Machine Learning

Before developing a course, we listen to the real needs and objectives of each client, to adjust training and get high profitability. We adjust each course to your needs.

We are also specialists in formations 'in company' tailored to the needs of each organization, where harvesting for several participants from the same company is much higher. If this is your case, contact us.

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Goal

This course will understand the concepts needed to perform processes Machine Learning, this branch of artificial intelligence that aims to develop techniques that allow computers to learn.

Machine Learning projects create algorithms that can generalize and recognize behavior patterns from information provided by way of example ( training). Machine Learning techniques are used among others in the following areas: Medicine, Bioinformatics, Marketing, Natural Language Processing, Image Processing, Machine Vision, Spam Detection.

Machine Learning

Target audiences

ICT professionals: Consultants BI, Scientific Data.
Professionals of Applied Sciences: Mathematics, Statistics, Physics.

Observations

Methodology: The course intersperses theoretical parts where fundamental concepts are taught to understand the practical exercises taught.
Requirements: Basics: Linear Algebra, calculus and probability theory.

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Syllabus

Machine Learning with Scikit-Learn Data Science framework (Anaconda with Python 3)

1. Introduction to Machine Learning

Techniques
- Classification
- Regression
- Clustering
Preprocessing and dimensional reduction
Attribute selection
Performance evaluation
- Matrices de confusión
- KPIs R2, MAE, MSE

2. Regression (Prediction of continuous values)

Algorithms
- Ordinal Least Squares
- Ridge Regression
- Laso Regression
- Elastic Net
Examples

3. Classification (Identification of the category to which an object belongs)

Algorithms
- Logistic Regression
- Support Vector Machines
- KNearest Neighbors
- Decision Trees
- Random Forest
- Multi-layer Perceptron
Examples

4. Clustering (Grouping similar objects in sets)

Algorithms
- KMeans
- Spectral Clustering
- DBSCAN
Examples

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