Machine Learning BootCamp

Kód kurzu: MLBC

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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

840 EUR s DPH

Najbližší termín od 01.11.2022

Výber termínov

Odborní
certifikovaní lektori

Mezinárodne
uznávané certifikácie

Široká ponuka technických
a soft skills kurzov

Skvelý zákaznicky
servis

Prispôsobenie kurzov
presne na mieru

Termíny kurzov

Počiatočný dátum: 01.11.2022

Forma: Virtuálna

Dĺžka kurzu: 5 dní

Jazyk: en

Cena bez DPH: 700 EUR

Registrovať

Počiatočný dátum: 01.11.2022

Miesto konania: Praha

Forma: Prezenčná

Dĺžka kurzu: 5 dní

Jazyk: en

Cena bez DPH: 700 EUR

Registrovať

Počiatočný dátum: Individuálny

Forma: Individuálna

Dĺžka kurzu: 5 dní

Jazyk: en

Cena bez DPH: 700 EUR

Registrovať

Počiatočný
dátum
Miesto
konania
Forma Dĺžka
kurzu
Jazyk Cena bez DPH
01.11.2022 Virtuálna 5 dní en 700 EUR Registrovať
01.11.2022 Praha Prezenčná 5 dní en 700 EUR Registrovať
Individuálny Individuálna 5 dní en 700 EUR Registrovať
G Garantovaný kurz

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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)

 

Predpokladané znalosti

Táto časť nie je lokalizovaná

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

Potrebujete poradiť alebo upraviť kurz na mieru?

daniel

Daniel Šťastný

pruduktová podpora

Naväzujúce kurzy

Introduction to machine learning en cz/sk

Dodávateľ: EDU Trainings

Oblasť: Strojové učenie

Cena od:

300 EUR bez DPH

Convolutional neural networks and image processing en cz/sk

Dodávateľ: EDU Trainings

Oblasť: Strojové učenie

Cena od:

155 EUR bez DPH

Natural Language Processing en cz/sk

Dodávateľ: EDU Trainings

Oblasť: Strojové učenie

Cena od:

155 EUR bez DPH

Time Series Analysis cz/sk en

Dodávateľ: EDU Trainings

Oblasť: Strojové učenie

Cena od:

155 EUR bez DPH

Machine Learning with R cz/sk

Dodávateľ: EDU Trainings

Oblasť: Strojové učenie

Cena od:

499 EUR bez DPH

Data Manipulation and Visualization with R cz/sk

Dodávateľ: EDU Trainings

Oblasť: Strojové učenie

Cena od:

499 EUR bez DPH

Data modelling in Power BI cz/sk en

Dodávateľ: EDU Trainings

Oblasť: Strojové učenie

Cena od:

399 EUR bez DPH

Interpretable machine learning (R/Python) cz/sk

Dodávateľ: EDU Trainings

Oblasť: Strojové učenie

Cena od:

399 EUR bez DPH