- Beginners level - from 6. November 2021 - Sign up in progress -
Fundamentals of machine learning and artificial intelligence
The aim of this chapter is to learn what machine learning is and what artificial intelligence is in general and their significance. To understand what problems we solve using machine learning algorithms.
How to use Google cloud with powerful servers to train large models
The goal of this chapter is to learn how to work with the Google Colab infrastructure.
Through practical situations, we make models for classification and prediction.
Example, whether a part of the text is written in a positive or negative tone, whether the mail is spam or not
How we make decisions in systems with complex rules
The goal of this chapter is to learn the standard Python libraries and functions used in machine learning algorithms.
We use the following Python ML libraries: pandas, sclearn, SciPy, NumPy, scikit-image
Deep learning tools, image classification, for example, a model that recognizes whether a picture is a dog or a cat. Sound classifications, prediction of the next number in the sequence, text classification.
The aim of this chapter is to learn the basics and principles of using Uber Ludwig tools to design, train and use deep learning models.