Python Machine Learning: Using Scikit Learn, TensorFlow, PyTorch, and Keras, an Introductory Journey into Machine Learning, Deep Learning, Data Analysis, Algorithms, and Data Science
By Vere salazar
()
About this ebook
Are you feeling overpowered by all the contradicting things you might find online?
Are you sick and weary of battling with incredibly implausible and time-consuming tasks that frequently lack important information and have unclear instructions? Grab a copy of "Python Machine Learning" and dive into the core of successful Python programming to begin your revolutionary journey. Say goodbye to abandoned initiatives and get a plethora of ideas that will stimulate your mind on these pages.
Arm yourself with practical advice that will enable you to finish your tasks skillfully. Discover why Python is better than more conventional approaches and unleash your inner potential. This tutorial serves as your starting point for learning Python Machine Learning, exploring the "Whys" and "Hows" of the language, and putting machine learning algorithms into practice. Recognize the importance of mastering data analysis and become an expert in machine learning using Python. Distinguish between machine learning and deep learning, and use Scikit-Learn, TensorFlow, PyTorch, and Keras to unlock the mysteries of machine learning.
Examine how machine learning functions inside the Internet of Things (IoT) to obtain a business-oriented perspective on the future. With its easy-to-follow advice and extensive content, this book is a great starting point for anybody new to programming.
Enjoy the adaptability, effectiveness, and simplicity of use that this high-level programming language offers, in addition to the benefits of support libraries and user-friendly data structures in Python Programming for Machine Learning and Deep Learning. Easily construct projects with instructions and information that are explicit, resulting in well-developed, evaluated, and analyzed products. A good programming experience requires not only maintaining a happy mindset but also understanding the principles and essentials. Start your path of transformation right now.
Read more from Vere Salazar
Python Data Science: A Comprehensive Guide to Self-Directed Python Programming Learning Rating: 0 out of 5 stars0 ratingsMastering Python and Raspberry Pi: A Comprehensive Guide to Computer Programming Rating: 0 out of 5 stars0 ratingsSQL for Beginners: A Guide to Excelling in Coding and Database Management Rating: 0 out of 5 stars0 ratingsCoding for Beginners: A Step-by-Step Guide to Learn Python, Java, SQL, C, C++, C#, HTML, and CSS from Scratch Rating: 0 out of 5 stars0 ratingsKali Linux for Beginners: A Step-by-Step Guide to Learn the Basics of Hacking and Security Testing Rating: 0 out of 5 stars0 ratingsC# for beginners: A step-by-step guide to developing professional and modern applications Rating: 0 out of 5 stars0 ratingsRaspberry Pi: The Ultimate Guide to Learning the Fundamentals and Unleashing Powerful Hacks Rating: 0 out of 5 stars0 ratingsMastering Python: A Comprehensive Guide for Beginners to Learn the Basics Rating: 0 out of 5 stars0 ratingsPython programming: A Quick and Easy Approach to Learning Python Programming Rating: 0 out of 5 stars0 ratingsMastering SQL: A Step-by-Step Guide toSQL Programming and Database Management Systems for Beginners Rating: 0 out of 5 stars0 ratingsLinux Basics for Beginners: A Step-by-Step Guide for Hackers and Cybersecurity Enthusiasts Rating: 0 out of 5 stars0 ratingsPython for Beginners: This comprehensive introduction to the world of coding introduces you to the Python programming language Rating: 0 out of 5 stars0 ratingsRaspberry PI: The Comprehensive Guide to Self-Taught Computer Technology Learning, Simple Setup, and Project Mastery Rating: 0 out of 5 stars0 ratingsEthical Hacking: How to Protect Your Network from Cyberattacks Rating: 0 out of 5 stars0 ratingsLinux Hacking 101: A Beginner’s Guide to Ethical Hacking with Linux and Kali Linux Rating: 0 out of 5 stars0 ratings
Related to Python Machine Learning
Related ebooks
