Machine Learning Bootcamp (EN)

Description

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)

Contents

• 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)

• 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

• 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

• 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

• 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