This is a quick introduction on popular Supervised Learning Algorithms.

As we may recall, Supervised Learning refers to the set of algorithms that uses training data comprising both of inputs and corresponding output to build a model that subsequently predicts the best output for future inputs.

Supervised Learning problems fall in two broad categories:

If your organization is running a web application, you are likely to use EC2. Further, there is a high likelihood your instances are all Linux. Linux systems are robust and a suitable first choice for server applications like a MEAN stack express.js web server, an application built using open source LAMP stack, commercial proprietary software, or basic server using Apache or Nginx. Linux is also very widely used for both SQL and NoSQL database applications like Redis, Mongo, or Postgres or MySQL.

In our previous blog, Supervised vs Unsupervised Learning, we learned that Machine Learning consists of algorithms that learn to solve a problem without expressly being provided step by step instructions by a human agent. Then we differentiated between two major categories of algorithms - Supervised Learning & Unsupervised Learning. 

In this blog, we will walk through Supervised and Unsupervised Learning and answer these questions: 

Introduction

Like it or not, most of us have a boss, and thus, we work under supervision. Our boss's job is to make sure we stay focused and complete our work. We have quotas to fulfill and projects to complete. They know what the desired and expected outcomes are, the same way data scientists understand the result they are trying to produce with supervised learning.

 

Supervised learning using Python

This blog is the third one of the series on learning Machine Learning using Python. In the first one, DataScience & Machine Learning: Where to start with Python, we covered setting up Python and installing the relevant libraries. In the second one Looking further into Machine Learning using Python, we covered different machine learning techniques and became familiar with supervised learning. We also talked about the scikit-learn toolkit and saw the SVM approach used due to its flexibility and usefulness.

Photo by Brooke Lark on Unsplash

Introduction

Welcome back! Earlier, we had covered the basics of getting started with machine learning and Python. (Here is that blog if you missed that: DataScience & Machine Learning: Where to start with Python) The current blog will take the next step and introduce some ML (Machine Learning) concepts and algorithms.

Since we are going to use Python, we will stick to the sklearn Python library as our choice.

Part 4: Applications of Artificial Intelligence and Machine Learning for Task Simplification

Welcome back and thanks for tuning in! This is part 4 of this series on AI & Cybersecurity. The focus of this article is the application of Artificial Intelligence and Machine Learning that are making life simpler.

The term Deep Learning(DL) seems quite fascinating and people often get confused between it and Machine Learning, so let me simplify it for you.

Adversarial Application of Artificial Intelligence and Machine Learning

Welcome back and thanks for tuning in! This is part 3 of this series on AI & Cybersecurity. The focus of this article is the adversarial application of Artificial Intelligence and Machine Learning.

If you're new to this thread I suggest you start here with the introductory blog, AI & Cybersecurity

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