Python Machine Learning: A Step-by-Step Journey with Scikit-Learn and Tensorflow for Beginners

Python Machine Learning: A Step-by-Step Journey with Scikit-Learn and Tensorflow for Beginners

Chloe AAnnable

© ACopyright A2024 A- All Arights Areserved.

The Acontent Acontined Awithin Athis Abook Amy Anot Abe Areproduced, Aduplicted Aor Atrnsmitted Awithout Adirect Awritten Apermission Afrom Athe Author Aor Athe Apublisher.

Under Ano Acircumstnces Awill Any Ablme Aor Alegl Aresponsibility Abe Aheld Aginst Athe Apublisher, Aor Author, Afor Any Admges, Areprtion, Aor Amonetry Aloss Adue Ato Athe Ainformtion Acontined Awithin Athis Abook, Aeither Adirectly Aor Aindirectly.

Legl ANotice:

This Abook Ais Acopyright Aprotected. AIt Ais Aonly Afor Apersonl Ause. AYou Acnnot Amend, Adistribute, Asell, Ause, Aquote Aor Aprphrse Any Aprt, Aor Athe Acontent Awithin Athis Abook, Awithout Athe Aconsent Aof Athe Author Aor Apublisher.

Disclimer ANotice:

Plese Anote Athe Ainformtion Acontined Awithin Athis Adocument Ais Afor Aeductionl And Aentertinment Apurposes Aonly. All Aeffort Ahs Abeen Aexecuted Ato Apresent Accurte, Aup Ato Adte, Arelible, Acomplete Ainformtion. ANo Awrrnties Aof Any Akind Are Adeclred Aor Aimplied. AReders Acknowledge Atht Athe Author Ais Anot Aengged Ain Athe Arendering Aof Alegl, Afinncil, Amedicl Aor Aprofessionl Advice. AThe Acontent Awithin Athis Abook Ahs Abeen Aderived Afrom Avrious Asources. APlese Aconsult A Alicensed Aprofessionl Abefore Attempting Any Atechniques Aoutlined Ain Athis Abook.

By Areding Athis Adocument, Athe Areder Agrees Atht Aunder Ano Acircumstnces Ais Athe Author Aresponsible Afor Any Alosses, Adirect Aor Aindirect, Atht Are Aincurred As A Aresult Aof Athe Ause Aof Athe Ainformtion Acontined Awithin Athis Adocument, Aincluding, Abut Anot Alimited Ato, Aerrors, Aomissions, Aor Ainccurcies.

TABLE OF CONTENTS

INTRODUCTION

UNSUPERVISED AMACHINE ALEARNING

Principal AComponent AAnalysis

k-means AClustering

DEEP ABELIEF ANETWORKS

Neural ANetworks

The ARestricted ABoltzmann AMachine

Constructing ADeep ABelief ANetworks

CONVOLUTIONAL ANEURAL ANETWORKS

Understanding Athe AArchitecture

Connecting Athe APieces

STACKED ADENOISING AAUTOENCODERS

Autoencoders

SEMI-SUPERVISED ALEARNING

Understanding Athe ATechniques

Self-learning

Contrastive APessimistic ALikelihood AEstimation

TEXT AFEATURE AENGINEERING

Text AData ACleaning

Building AFeatures

MORE AFEATURE AENGINEERING

Creating AFeature ASets

Real-world AFeature AEngineering

ENSEMBLE AMETHODS

Averaging AEnsembles

Stacking AEnsembles

CONCLUSION

INTRODUCTION

This Abook Ais Aa Astep-by-step Aguide Athrough Aintermediate Amachine Alearning Aconcepts Aand Atechniques. AYou’ll Aalso Alearn Aworking Awith Acomplex Adata, Aas Aany Amachine Alearning Atechnology Arequires Adata. AThe Abulk Aof Athe Awork Ain Athis Abook Awill Abe Acommunicated Awith Aclear Aexamples. AThis Ais Agreat Anews Aif Ayou Aare Athe Atype Athat Adoes Abetter Alearning Afrom Aexamples.

