Courses on a specific date with a live lecturer

Term

Language

Place

Form

Price without VAT

14. - 22. 10. 2024

Language

Place

Praha

Form

classroom

Code of the course: PU21110288-0019

Price without VAT

21 990 Kč

Order

14. - 22. 10. 2024

Language

Place

online

Form

virtual classroom

Code of the course: PU21110288-0020

Price without VAT

21 990 Kč

Order

Open term

Language

Place

Praha

Form

classroom

Code of the course: PU21110288-0003

Price without VAT

21 990 Kč

Order

Open term

Language

Place

Praha

Form

classroom

Code of the course: PU21110288-0004

Price without VAT

21 990 Kč

Order

Course description

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)

Required knowledge

No previous knowledge of machine learning is required.

Course content

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)

Lecturers

Jiří Materna

He is a machine learning specialist with experience in its applications in industry since 2007. Between 2008 and 2017, he worked at Seznam.cz, of which the last 7 years as head of the research department. He now works as a freelancer, offers the development of custom machine learning solutions, organizes the Machine Learning Prague conference and writes the ML Guru blog.

Do you want this tailor-made course for your company?

News with the course

Machine Learning 18. 3. 2023

The differences between Machine learning and Artificiant inteligence

Machine learning (ML) and Artificial intelligence (AI) are related fields, but they are not the same thing. AI is a broader field that encompasses many different technologies, including machine learning. Check with us the key differences between machine learning and artificial intelligence.

Machine Learning 3. 6. 2021

Discover the benefits of Machine Learning

Machine Learning allows companies to be efficient, search for patterns in data, automate and make decisions with minimal human intervention. Learned algorithms solve defined tasks in real time and based on input data. At the same time, they learn from the new data and adapt to changing conditions.

More news