    This training focuses on distance or density-based machine learning models that primarily take into account the local character of data points. Encompassing Density Estimation, Nearest Neighbors and Clustering, these algorithms are necessary tools in our Machine Learning toolkit.

    We start by introducing the topic of Local Models and their applications in supervised as well as in unsupervised machine learning. We then discuss the relevance of different distance metrics and normalization methods, before delving into Density Estimation methods such as Kernel Density Estimation as well as parametric alternatives. Continuing to nearest neighbors algorithms like k-Nearest Neighbors and Approximate Nearest Neighbors, we finally arrive at unsupervised methods for Clustering, such as k-Means, Expectation Maximization and DBSCAN. This theoretical knowledge is applied in practice during a two-part lab session. 

    The training includes theory, demos, and hands-on exercises. After this training you have gained knowledge about:

    • Distance Metrics
    • Normalization
    • Density Estimation
    • k-Nearest Neighbors
    • Approximate Nearest Neighbors
    • Clustering
    • k-Means
    • Expectation-Maximization
    • Hierarchical Clustering
    • DBSCAN
  • Ensemble Models

    As Machine Learning encompasses a large set of different algorithms, many of them (if not all) suffer from high bias or variance. Ensemble Learning aims to reduce bias and/or variance using methods such as bagging, boosting and stacking, thereby combining weak learners into stronger ones.

    We first revisit the Bias-Variance Tradeoff and give a good motivation for how Ensemble Learning tries to address this. We then discuss Bootstrap Aggregating (bagging), it’s role in reducing variance and how it is implemented in Random Forests. Continuing to Boosting, we explain how it aims to tackle issues of too high bias and discuss implementations like Adaboost and Gradient Boosting. We address how model performance can be improved using Stacking, and when this generally works best. We conclude with an overview of techniques and their advantages/disadvantages.

    Having learned the theory, we apply these methods in practice during a lab exercise, thereby giving more understanding about all three methods, i.e. Stacking, Bagging and Boosting.

    The training includes theory, demos and hands-on exercises. After this training you have gained knowledge about:

    • Combining algorithms
    • Bias-Variance Trade-off
    • Bagging (bootstrap aggregating)
    • Majority Voting
    • Random Forests
    • Boosting
    • Adaboost & Gradient Boosting
    • Stacking
  • Problem Solving & Optimization

    Problem Solving & Optimization encompasses a broad range of techniques and subfields, that can be applied to many real-world applications. This training aims to provide a thorough overview of these techniques and valuable knowledge about how they can be applied, in machine learning as well as beyond. 

    We will provide an overview of different groups of optimization methods, from ‘exact’ methods such as Mathematical Programming and Gradient-Based Optimization to heuristics methods like Simulated Annealing and Evolutionary Computation, spending more time on the most important ones. Finally, we discuss the most common challenges in optimization, e.g. local vs global optima, the exploration vs exploitation tradeoff and tuning, before concluding with some practical notes on specialized solvers and good use cases.

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

    • Problem solving contexts
    • Solution representations, constraints and objective functions
    • Mathematical programming
    • Gradient-based optimization
    • Black box optimization
    • (Meta)heuristics
    • Simulated Annealing
    • Tabu search
    • Evolutionary Computation
    • Local vs global optima
    • Exploration vs Exploitation tradeoff
    • Tuning
    • Specialized solvers
    • No free lunch theorem
  • Deep Learning

    As part of a module on machine learning algorithms and as a follow-up on the Neural Networks training, this training will provide a good theoretical overview on the topic of Deep Neural Networks.

    Starting with an extensive introduction into the history and background of Deep Learning we get an understanding of the obstacles and subsequent breakthroughs in (deep) neural networks and a broad overview of where the field is currently at. Moving more into the theory, we first give a short recap of neural networks basic concepts, before recognizing common issues that particularly occur in deep neural networks and subsequently discussing ways to overcome them. Additionally, we describe different kinds of deep neural networks, such as Restricted Boltzmann Machines, Deep Belief Nets before arriving at Convolutional Neural Networks. Finally, we summarize advantages and disadvantages of applying Deep Learning and mention commonly used deep learning frameworks, such as Keras and TensorFlow.

    The training includes theory, demos, and hands-on exercises. After this training you will have gained knowledge about:

    • Deep Learning history and background
    • Applications
    • Neural networks recap: activation functions, loss functions, architectures, backpropagation, optimizers
    • Common issues, such as vanishing gradients, overfitting and tuning the learning rate
    • Methods to prevent overfitting, such as regularization, early stopping, dropout, sparse connectivity and data augmentation
    • Different kinds of deep neural networks
    • Restricted Boltzmann Machines
    • Deep Belief Nets
    • Stacked Denoising Autoencoder
    • Convolutional Neural Networks
    • Transfer learning
    • Software stack: Keras, TensorFlow/Theano, etc.
    • Advantages/disadvantages