ML — Machine Learning Intro. What Is It and How It Evolves Over…
Tags: Machine Learning, Autonomous Vehicles, Supervised Learning, Unsupervised Learning, Python3

Reinforcement Learning vs. Machine Learning vs. Deep Learning

Reinforcement learning is a branch of machine learning.

Unlike unsupervised and supervised machine learning, reinforcement learning does not rely on a static dataset, but operates in a dynamic environment and learns from collected experiences.

Data points, or experiences, are collected during training through trial-and-error interactions between the environment and a software agent.

This aspect of reinforcement learning is important, because it alleviates the need for data collection, preprocessing, and labeling before training, otherwise necessary in supervised and unsupervised learning.

Practically, this means that, given the right incentive, a reinforcement learning model can start learning behavior on its own, without (human) supervision. (https://ww2.mathworks.cn/discovery/reinforcement-learning.html)

Applications of Machine Learning

Facial recognition/Image recognition;

Automatic Speech Recognition;

Financial Services;

Marketing and Sales;

Healthcare;

Spam filter: the algorithm learns a predictive model from data labelled as spam and “no spam” (ham). After training it can predict for new emails whether they are spam or not;

Character recognition;

Object recognition in images;

And more...

What’s a Good Model?

Terminology of Machine Learning

(source: https://www.mygreatlearning.com/blog/what-is-machine-learning/)

Model: Also known as “hypothesis”, a machine learning model is the mathematical representation of a real-world process. A machine learning algorithm along with the training data builds a machine learning model. Feature: A feature is a measurable property or parameter of the data-set. Feature Vector: It is a set of multiple numeric features. We use it as an input to the machine learning model for training and prediction purposes. Training: An algorithm takes a set of data known as “training data” as input. The learning algorithm finds patterns in the input data and trains the model for expected results (target). The output of the training process is the machine learning model. Prediction: Once the machine learning model is ready, it can be fed with input data to provide a predicted output. Target (Label): The value that the machine learning model has to predict is called the target or label. Over fitting: When a massive amount of data trains a machine learning model, it tends to learn from the noise and inaccurate data entries. Here the model fails to characterise the data correctly. Under fitting: It is the scenario when the model fails to decipher the underlying trend in the input data. It destroys the accuracy of the machine learning model. In simple terms, the model or the algorithm does not fit the data well enough. Affine transformation: In Euclidean geometry, an affine transformation, or an affinity, is a geometric transformation that preserves lines and parallelism. In Euclidean geometry, an affine transformation, or an affinity, is a geometric transformation that preserves lines and parallelism. Fold change: It is defined as the It is defined as the ratio between the two quantities; for quantities A and B, then the fold change of B with respect to A is B/A. In other words, a change from 30 to 60 is defined as a fold-change of 2 Lingo: the language, word style, speech, jargon, or slang, used by a person or group

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Credits & References

INTRODUÇÃO A MACHINE LEARNING PARA CERTIFICAÇÃO HCIA-AI by crateus.ufc.br

The 7 steps of machine learning by Google Cloud Tech

A guide to convolution arithmetic for deep learning by Vincent Dumoulin F and Francesco Visin 2 F † FMILA — Université de Montréal †AIRLab, Politecnico di Milano

The Difference between Data Engineer, Data Scientists & Data Analysts:

Data Scientists and Data Analysts need to have TRUSTED, TIMELY & EFFICIENT ACCESS to the Data to do their best work. Data Enginner's role is the most distinct amongst the three. Data Enginner CREATE & MANTAIN THE INFRASTRUCTURE & DATA PIPELINE that takes terabytes of raw data coming from differente sources into one CENTRALIZED LOCATION with clean, relevant data for the organization. Data Analyst do what's described in their title: ANALYSE DATA & REPORT INSIGHTS from their analysis, and as AI field, for DEVELOPING A PREDICTIVE MODEL for BETTER ALLOCATION OF RESOURCES & FOR INCENTIVE OF PLANNING. Data Scientists INVESTIGATE, EXTRACT & REPORT MEANINGFUL INSIGHTS into the organization's data. In summary, Engineer create data, Scientist investigate data, and Analyst report insights :)