Everand Logo

Welcome to Everand!

  • Discover millions of audiobooks, ebooks, and so much more with a free trial

    From $11.99/month after trial. Cancel anytime.

    Python Machine Learning: Using Scikit Learn, TensorFlow, PyTorch, and Keras, an Introductory Journey into Machine Learning, Deep Learning, Data Analysis, Algorithms, and Data Science
    Python Machine Learning: Using Scikit Learn, TensorFlow, PyTorch, and Keras, an Introductory Journey into Machine Learning, Deep Learning, Data Analysis, Algorithms, and Data Science
    Python Machine Learning: Using Scikit Learn, TensorFlow, PyTorch, and Keras, an Introductory Journey into Machine Learning, Deep Learning, Data Analysis, Algorithms, and Data Science
    Ebook143 pages1 hour

    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

    ()

    Read preview

    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.

    LanguageEnglish
    PublisherVere salazar
    Release dateFeb 29, 2024
    ISBN9798224086467
    Python Machine Learning: Using Scikit Learn, TensorFlow, PyTorch, and Keras, an Introductory Journey into Machine Learning, Deep Learning, Data Analysis, Algorithms, and Data Science

    Read more from Vere Salazar

    Related to Python Machine Learning

    Related ebooks

    Intelligence (AI) & Semantics For You

    View More
    View More

    Related podcast episodes

    Related categories

    Reviews for Python Machine Learning

    0 ratings

    0 ratings0 reviews

    What did you think?

    Tap to rate

    Review must be at least 10 words

      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,

      Enjoying the preview?
      Page 1 of 1