Python Programming
The Ultimate Beginners Guide to Learn Python Machine Learning Step-by-Step
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Description
If you are brand new to machine learning in Python, then Python Programming: The Ultimate Beginners Guide to Learn Python Machine Learning Step-by-Step is the book for you. The book will introduce you to the basic concepts of Machine Learning, Python programming language, various program libraries, and supporting platforms.
Most programmers are amazed at how easily they can pick up the Python programming language, as it is straightforward to use, making it easy to learn. Python is one of the best platforms for those new to programming to begin with, especially if you are going to be using the integrated development environment bundled with the Python package.
Python is also a freely available open-source package that comes with loads of supporting libraries which give it its programming power. For machine learning, Python is fast becoming one of the go-to languages for newbies to cut their machine learning teeth on.
Pythons concise, readable code offers programmers a simplicity that other programs do not. Thus, instead of getting lost in complex programming techniques and technical nuances, Python allows the programmer to concentrate on the application they are writing. In machine learning, the programmer needs to pay more attention to the complex algorithms and not get caught up in complex code as well.
For seasoned developers and data scientists, programming languages such as C, C ++, and so on are fine. But for someone starting out with both programming and machine learning, they can be quite cumbersome and confusing. Python programming offers a more streamlined and simpler platform to learn on.
This guide will help you with your journey into the world of Python machine learning and help you navigate your way from a newbie to an intermediate level.
Table of Contents
Introduction
Thank you for choosing Python Programming: The Ultimate Beginners Guide to Learn Python Machine Learning Step-by-Step as your guide to Python machine learning.
In this beginners guide, you will learn the programming language syntax, basic programming concepts, and how to apply them to code. Other topics covered in this booking include:
Useful Python features that beginners can use in real-world situations
How to install and configure Python
Python programming mechanics such as control stream, factors, records or lexicons, and classes
Machine learning basics and how machine learning is implemented in Python, including Scikit-learn, Tensorflow, and more
Artificial Intelligence (AI)
Both a robot controlled by a computer and a digital computer that is able to perform tasks that an intelligent being can are defined as artificial intelligence (AI). There are many subsets of AI which most commonly include:
Deep Learning (DL) neural networks.
Expert Systems (ES) teaching systems and systems with decision support.
Machine Learning (ML) version space and decision tree learning.
Machine Vision (MV) understanding images and character/object recognition
Natural Language Processing (NLP) machine translation.
Planning gameplay, scheduling, and task-driven systems.
Robotics autonomous systems with intelligent control.
Speech Recognition (SR) language recognition.
For a machine to be classed as an artificial intelligence system, it needs to be able to:
Sense
Reason
Act
Adapt
For an AI to be able to carry out its goals successfully and exhibit the four qualities above, it needs to be able to think. Unlike natural intelligence, a computer's intelligence comes from cleverly designed algorithms that can learn. Therefore, one of the most important parts of AI is machine learning, as well as deep learning (which can be classified as a subset of ML).
Machine learning is the subset of AI that provides the machine with the ability to learn. ML is like the brain of an AI system, giving it the intelligence it needs to automatically learn by experience without explicit programming instructions.
Machine Learning (ML)
Machine Learning is the study of algorithms designed to be able to learn through the input of data. Using the data the programmer trained the system on, the algorithm will learn how to describe the data, improve upon the data, and predict outcomes. Most analytical and statistical data found in the modern day comes from using a machine learning algorithm.
A prime example of machine learning is the popup adverts found on search engines such as Google. You may notice that the adverts that appear will pertain to recent searches or products you have looked at on the Web. That is the machine learning algorithm assimilating your search history and predicting what it thinks you will find interesting or useful.
Some of the more common machine learning methods or algorithms include:
Decision trees
K-means clustering
K-nearest neighbor
Naive Bayes
Random forest
Support vector machine
Four Important Techniques Most Artificial Intelligence Systems Have in Common
Learning AIs have the capability to gain knowledge from their environment.
Representation AIs have the capability to determine how to represent the knowledge they learn from their environment.
Rules AIs have an explicit set of instructions or rules which are the guidelines upon which it learns and grows.
Search AIs have the ability to search through and find a sequence of states, the connection of weights, etc., that lead to a solution or outcome.
This guide covers an introduction to the basics of Python machine learning. Python is by far one of the easiest and most accessible programming environments to use to start learning about this subdivision of artificial intelligence.
History of Python
Guido van Rossum founded Python after working for a couple of years at the Centrum Wiskunde & Informatica (CWI), in Amsterdam, Netherlands. At CWI Guido worked on a project creating a programming language called ABC in the mid 1980s. It was the ABC programming language that inspired him to create Python in ABCs image in the late 1980s. Guido used his knowledge of what worked best with ABC to draw from and enhance Python. The things he knew only frustrated him with ABC, he found a way to fine-tune or leave out all together if they were not necessary.
