Artificial Intelligence is the discipline that automates tasks typically performed by humans. One of its most relevant domains is Machine Learning, which aims to teach computers to learn automatically in the absence of explicit programming activity.
In recent years, due to the massive amount of data available and the increased computational capacity of the hardware, the market’s focus on the machine learning paradigm has grown exponentially. Unlike classical programming, which is characterized by the centrality of the programmer’s activity in writing rules to transform input data into output data, in Machine Learning the rules are extracted from the input or output data. It is possible to reach these results through different types of algorithms, including artificial neural networks, which evolve in Deep Learning.
The AI and Machine Learning Mastery path, from terminology to the definition of its basic elements and the fundamental workflows, aims to provide proper knowledge of both the basic concepts of Machine Learning, with particular focus on the data preparation and the classification and regression problems solving, and the ones of Deep Learning.
You will learn the functioning dynamic of the AI and its practical use through Python language and popular libraries such as Scikit-learn, Keras, Pandas, and more.
You develop models capable of solving regression (modeling physical phenomena, predicting time series trends) and classification (image recognition) problems. It may interest you if you deal with statistical analysis aimed at highlighting patterns.
The following knowledge is required:
- Basic Statistics
- Basic Probability
- Linear Algebra and Signal Theory
- Demonstrate good programming aptitude
Nice to have
- Data analysis
- Image processing
In this training course, the planned modules will cover the following topics:
- Data Pre-processing
- Machine Learning
- Deep Learning
- Computer Vision