Machine Learning Bootcamp (EN)

Jiří Materna

Kurzem Vás provede Jiří Materna

Základní info

Popis kurzu

This is a weekly intensive series of all our courses at a discounted price.

The package contains:

  • Introduction to machine learning (2 days)

  • Convolutional neural networks and image processing (1 day)

  • Natural Language Processing (1 day)

  • Time Series (1 day)

Obsah kurzu

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

Day 3

  • 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

  • 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

  • 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)


No previous knowledge of machine learning is required.

Machine Learning Bootcamp (EN)

Vybraný termín:


17 990 Kč + 21% DPH

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