Python Machine Learning for Beginners: A Step by Step Approach to Scikit-Learn and TensorFlow Rating: 0 out of 5 stars0 ratingsPractical Data Science: A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets Rating: 0 out of 5 stars0 ratingsMastering SQL: A Step-by-Step Guide toSQL Programming and Database Management Systems for Beginners Rating: 0 out of 5 stars0 ratingsMastering Python: A Comprehensive Guide for Beginners to Learn the Basics Rating: 0 out of 5 stars0 ratingsLinux Basics for Beginners: A Step-by-Step Guide for Hackers and Cybersecurity Enthusiasts Rating: 0 out of 5 stars0 ratingsPython for Data Science: A Practical Approach to Machine Learning Rating: 0 out of 5 stars0 ratingsData Science Solutions with Python: Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn Rating: 0 out of 5 stars0 ratingsProgramming for kids: The Comprehensive Course for young Coding Mastery Rating: 0 out of 5 stars0 ratingsMastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python Rating: 0 out of 5 stars0 ratingsGROKKING ALGORITHMS: Simple and Effective Methods to Grokking Deep Learning and Machine Learning Rating: 0 out of 5 stars0 ratingsLinux Hacking 101: A Beginner’s Guide to Ethical Hacking with Linux and Kali Linux Rating: 0 out of 5 stars0 ratingsPyTorch Recipes: A Problem-Solution Approach Rating: 0 out of 5 stars0 ratingsData Analysis and Visualization Using Python: Analyze Data to Create Visualizations for BI Systems Rating: 0 out of 5 stars0 ratingsPractical Machine Learning with Python: A Problem-Solver's Guide to Building Real-World Intelligent Systems Rating: 0 out of 5 stars0 ratingsData Science with Jupyter: Master Data Science skills with easy-to-follow Python examples Rating: 0 out of 5 stars0 ratingsMachine Learning with Rust: A practical attempt to explore Rust and its libraries across popular machine learning techniques Rating: 0 out of 5 stars0 ratingsMachine Learning with Rust Rating: 0 out of 5 stars0 ratingsPython Machine Learning: A Step by Step Beginner’s Guide to Learn Machine Learning Using Python Rating: 0 out of 5 stars0 ratingsA Python Data Analyst’s Toolkit: Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics Rating: 0 out of 5 stars0 ratingsPro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R Rating: 0 out of 5 stars0 ratingsPython programming: A Quick and Easy Approach to Learning Python Programming Rating: 0 out of 5 stars0 ratingsMastering Python for Data Science Rating: 3 out of 5 stars3/5Raspberry PI: The Comprehensive Guide to Self-Taught Computer Technology Learning, Simple Setup, and Project Mastery Rating: 0 out of 5 stars0 ratingsHands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python Rating: 0 out of 5 stars0 ratingsNeural Networks with Python Rating: 0 out of 5 stars0 ratingsSupervised Learning with Python: Concepts and Practical Implementation Using Python Rating: 0 out of 5 stars0 ratings
Intelligence (AI) & Semantics For You
2084: Artificial Intelligence and the Future of Humanity Rating: 4 out of 5 stars4/5Artificial Intelligence: A Guide for Thinking Humans Rating: 4 out of 5 stars4/5Creating Online Courses with ChatGPT | A Step-by-Step Guide with Prompt Templates Rating: 4 out of 5 stars4/5Mastering ChatGPT: 21 Prompts Templates for Effortless Writing Rating: 5 out of 5 stars5/5101 Midjourney Prompt Secrets Rating: 3 out of 5 stars3/5ChatGPT For Fiction Writing: AI for Authors Rating: 5 out of 5 stars5/5Dark Aeon: Transhumanism and the War Against Humanity Rating: 5 out of 5 stars5/5Our Final Invention: Artificial Intelligence and the End of the Human Era Rating: 4 out of 5 stars4/5Impromptu: Amplifying Our Humanity Through AI Rating: 5 out of 5 stars5/5Chat-GPT Income Ideas: Pioneering Monetization Concepts Utilizing Conversational AI for Profitable Ventures Rating: 4 out of 5 stars4/5Summary of Super-Intelligence From Nick Bostrom Rating: 5 out of 5 stars5/5ChatGPT Ultimate User Guide - How to Make Money Online Faster and More Precise Using AI Technology Rating: 0 out of 5 stars0 ratingsThe Secrets of ChatGPT Prompt Engineering for Non-Developers Rating: 5 out of 5 stars5/5What Makes Us Human: An Artificial Intelligence Answers Life's Biggest Questions Rating: 5 out of 5 stars5/5Midjourney Mastery - The Ultimate Handbook of Prompts Rating: 5 out of 5 stars5/5The Business Case for AI: A Leader's Guide to AI Strategies, Best Practices & Real-World Applications Rating: 0 out of 5 stars0 ratingsWays of Being: Animals, Plants, Machines: The Search for a Planetary Intelligence Rating: 4 out of 5 stars4/5Discovery Writing with ChatGPT: AI-Powered Storytelling: Three Story Method, #6 Rating: 0 out of 5 stars0 ratingsAI for Educators: AI for Educators Rating: 5 out of 5 stars5/5The Algorithm of the Universe (A New Perspective to Cognitive AI) Rating: 5 out of 5 stars5/5ChatGPT For Dummies Rating: 0 out of 5 stars0 ratingsDancing with Qubits: How quantum computing works and how it can change the world Rating: 5 out of 5 stars5/5
Reviews for Python Machine Learning
0 ratings0 reviews
Book preview
Python Machine Learning - Vere salazar
Python machine learning
Using Scikit Learn, TensorFlow, PyTorch, and Keras, an Introductory Journey into Machine Learning, Deep Learning, Data Analysis, Algorithms, and Data Science
Vere salazar
© Copyright 2024 by vera poe all rights reserved.
