[ML-301] Experimental Machine Learning

Experimental Design in Machine Learning
Experimental design is a crucial step to ensure your models are effective and their results reliable. In this section, you'll explore the essential principles for conducting proper experiments, how to collect and sample data effectively, and how to avoid problems such as overfitting and underfitting, which can negatively affect the accuracy of your models.

Principles of Experimental Design

In this section, you'll learn how to establish hypotheses, how to design controlled experiments, and how to ensure that the results are valid and reproducible. Experimental design will allow you to apply scientific methodologies to test and adjust your models.

Data Collection and Sampling

The success of any model depends largely on the quality of the data. This section covers how to collect and select the right data, how to divide it into training and test sets, and how to choose a representative sample to obtain generalizable results.

Overfitting and Underfitting

These are two common problems in machine learning. Overfitting occurs when the model overfits the training data, losing its ability to generalize to new data. Underfitting, on the other hand, occurs when the model does not sufficiently capture the complexity of the data. You will learn to identify these problems and how to mitigate them.

Advanced Supervised Learning Techniques
They improve the ability of machine learning models to make more accurate and robust predictions.

Ensembles (Bagging, Boosting, Random Forests)

Ensemble methods combine multiple models to obtain more accurate and reliable predictions. You will learn how techniques such as Bagging, Boosting, and Random Forests help improve model performance by reducing variance and bias by combining the predictions of multiple algorithms.

Basic Neural Networks

Neural networks are one of the most powerful approaches in machine learning. In this section, you'll learn about the basic structure of a neural network, how its layers work, and how they are trained to solve complex problems, such as image recognition or natural language processing.

Unsupervised Learning (Clustering, Dimensionality Reduction)

Unsupervised learning focuses on extracting hidden patterns in unlabeled data. Here, you'll learn about techniques such as clustering, which groups similar data together, and dimensionality reduction, which simplifies complex data while maintaining its essential structure. These tools are useful for exploring large volumes of data without explicit supervision.

This course will give you a comprehensive overview of experimental design techniques and advanced machine learning methods, allowing you to improve your skills and understand more sophisticated approaches to building predictive models.

Experimental Design in Machine Learning