Machine Learning with Python - Concepts


Advertisements

In this chapter, you will learn in detail about the concepts of Python in machine learning.

Python in Machine Learning

Python has libraries that enables developers to use optimized algorithms. It implements popular machine learning techniques such as recommendation, classification, and clustering. Therefore, it is necessary to have a brief introduction to machine learning before we move further.

What is Machine Learning?

Data science, machine learning and artificial intelligence are some of the top trending topics in the tech world today. Data mining and Bayesian analysis are trending and this is adding the demand for machine learning. This tutorial is your entry into the world of machine learning.

Machine learning is a discipline that deals with programming the systems so as to make them automatically learn and improve with experience. Here, learning implies recognizing and understanding the input data and taking informed decisions based on the supplied data. It is very difficult to consider all the decisions based on all possible inputs. To solve this problem, algorithms are developed that build knowledge from a specific data and past experience by applying the principles of statistical science, probability, logic, mathematical optimization, reinforcement learning, and control theory.

Applications of Machine Learning Algorithms

The developed machine learning algorithms are used in various applications such as −

  • Vision processing
  • Language processing
  • Forecasting things like stock market trends, weather
  • Pattern recognition
  • Games
  • Data mining
  • Expert systems
  • Robotics

Steps Involved in Machine Learning

A machine learning project involves the following steps −

  • Defining a Problem
  • Preparing Data
  • Evaluating Algorithms
  • Improving Results
  • Presenting Results

The best way to get started using Python for machine learning is to work through a project end-to-end and cover the key steps like loading data, summarizing data, evaluating algorithms and making some predictions. This gives you a replicable method that can be used dataset after dataset. You can also add further data and improve the results.

Advertisements