Technical Summer Classes

(July - August)

Academy Summer Program 2020

This year again we are opening up our Summer Class Program and offering the chance to everyone to join one of our Friday online classrooms and inspire you to start your data journey or expand your knowledge.

Join one of our free Academy classes for a day and develop your data knowledge. Nothing is out of reach!

We are offering a wide range of Data Science, Artificial Intelligence, Big Data, and Platform trainings. All of the classes include half a day of theory in an interactive classroom with other professionals and half a day of hands-on training. The classes are given by Anchormen data specialists whom you can ask all of your questions.

All of the classes are provided online in a virtual class room. Fill in the form and we will contact you to arrange everything. See you online!

 

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Close the data gap with our Summer Class 2020 Program

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Summer Class Program Overview

03 July - Google Cloud Platfom

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

03 July - Decision and Regression Trees (Machine Learning)

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

10 July - Neural Networks (Machine Learning)

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

10 July - SQL ETL

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

17 July - 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

17 July - Bayesian Learning (Machine 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

17 July - Graph Databases

The class starts with a discussion about Graph Theory and some properties of Graph Databases. We will explain the different types of databases, including Key-Value (REDIS), Document Stores (MongoDB, ElasticSearch), Relational (Oracle, MySQL, Postgres) and Graph (Neo4j, Janus, Neptune, Azure Cosmos). Then, we will describe the position of these databases according to the CAP Theorem.

Additionally, we will give you advise on when to use SQL or NoSQL databases, the concept of Traversals vs Joins and whether a Database Scheme is required. Next to that, we will describe the main Graph Databases available on the market.

Finally, we will focus on Graph Languages, such as Cypher, SparQL, Gremlin and GraphQL. 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:

  • Graphs
  • Databases
  • SQL versus NoSQL
  • Neo4j
  • Cypher
  • SparQL
  • Gremlin
  • GraphQL
24 July - Apache Hive (Big Data)

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

31 July - Data Architectures (Big Data)

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

07 Aug - Azure Cloud Platform

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

14 Aug - SQL ETL

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

14 Aug - Computer Vision (Machine Learning)

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

21 Aug - Natural Language Processing (Machine Learning)

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

21 Aug - Local Models (Machine Learning)

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

21 Aug - Azure Cloud Platform (Advanced)

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

28 Aug - Ensemble Models (Machine Learning)

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

28 Aug - Deep Learning (Machine 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

28 Aug - Google Cloud Platform (Advanced)

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

Company Training Courses

Do you want to receive training from the best Data Experts and boost the skills of your employees? Take a look at our Academy Training Courses:

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