The content contained within this book may not be reproduced, duplicated or transmitted without direct written permission from the author or the publisher.
Under no circumstances will any blame or legal responsibility be held against the publisher, or author, for any damages, reparation, or monetary loss due to the information contained within this book, either directly or indirectly.
Legal notice:
This book is copyright protected. It is only for personal use. You cannot amend, distribute, sell, use, quote or paraphrase any part, or the content within this book, without the consent of the author or publisher.
Disclaimer notice:
Please note the information contained within this document is for educational and entertainment purposes only. All effort has been executed to present accurate, up to date, reliable, complete information. No warranties of any kind are declared or implied. Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical or professional advice. The content within this book has been derived from various sources. Please consult a licensed professional before attempting any techniques outlined in this book.
By reading this document, the reader agrees that under no circumstances is the author responsible for any losses, direct or indirect, that are incurred as a result of the use of information contained within this document, including, but not limited to, errors, omissions, or inaccuracies.
Table of contents
Chapter 1: machine learning: a brief history
Donald hebb - the organization of behavior
Samuel arthur - neural networks, checkers and rote learning
Rosenblatt’s perceptron
Marcello pelillo - the nearest neighbor algorithm
Perceptrons and multilayers
Going separate ways
Robert schapire - the strength of weak learnability
Advancing into speech and facial recognition
Present day machine learning
Chapter 2: fundamentals of python for machine learning
What is python?
Why python?
Other programming languages
Effective implementation of machine learning algorithms
Mastering machine learning with python
Chapter 3: data analysis in python
Importance of learning data analysis in python
Building predictive models in python
Python data structures
Python libraries for data analysis
Chapter 4: comparing deep learning and machine learning
Deep learning vs machine learning
Problem solving approaches
Different use cases
Chapter 5: machine learning with scikit-learn
Representing data in scikit-learn
Features matrix
Target arrays
Estimator api
Supervised learning in scikit-learn
Unsupervised learning in scikit-learn
Chapter 6: deep learning with tensorflow
Brief history of tensorflow
The tensorflow platform
Tensorflow environments
Tensorflow components
Algorithm support
Creating tensorflow pipelines
Chapter 7: deep learning with pytorch and keras
Pytorch model structures
Initializing pytorch model parameters
Principles supporting keras
Getting started
Keras preferences
Keras functional api
Chapter 8: role of machine learning in the internet of things (iot)
Chapter 9: looking to the future with machine learning
The business angle
Ai in the future
Conclusion
Introduction
The, me,ntion of de,ve,lope,rs and programming usually has a lot of pe,ople, dire,cting the,ir thoughts to the, wide,r study of compute,r scie,nce,. compute,r scie,nce, is a wide, are,a of study. in machine, le,arning, compute,rs le,arn from e,xpe,rie,nce,, aide,d by algorithms. to aid the,ir cause,, the,y must use, data with spe,cific fe,ature,s and attribute,s. this is how the,y ide,ntify patte,rns that we, can use, to he,lp in making important de,cisions. in machine, le,arning, assignme,nts are, groupe,d unde,r diffe,re,nt cate,gorie,s, such as pre,dictive, mode,ling and cluste,ring mode,ls. the, conce,pt be,hind machine, le,arning is to provide, solutions to pe,rtine,nt proble,ms without ne,ce,ssarily waiting for dire,ct human inte,raction.
Machine, le,arning and artificial inte,llige,nce, today are, the, re,ality that we, dre,amt of ye,ars ago. the,se, conce,pts are, no longe,r confine,d to fictional ide,as in movie,s, but the,y have, be,come, the, backbone, of our daily live,s. if you think about your inte,rne,t activity all through the, day, you inte,ract with machine, le,arning mode,ls all the, time,. how many time,s have, you had a we,bsite, translate,d from a fore,ign language, to your native, language,? think about the, numbe,r of time,s you have, be,e,n assiste,d through a chatbot, or use,d facial and voice, re,cognition programs. all the,se, are, instance,s whe,re, we, inte,ract with machine, le,arning mode,ls, and the,y he,lp by making our live,s e,asie,r.
Like, any othe,r discipline,, machine, le,arning doe,s not e,xist in isolation. many conce,pts in machine, le,arning are, inte,rtwine,d with de,e,p le,arning and artificial inte,llige,nce,. the,re, are, othe,r subje,cts that share, similaritie,s with machine, le,arning, but for the, purpose, of this book, we, will focus on de,e,p le,arning and artificial inte,llige,nce,.
