Thinking-data-science-a-data-science-practitioners-guide

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8 1 Data Science ProcessFig. 1.2 Processing textcorpusExploratory Data AnalysisFigure 1.3 shows the workflow for exploratory data analysis (EDA) for understandingthe data.To understand the data, there are several libraries available for data visualization.The most commonly used Python libraries are Matplotlib and Seaborn that canprovide a very advanced level of visualization. You may do scatter plots, histograms,
Data Science Process 9Fig. 1.3 Data visualizationlibrariesFig. 1.4 Matplotlib sample outputsbar charts, pie charts, three-dimensional plots, contours, polar plots, and whateverelse you need to understand the data. Figure 1.4 shows some capabilities ofMatplotlib.Figure 1.5 shows some advanced plots created by Seaborn.Such data visualizations help in detecting outliers, understanding correlations,data distributions, and so on. We use this information for further refining our trainingdatasets.Next, we come to feature engineering.
Page 1 and 2: The Springer Series in Applied Mach
Page 3 and 4: Editorial BoardOver the last decade
Page 5 and 6: Poornachandra SarangPracticing Data
Page 7 and 8: viPrefacemachine learning algorithm
Page 9 and 10: viiiPrefaceLarge Applications) is c
Page 11 and 12: Contents1 Data Science Process ....
Page 13 and 14: ContentsxiiiTree Traversal Algorith
Page 15 and 16: ContentsxvModel Fitting for Huge Da
Page 17 and 18: Contentsxvii13 BIRCH ..............
Page 19 and 20: Contentsxix18 ANN-Based Application
Page 21 and 22: Chapter 1Data Science ProcessWith l
Page 23 and 24: AI on Image Datasets 3creates a fin
Page 25 and 26: Data Science Process 5(NLU). So, we
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Page 33 and 34: AutoML 13Fig. 1.9 Exhaustive list o
Page 35 and 36: Hyper-Parameter Tuning 15Fig. 1.11
Page 37 and 38: Models Based on Transfer Learning 1
Page 39 and 40: Chapter 2Dimensionality ReductionCr
Page 41 and 42: Dimensionality Reduction Techniques
Page 71 and 72: Summary 51Fig. 2.25 LDA performance
Page 73 and 74: Part IClassical Algorithms: Overvie
Page 75 and 76: Chapter 3Regression AnalysisA Well-
Page 77 and 78: Regression Types 57Generally, these
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Regression Types 59Polynomial Regre
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Regression Types 61ElasticNet Regre
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Regression Types 63As before, you p
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Bayesian Linear Regression 65Fig. 3
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Bayesian Linear Regression 67You ma
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Logistic Regression 69Fig. 3.8 Sigm
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Logistic Regression 71Fig. 3.9 Asso
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Summary 73those. I discussed only a
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76 4 Decision Treegrades, etc. by b
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78 4 Decision TreeFig. 4.2 Balanced
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80 4 Decision Tree¼ 1X Ci¼1pi ð
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82 4 Decision TreeInformation gain
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84 4 Decision TreeTable 4.2 Subset
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86 4 Decision TreeTable 4.4 Subset
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88 4 Decision TreeFig. 4.6 Final tr
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90 4 Decision TreeFig. 4.7 Housing
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92 4 Decision TreeEvaluating Perfor
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94 4 Decision TreeFig. 4.9 Features
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96 4 Decision TreeFig. 4.11 Decisio
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98 5 Ensemble: Bagging and Boosting
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100 5 Ensemble: Bagging and Boostin
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Chapter 6K-Nearest NeighborsA Super
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KNN Working 133• Step-3: For each
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Advantages 135Table 6.2 Euclidean d
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Project 137those who are further aw
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Project 139#Setup arrays to store t
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Summary 141Fig. 6.7 Classification
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144 7 Naive BayesWhen to Use?A few
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146 7 Naive BayesNow we substitute
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148 7 Naive BayesNaive Bayes TypesD
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150 7 Naive BayesPreparing DatasetT
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152 7 Naive BayesThe output in this
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154 8 Support Vector MachinesFig. 8
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156 8 Support Vector MachinesComple
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164 8 Support Vector MachinesTable
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Part IIClustering: OverviewClusteri
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Part II Clustering: Overview 169fal
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Chapter 9Centroid-Based ClusteringC
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The K-Means Algorithm 173observatio
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The K-Means Algorithm 175• Elbow
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The K-Means Algorithm 177The Gap St
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The K-Means Algorithm 179ProjectWe
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The K-Medoids Algorithm 181in the c
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Summary 183Fig. 9.11 Clustering wit
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186 10 Connectivity-Based Clusterin
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Chapter 11Gaussian Mixture ModelA P
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Probability Distribution 199For a m
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Project 201Fig. 11.3 Features and t
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Determining Optimal Number of Clust
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Determining Optimal Number of Clust
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Summary 207SummaryThe Gaussian mixt
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210 12 Density-Based ClusteringFig.
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212 12 Density-Based ClusteringTher
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230 13 BIRCHTo understand the BIRCH
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232 13 BIRCH3. Apply existing clust
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234 13 BIRCHFig. 13.4 BIRCH cluster
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236 13 BIRCHYou observe that the cl
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238 14 CLARANS1. Select multiple su
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240 14 CLARANSProjectFor this proje
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242 14 CLARANSThe output shows the
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244 15 Affinity Propagation Cluster
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252 16 STING & CLIQUEFig. 16.1 STIN
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254 16 STING & CLIQUEPros/ConsHere
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256 16 STING & CLIQUEIntervals = 30
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Part IIIANN: OverviewSo far, you ha
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262 17 Artificial Neural NetworksTh
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264 17 Artificial Neural NetworksNe
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266 17 Artificial Neural NetworksIm
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268 17 Artificial Neural NetworksNo
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270 17 Artificial Neural NetworksFi
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272 17 Artificial Neural Networks
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280 17 Artificial Neural NetworksFo
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284 17 Artificial Neural Networksbi
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Chapter 18ANN-Based ApplicationsTex
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Developing NLP Applications 291This
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Developing NLP Applications 293The
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Developing NLP Applications 295bert
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Developing NLP Applications 297For
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Developing NLP Applications 299test
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Developing NLP Applications 301We w
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Developing NLP Applications 303Fig.
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Developing NLP Applications 305Mode
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Developing NLP Applications 307Fig.
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Developing NLP Applications 309Glov
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Fig. 18.15 Network summaryFig. 18.1
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Developing Image-Based Applications
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Summary 327SummaryWe have several p
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330 19 Automated Toolsseveral combi
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332 19 Automated Toolsauto-sklearn
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334 19 Automated ToolsIt produced t
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336 19 Automated ToolsFig. 19.3 Reg
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338 19 Automated Toolson the best p
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340 19 Automated ToolsFig. 19.6 Mod
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342 19 Automated Toolsestablish the
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344 19 Automated ToolsFig. 19.9 Net
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346 19 Automated ToolsThe plot for
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348 19 Automated ToolsTPOTThis is a
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Chapter 20Data Scientist’s Ultima
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Workflow-0: Quick Solution 353Workf
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Workflow-4: Features Engineering 35
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Workflow-11: Clustering 357Workflow
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