Machine Learning BootCamp

Course code: MLBC

This is intensive series of machine learning courses at a discounted price. No prior knowledge of machine learning is required. The package includes:

Introduction to machine learning (2 days)

This course is intended for beginners who have no or limited experience with machine learning and want to do their first steps in this field. The participants will learn what machine learning is, what types of ML are the most typical in practical applications and how the basic algorithms work. We are not going to sink into mathematical formulas or complex proofs.  Instead, we will focus on an intuitive understanding of the principles, which are necessary for the ability to design machine learning models.

Convolutional neural networks and image processing (1 day)

This workshop is for people who are looking for hands on experience with deep neural networks for image processing, but they didn’t have any real opportunity to do so yet. Through experiments, we will explore how and why such models work, what are the intuitions behind its’ functionality, and gradually, through simple examples, we’ll come to the models that are commonly used in industry. We will focus on possible use cases for neural net’s internal semantic image representation and how to visualize neural net behavior in the most effective way.

Natural Language Processing (1 day)

This course is focused on the analysis and processing of text data. We are expecting knowledge of basic principles of machine learning in the same extent as the Introduction to Machine Learning course provides. A special attention will be aimed to text preprocessing and vectorization, which is crucial for NLP. We will further focus on text classification, language modeling and text synthesis.

Time Series (1 day)

This course is focused to time series prediction problem. We begin with examples of classical methods for modeling and prediction of time series and we continue to more advanced methods based on machine learning. We finish with a complex example of training time series model on historical data using neural network and we evaluate its performance in predicting future.

700 EUR

847 EUR including VAT

The earliest date from 01.11.2022

Selection of dates
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+420 731 175 867 edu@edutrainings.cz

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Course dates

Starting date: 01.11.2022

Type: Virtual

Course duration: 5 dnů

Language: en

Price without VAT: 700 EUR

Register

Starting date: 01.11.2022

Place : Praha

Type: In-person

Course duration: 5 dnů

Language: en

Price without VAT: 700 EUR

Register

Starting date: Individual

Type: Individual

Course duration: 5 dnů

Language: en

Price without VAT: 700 EUR

Register

Starting
date
Place
Type Course
duration
Language Price without VAT
01.11.2022 Virtual 5 dnů en 700 EUR Register
01.11.2022 Praha In-person 5 dnů en 700 EUR Register
Individual Individual 5 dnů en 700 EUR Register
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Course structure


Introduction to machine learning

Day 1

  • What is machine learning?
  • Types of machine learning (classification, regression, ranking, reinforcement learning, clustering, anomaly detection, recommendation, optimization)
  • Data preparation (train, test and validation data sets, imbalanced and noisy data)
  • Classification model evaluation (accuracy, precision, recall, confusion matrix, ROC, AUC)
  • Basic algorithms for classification (baseline models, Naïve Bayes Classifier, Logistic regression, Support Vector Machines, decision trees, ensemble models)
  • Quick Scikit-Learn tutorial (how to load and transform data, training models, predicting values, model pipelines and evaluation)
  • Practical classification task
  • Basic algorithms for regression (analytical methods, gradient descent, SVR, regression trees)

Day 2

  • Basic algorithms for clustering (K-means, hierarchical clustering)
  • Practical clustering task
  • Introduction to artificial neural networks (why they are so popular, what their advantages and disadvantages are, perceptron neural network)
  • Most frequently used activation functions (Sigmoid, Linear, Tanh, Relu, Softmax)
  • Multi-Layer neural networks (back propagation algorithm, stochastic gradient descent, convolution, pooling, regularizations)
  • Quick tutorial to Keras (sequential models, optimizers, training, data workflow)
  • Practical classification and regression tasks using neural networks

Convolutional Neural Networks and Image Processing

Day 3

  • VGG 16 and ResNet
  • Transfer learning and fine-tuning
  • Image classification
  • Batch normalization and data augmentation
  • U-net and Image segmentation
  • GANs and superresolution
  • Neural network explainability
  • Adversarial patch

Natural Language Processing

Day 4

  • Introduction to natural language processing
  • Chapters from computational linguistics (corpus, tokenization, morphological, syntactic and semantic analysis, entropy, perplexity)
  • Text document vectorization (bag of words, one-hot encoding, TF-IDF)
  • Practical taks on text classification
  • Word embedding (word2vec, GloVe)
  • Introduction to language modelling (n-gram models, smoothing, neural network based language models)
  • Practical task on language modelling (implementation of a language detection algorithm based on language models)
  • Neural network based text generator

Time series

Day 5

  • Introduction to the theory of time series modeling
  • Classical methods for time series prediction (space & frequency domain, spectral analysis, autocorrelation, ARIMA models etc.)
  • Hands-on example (pandas, basic characteristics, simple prediction)
  • Machine learning for time series prediction (state-space methods, Hidden Markov Chain, Kalman filter, classical neural networks, recurrent networks, LSTM)
  • Hands-on examples of machine learning methods (training set preparation for specific task and model, training process & evaluation)
  • Complex example of time series prediction using recurrent neural network (temperature prediction from high-dimensional input data: training data set preparation, training process & validation, prediction with trained neural network)

 

Prerequisites

  • basic knowledge of programing in Python
  • high school level of mathematics

Do you need advice or a tailor-made course?

daniel

Daniel Šťastný

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