Machine Learning - Landscape (1)

octubre 15, 2020

I have recently started Data Engineering - Data Science Masters Degree and I have been studying "Machine Learning".  

As part of this subject I have to read the book: "Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition"

I am going to use my blog as summary of this subject and the other Master ones. Lets go!

How to define "Machine Learning"

Here two approachs:

- Is the science (and art) of programming computers so they can learn from data.

- Is the field of study that gives computers the ability to learn without being explicitly programmed.

Machine Learning is great for...

- Problems for which existing solutions require a lot of fine-tuning or long lists of rules.

- Complex problems for which using a traditional approach yields no good soution.

- Fluctuating environments: ML can adapt to new data.

- Getting insights about complex problems and large amount  of data.

Type of Machine Learning


The training set you feed to the algorithm includes the designed solutions, called labels.

a) Classification: i.e. spam / ham.

b) Predictors: predict the target numeric value, given a set of features.

This sort of task is called regression.

Some algorithms: k-nearest neighbour, Linear Regression, Logictics Regressions, Support Vector Machines (SVMs), Decission Trees and Random Forest, Neuronal Networks.

Unsupervised Learning

The training data is unlabeled.

- Dimensionality Reduction: the goal is to simplify the data without losing too much information. I.e. merge several features into one is called Feature Extraction. Is recommended due to improve the performance.

- Amount Detection: if sees a "new" instance can be classified as "anomaly".

- Novelty Detection: it aims to detect new instances that look different from all instances in the training set.

- Association Rule Learning: the goal is to dig into large amounts of data and discover interesting relations between attributes.

Semisupervised learning

Algorithms that can deal with data that is partially labeled.

Reinforcement learning

- Agent: is a learning system. Can observe the environment, select and perform actions, and get rewards in return.

Batch and online learning

- Batch: the system is incapable of learning incrementally: it must be trained using all the available data.

- Online: we train the system incrementally by feeding it data instances secuentially, either on in small groups called mini-batches.

- Out-of-core learning: when the Online learning is used to train systems on huge datasets that cannot fit in one machine´s main memory.

- Learning rate: how fast they should adapt to changing data.

Thats all for today!

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