Praha 4 - Nusle

MLBC - Machine Learning BootCamp

Základní informace
Přihlásit se na kurz

Popis kurzu

Jedná se o týdenní intenzivní sérii všech našich kurzů za zvýhodněnou cenu. Nejsou třeba žádné předchozí znalosti strojového učení. Balíček obsahuje:

  • Úvod do strojového učení (2 dny)
  • Zpracování přirozeného jazyka (1 den)
  • Konvoluční neuronové sítě a zpracování obrazu (1 den)
  • Časové řady (1 den)

Obsahová náplň

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, Na.ve 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

Day 3.- Natural Language Processing

  • 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

Day 4. – Convolutional neural networks and image processing

  • Back to the history
  • What the convolution is and why it works
  • TensorFlow (designing a simple convolutional neural network)
  • Practical classification task with the Fashion MNIST data set.
  • Experiments with the MSCOCO and ResNet data sets
  • Visualisations using TensorBoards
  • Image classification
  • How to deal with noisy data?

Day 5. – Time Series Analysis

  • 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)

Termíny kurzů

Otvírá se podle zájmu.

Kontakt a místo konání

Kontaktní údaje
Lukáš Vallo
e-mail:
lukas.vallo@... Zobrazit


Adresa
Táborská 619/46, 140 00 Praha 4

O organizátorovi

Kontaktní údaje
tel:
+420 724 792 *** Zobrazit

mobil:
+420 724 792 *** Zobrazit

e-mail:
edu@... Zobrazit


Adresa
EDU Trainings s.r.o.
Táborská 619/46, Praha 4 - Nusle Zobrazit na mapě

Hodnocení kurzu

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