Machine Learning

What is Machine Learning?

Machine learning (ML) uses to be categorized as a subfield of artificial intelligence (AI). Fundamentally, machine learning involves building mathematical models to help understand data. Once these models have been fit to previously seen data, they can be used to predict and understand aspects of newly observed data.

Categories of Machine Learning

At the most fundamental level, machine learning can be categorized into two main types: Supervised Learning and Unsupervised Learning. And one more special case known as Reinforcement Learning.

• Supervised learning involves somehow modeling the relationship between measured features of data and some label associated with the data; once this model is determined, it can be used to apply labels to new, unknown data. This is further subdivided into Classification tasks and Regression tasks. In classification, the labels are discrete categories, while in regression, the labels are continuous quantities.

• Unsupervised learning involves modeling the features of a dataset without reference to any label, and is often described as “letting the dataset speak for itself.” These models include tasks such as Clustering and Dimensionality reduction. Clustering algorithms identify distinct groups of data, while dimensionality reduction algorithms search for more succinct representations of the data.

• Reinforcement learning we have an agent and it receive a reward or punishment depending on its current state and action.

Principal ML Applications

• Classification: Models that predict labels as two or more discrete categories. Know more

• Regression: Models that predict continuous labels. Know more

• Clustering: Models that detect and identify distinct groups in the data. Know more

• Dimensionality reduction: Models that detect and identify lower-dimensional structure in higherdimensional data. Know more

ML Models

• Naive Bayes:

• Support Vector Machines (SVM):

• Decission Trees:

• Random Forest:

• K Nearest Neighbors:

• K-Means:

• Gaussian Mixture Models (GMM):

• Principal Component Analysis:

• t-SNE:

ML Technicalities

• Overfitting:

• Underfitting:

• Bias:

• Variance:

• Cross-Validation:

• Normalization:

Other methods

• Boosting:

• Bagging:

• Classification:

Related projects

def my_func(input)
  name = "Courses"
  url = "courses/"
  weight = 50
  return weight

SELECT *
FROM tutorial.houses
WHERE price > 50
ORDER BY size

Bibliography

• Bishop
• O’reilly Python data science Handbook

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