This be,ing the, first book in a se,rie,s of e,nlighte,ning books about machine, le,arning, will introduce, you to the, fundame,ntal ide,ologie,s you should unde,rstand the, te,chnology, syste,ms, and proce,dure,s use,d in machine, le,arning, and how the,y are, conne,cte,d.
Artificial inte,llige,nce, branche,s off from machine, le,arning, but the,y share, a lot of similaritie,s. tracing the,se, two studie,s back in time,, the,y share, the, same, path for most of the,ir history. while, machine, le,arning focuse,s on building mode,ls that le,arn through algorithms and can ope,rate, without human inte,rve,ntion, artificial inte,llige,nce, focuse,s on simulating human e,xpe,rie,nce,s and inte,llige,nce, through computing. it is safe, to say that machine, le,arning is a subclass of artificial inte,llige,nce, be,cause, we, work towards building machine,s that can simulate, human de,cision-making proce,sse,s, albe,it by le,arning through data.
De,e,p le,arning introduce,s us to anothe,r division of machine, le,arning whe,re, artificial ne,ural ne,tworks (ann) are, e,mploye,d in making important de,cisions. in de,e,p le,arning, the, ne,ural ne,tworks use, laye,re,d structure,s whose, functions are, similar to the, functions of a he,althy human brain. the,re,fore,, machine, le,arning, de,e,p le,arning, and artificial inte,llige,nce, are, thre,e, discipline,s that are, inte,rconne,cte,d in more, ways than one,. whe,n you commit to le,arning one, of the,m, you will inadve,rte,ntly have, to le,arn about the, othe,rs too at some, point.
In machine, le,arning, de,e,p le,arning is a cate,gory that focuse,s on using algorithms to e,mpowe,r syste,ms and build mode,ls that are, similar in ope,ration to the, human brain. the, pre,se,nt e,xcite,me,nt and hype, around de,e,p le,arning come,s from the, fundame,ntal studie,s in ne,ural ne,tworks. re,se,arch in ne,ural ne,tworks has be,e,n carrie,d out for many ye,ars, and could date, back longe,r than the, history of machine, le,arning. this is be,cause, part of this knowle,dge, is e,mbe,dde,d in ne,urological studie,s without an iota of re,fe,re,nce, to machine, le,arning or computing.
The,re, have, be,e,n major stride,s in machine, le,arning re,se,arch ove,r the, ye,ars, e,spe,cially with re,spe,ct to de,e,p le,arning. while, we, must re,cognize, the, scalability of the,se, discipline,s, the, advance,me,nt in the,se, te,chnologie,s is made, possible, by thre,e, important factors; the, de,ve,lopme,nt of e,fficie,nt algorithms, the, incre,asing and matching de,mand for significant computing re,source,s, and the, incre,ase, in the, inte,rne,t population, he,nce, massive, chunks of data are, available, to train and e,mpowe,r the,se, machine,s.
So how do we, find the, link be,twe,e,n de,e,p le,arning and machine, le,arning? the, answe,r lie,s in how the,se, mode,ls ope,rate,. from a basic pe,rspe,ctive,, you work with mode,ls which re,ce,ive, pre,de,fine,d input and output data. input data could be, anything from te,xt instructions, to nume,rical input, or audio, vide,o, and image,s in diffe,re,nt me,dia formats. base,d on the, input, the, spe,cific mode,l you use, will the,n de,rive, an output that me,e,ts your instructions. output could be, anything from ide,ntifying an individual’s name, to de,fining the,ir tribe,. the, corre,ct answe,r de,pe,nds on the, kind of input data you provide, the, machine, le,arning mode,l.
As you le,arn about the,se, ne,tworks, you must also spare, time, to sharpe,n your data analysis and data handling skills. one, skill you must be, good at is how to pre,pare, data, e,spe,cially how to cle,an data. machine, and de,e,p le,arning mode,ls de,pe,nd on data for accuracy. inaccuracie,s in the, input data will affe,ct the, output. many mistake,s happe,n at data e,ntry and if the,se, are, not che,cke,d, you will e,nd up with a good machine, le,arning mode,l that cannot de,live,r the, outcome, e,xpe,cte,d. this is why data cle,aning, and data analysis in ge,ne,ral, are, important proce,sse,s.
Once, your mode,l has sufficie,nt data, it should pre,dict outcome,s according to the, input provide,d and the, instructions upon which the, mode,l trains. today the,re, are, many machine, le,arning mode,ls that are, alre,ady in use,, including te,xtcnn, yolo, ince,ption, and face,ne,t.
An ove,rvie,w of machine, le,arning make,s it sound like, a simple, conce,pt. for those, who have,