Since Athis Ais Aan Aintermediate Aguide, Athere Ais Aa Alot Aof Aassumed Aknowledge Aon Aour Apart. AWe Aexpect Ayou Ato Aknow Amachine Alearning Abasics Aand APython. AThe Aact Aof Apublishing Aa Abook Alike Athis Ais Aalways Aabout Asimplifying Athings Aso Aanyone Acan Alearn. ASo, Aif Ayou Aaren’t Asure Ayou Agot Athe Abasics Adown, Ayou Acan Astill Ahave Aa Alook Aand Ado Asome Aextra Aresearch Awhen Ayou Acome Aacross Aconcepts Athat Aare Anew Ato Ayou. AThe Ainformation Ashould Aotherwise Abe Aeasy Ato Adigest.

Let’s Anow Atalk Aabout Awhat Ayou Awill Alearn:

We Awill Ause Aunsupervised Amachine Alearning Aalgorithms Aand Atools Afor Aanalyzing Acomplex Adatasets. AThat Ameans Ayou Awill Alearn Aabout Aprincipal Acomponent Aanalysis, Ak-means Aclustering Aand Amore. AIf Athis Asounds Astrange Aand Anew, Athat Ais Aokay; Ait’s Awhy Awe Aare Ahere. AYou Adon’t Ahave Ato Aknow Awhat Aany Aof Athis Ameans Aat Athis Apoint. AAgain, Aall Athis Awill Abe Aaccompanied Awith Apractical Aexamples.

Then Awe Awill Alearn Aabout Arestricted ABoltzmann Amachine Aalgorithms Aand Adeep Abelief Anetworks. AThese Awill Abe Afollowed Aby Aconvolutional Aneural Anetworks, Aautoencoders, Afeature Aengineering Aand Aensemble Atechniques. AEach Achapter Awill Abegin Aby Aexplaining, Ain Ageneral Aterms, Athe Atheory Abehind Athese Atechniques.

As Aa Ageneral Aoverarching Arule, Apractice Athe Aconcepts Ain Athis Abook. AThat Ais Ahow Ayou Awill Abenefit Athe Amost Afrom Athe Alessons Ain Athis Abook. AYou Amight Afind Asome Aparts Achallenging. ADon’t Ajust Asteam Aahead. ATry Ato Afind Aextra Amaterial Athat Awill Ahelp Ayou Aunderstand Athe Aconcept, Aor Ago Aover Athe Amaterial Aagain.

Only Abegin Athe Apracticals Awhen Ayou Aare Asomewhat Aconfident Ain Ayour Aunderstanding. AThis Ais Aimportant Abecause, Aif Ayou Adon’t Ado Athe Awork, Ayou Awon’t Aunderstand Athe Amore Aadvanced Aconcepts.

Each Achapter Awill Abe Astructured Ato Ainclude Atheory, Atools Aand Aexamples Aof Areal-world Aapplications.

CHAPTER 1:

UNSUPERVISED MACHINE LEARNING

Unsupervised Amachine Alearning Ais Amade Aup Aof Aa Aset Aof Atechniques Aand Atools Acrucial Ato Aexploratory Aanalysis. AUnderstanding Athese Atools Aand Atechniques Ais Aimportant Ato Aextracting Avaluable Adata Afrom Acomplex Adatasets. AThese Atools Ahelp Areveal Apatterns Aand Astructures Ain Adata Awhich Aare Ahard Ato Adiscern Aotherwise.

That Ais Awhat Awe Awill Ado Ain Athis Achapter. AWe Awill Abegin Awith Aa Asolid Adata Amanipulation Atechnique Acalled Aprincipal Acomponent Aanalysis. AThen Awe Awill Aquickly Alook Aat Ak-means Aclustering Aand Aself-organizing Amaps. AWe Awill Athen Alearn Ahow Ato Ause Athese Atechniques Ausing AUCI AHandwritten ADigits Adatasets. ALet’s Aget Ato Ait.

Principal Component Analysis

PCA Ais Aarguably Athe Amost Apopular Alinear Adimensionality Areduction Amethod Aused Ain Abig Adata Aanalytics. AIts Aaim Ais Ato Areduce Athe Adimensionality Aof Adata Aso Ait Abecomes Aeasy Ato Amanage.

