Hi,

this checklist can guide you through your Machine Learning projects. There are eight main steps:

1. Frame the problem and look at the big picture.

2. Get the data.

3. Explore the data to gain insights.

4. Prepare the data to better expose the underlying data patterns to Machine Learning algorithms.

5. Explore many different models and shortlist the best ones.

6. Fine-tune your models and combine them into a great solution.

7. Present your solution.

8. Launch, monitor, and maintain your system.

Here the second chapter based on the Machine Learning Landscape book "Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition"

If you are interested in the first part, here the link: https://www.franciscojavierpulido.com/2020/10/machine-learning-landscape-1.html

Note: I use this post as a summary and are notes for me and part of my studies, but I like to share it with the community.

Great then, continue with the party.

Instance-based versus Model-based learning

Concepts:

- Generalization: another way to classify ML Algorithms.

Generalizate to = Make good predictions for!

- Having good performance measure on the training data is good, but insufficient; the true goal is to perform well on new instances.

Instance-based learning

The system learns the examples by heart, then generalizes to new cases by using a similarity measure to compare them to the learned examples.

Model-based learning

Building models to make good preditions.

Concepts:

- Utility function/fitness function: measures how good is a model.

- Cost function: measure how bad is a model.

For linear regression problems, people tipically use a cost function that measures the distance between the linear models prediction and the training examples; the objetive is to minimize the distance.

Training the model: when we finds the parameters that make the linear model that fit best to our data when we feed it with our training examples.

Main challenges of Machine Learning

Two main problems: bad algorithms / bad data

- Insufficient Quantity of training Data

- Non representative trainin data: for accurated preditions

a) Sampling noise: if the sample is too small.

b) Sampling bias: large samples that cannot be representative.

- Poor quality data: errors, outliers, noise ...

- Irrelevant features: feature engineering:

a) feature selection

b) feature extraction

c) creating new features

- Overfitting the training data: the model performs well on the training data, but it does not generalize well.

There are two possible solutions for overfitting:

a) Simplifing the model by selecting one with fewer parameters(e.g., a linear model rather than a high-degree polynomial model), by reducing the number of attributes in the training data, or by constraining the model.

b) Regularization: constraining a model to make it simpler and reduce the risk of overfitting is called regularization.

- Hyperparameter: is a parameter of a learning algorithm (not of the model). We can control the amount of regularization to apply during learning.

- Underfitting the tranining data: it occurs when the model is too simple to learn the underlying structure of Data. To fix this problem:

a) Select a more powerful model, with more parameters.

b) Feed better features to the learning algorithm (feature engineering).

c) Reduce the constrains of the model

Testing and validating

It is basically to split the data into two sets: the training set and the test set.

- Generalization error: the error rate on new cases. This value tells how well our model will perform on instances it has never seen before.

If the training error is low but the generalization error is high, it means that our model is overfitting the tranining data.

- Hyperparameter tunning and model selection: it uses cross-validation that consist by using multiple small test validation to avoid holdout validation, using different models and hyperparameters.

- Avoid Data missmatch: split the validation set into:

a) the test set to test the model

b) train-dev set to test the tested model

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

Supervised

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!