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Fundamentos del Aprendizaje Automático

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El curso ofrece una introducción a los conceptos y técnicas esenciales en el campo del aprendizaje automático. Este completo itinerario de aprendizaje abarca varios temas cruciales, como la utilización de la biblioteca scikit-learn para la iniciación al aprendizaje automático, la aplicación de la regresión lineal para el modelado predictivo, la exploración de métodos de clasificación para categorizar datos y el estudio de algoritmos de agrupación para descubrir patrones inherentes a los conjuntos de datos. Mostrar más

python

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What you will learn

Gain foundational insights into Machine Learning concepts.
Explore data types and preprocessing techniques in Scikit-learn.
Understand Machine Learning workflows including training sets and models.
Grasp data scaling methods: StandardScaler, MinMaxScaler, MaxAbsScaler.
Discover the structure and implementation of pipelines and ColumnTransformer.
Develop skills in evaluating models using cross-validation, GridSearchCV, and RandomizedSearchCV.
Assemble a complete Machine Learning pipeline integrating preprocessing and modeling.

109 chapters
13 hours
87 tasks

Trusted by employees of leading companies

Learning track content

Module 1 / ML Introduction with scikit-learn

Learn the Machine Learning concepts and the ML project workflow.

Preprocessing is probably the most important stage of an ML project. This chapter covers the preprocessing steps needed for almost any dataset.

A pipeline is a neat way to combine all the preprocessing steps as well as a model. Pipelines make it much easier to train and use a model.

Modeling is the most fun stage of an ML project. Let's learn to build, fine-tune and evaluate the model!

Module 2 / Linear Regression with Python

Let's start with the simplest Linear Regression model! You will learn the idea behind Linear Regression and how to make predictions in Python.

Most real-world prediction tasks involve more than one feature. You will learn how to handle Linear Regression with multiple features.

A straight line does not always describe the data well. Let's learn how to build a more complex model for prediction! That's what the Polynomial Regression is suited for.

Now that you know how to build many Linear Regression models, you need a way to choose the best one. This is achievable using metrics. This section explains the most used ones and the difficulties you can face using them.

Module 3 / Classification with Python

Module 4 / Ensemble Learning

What is an ensemble? How are ensembles different from standard machine-learning models? What are the types of ensembles? Let's consider the answers to these questions.

Let's consider some commonly used bagging ensemble models, the features of their use, and also apply some of them to solve real-life tasks.

The mechanism of work of boosting models differs from bagging models. Now we will explore these distinctions, gain insights into utilizing model boosting for problem-solving, and illustrate its functionality through practical demonstrations.

Let's consider some commonly used stacking ensemble models, the features of their use, and also apply some of them to solve real-life tasks.

Module 5 / Logistic Regression Mastering

In this project, we are going to understand Logistic Regression.

Requirements

A computer with a browser - all browsers are supported.
Your enthusiasm to enhance your tech skills.
Everything else needed to start learning and practicing is already included in this course.

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