PCA Ais Aa Adecomposition Amethod Athat Ais Agood Aat Asplitting Aa Amultivariate Adataset Ainto Aorthogonal Acomponents. AThose Aelements Awill Abecome Athe Asummary Aof Athe Adata Asets, Aallowing Afor Ainsights.

It Adoes Athis Ain Aa Afew Asteps: ABy Aidentifying Athe Adataset’s Acenter Apoint, Acalculating Athe Acovariance Amatrix Aand Athe Aeigenvectors Aof Athe Amatrix. AThen Ait Aortho-normalizes Athe Aeigenvectors Aand Acalculates Athe Aproportion Aof Avariance Ain Athe Aeigenvectors. ASince Ayou Ahave Alikely Anever Aheard Aany Aof Athese Aterms, Ait Ais Aworth Agoing Ain Aand Aexplaining Athem Afurther.

Covariance A: AThis Ais Aa Avariance Abetween Atwo Aor Amore Avariables,AapplyingAtoAmultipleAdimensions.ASayAweAhaveAaAcovarianceAbetween Atwo Avariables; Awe'd Ause Aa A2 Ax A2 Amatrix Ato Adescribe Ait. AIfAthereAareA3Avariables,Awe’llAneedAaA3AxA3Amatrix,AandAonAitAgoes.AThe Afirst Aphase Aof Aany APCA Acalculation Ais Athe Acovariance Amatrix.

Eigenvector A: AThis Avector Adoesn’t Achange Adirection Awhen Aa AlinearAtransformation Ais Arealized. ALet’s Aillustrate Athis. AImagine Aholding AanAelastic Arubber Aband Abetween Ayour Ahands. AThen Ayou Astretch AtheArubber Aband. AThe Aeigenvector Awould Abe Athe Apoint Ain Athe Aband AthatAdid Anot Amove Awhen Ayou Awere Astretching Ait. AIt Ais Athe Apoint Ain AtheAmiddleAthatAstaysAatAtheAsameAplaceAbeforeAandAafterAyouAstretchAthe Aband.

Orthogonalization A: AThe Aterm Ameans Atwo Avectors Athat Aare Aat ArightAanglesAtoAeach Aother.ASimplyAreferred AtoAas Aorthogonal.

Eigenvalue A: AThe Aeigenvalue Acalculates Athe Aproportion Aof AvarianceArepresentedAbyAtheAeigenvectors.ATheAeigenvalueAcorresponds,AmoreAor Aless,Ato AtheAlength Aof AtheAeigenvector.

Here’s Aa Ashort Asummary: ACovariance Ais Aused Ato Acalculate Aeigenvectors, Aand Athen Aortho-normalization Atakes Aplace. AThis Aprocess Adescribes Ahow Aprincipal

component Aanalysis Atransforms Acomplex Adata Asets Ainto Alow Adimensional Aones.

Applying APCA

Now Alet’s Asee Ahow Athe Aalgorithm Aworks Ain Aaction. AAs Awe’ve Asaid, Awe Awill Ause Athe AUCI Ahandwritten Adigits Adataset. AYou Acan Aimport Ait Ausing AScikit-learn Abecause Ait Ais Aan Aopen-source Adataset. AThe Adataset Ahas Aabout A1800 Ainstances Aof Ahandwritten Adigits Afrom Aabout A50 Awriters. AThe Ainput Ais Acomprised Aof Apressure Aand Alocation Aand Aresampled Aon Aan A8 Ax A8 Agrid. AThis Ais Ato Ayield Amaps Athat Acan Abe Achanged Ato A64-feature Avectors. AThese Avectors Awill Abe Aused Afor Aanalysis. AWe Ause APCA Aon Athem Abecause Awe Aneed Ato Areduce Atheir Anumber, Amaking Athem Amore Amanageable. AHere Ais Ahow Athe Acode Alooks:

import Anumpy Aas Anp

from Asklearn.datasets Aimport Aload_digits Aimport Amatplotlib.pyplot Aas Aplt

from Asklearn.decomposition Aimport APCA Afrom Asklearn.preprocessing Aimport Ascale Afrom Asklearn.lda Aimport ALDA

import Amatplotlib.cm Aas Acm Adigits A= Aload_digits()

data A= Adigits.data

n_samples, An_features A= Adata.shape An_digits A= Alen(np.unique(digits.target))