Answer:

As a request from my friend Richaldo, in this post I’m going to explain the types of machine learning algorithms and when you should use each of them. I particularly think that getting to know the types of Machine learning algorithms is like getting to see the Big Picture of AI and what is the goal of all the things that are being done in the field and put you in a better position to break down a real problem and design a machine learning system.

Types of machine learning Algorithms

There some variations of how to define the types of Machine Learning Algorithms but commonly they can be divided into categories according to their purpose and the main categories are the following:

• Supervised learninhg
• Unsupervised Learning
• Semi-supervised Learning
• Reinforcement Learning

Supervised Learning

-I like to think of supervised learning with the concept of function approximation, where basically we train an algorithm and in the end of the process we pick the function that best describes the input data, the one that for a given X makes the best estimation of y (X -> y). Most of the time we are not able to figure out the true function that always make the correct predictions and other reason is that the algorithm rely upon an assumption made by humans about how the computer should learn and this assumptions introduce a bias, Bias is topic I’ll explain in another post.

Draft

-Predictive Model

-we have labeled data

-The main types of supervised learning problems include regression and classification problems

List of Common Algorithms

-Nearest Neighbor

-Naive BayesDecision

- Trees

-Linear Regression

-Support Vector Machines (SVM)Neural Networks

Explanation:

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