{"product_id":"138142","title":"The Complete Guide to Python Machine Learning ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPCfJhzyt4A2l6Jq87YQxFLoyniW.png?v=1765060667\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e The Complete Guide to Python Machine Learning \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/108824557\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n \u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003eDescription \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eYou can master machine learning through detailed theoretical explanations and Python practice!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e \"The Complete Guide to Python Machine Learning\" breaks away from theory-focused machine learning books, allowing you to learn machine learning through hands-on implementation of various practical examples.\u003cbr\u003e We've compiled practical examples based on challenging practice data from Kaggle and the UCI Machine Learning Repository, and we've covered in detail the latest algorithms and techniques used in many data science fields on Kaggle, including XGBoost, LightGBM, and stacking techniques.\u003cbr\u003e \u003cbr\u003eThis revised second edition implements practice code that upgrades all libraries used in the book to the latest version, including the latest scikit-learn version (1.0.2), and provides practice applying Bayesian optimization techniques for optimal hyperparameter tuning of XGBoost and LightGBM models with various types of hyperparameters.\u003cbr\u003e We also added a new chapter covering the use of matplotlib and seaborn, visualization libraries widely used in machine learning-related data analysis.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eChapter 1: Understanding Python-Based Machine Learning and Its Ecosystem\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 01.\u003cbr\u003e The concept of machine learning\u003cbr\u003e ___Classification of machine learning\u003cbr\u003e ___Data Wars\u003cbr\u003e ___Comparing Python and R-based Machine Learning\u003cbr\u003e 02.\u003cbr\u003e Key packages that make up the Python machine learning ecosystem\u003cbr\u003e ___Installing software for Python machine learning\u003cbr\u003e 03.\u003cbr\u003e NumPy\u003cbr\u003e ___NumPy ndarray overview \u003cbr\u003e___ndarray data type\u003cbr\u003e Conveniently creating ___ndarray - arange, zeros, ones\u003cbr\u003e reshape( ) to change the dimensions and size of ___ndarray\u003cbr\u003e ___Selecting a dataset from NumPy's ndarray - Indexing\u003cbr\u003e Sorting a matrix - sort( ) and argsort( )\u003cbr\u003e ___Linear Algebra Operations - Matrix Inner Product and Transpose\u003cbr\u003e 04.\u003cbr\u003e Data Handling - Pandas\u003cbr\u003e ___Getting Started with Pandas - Loading Files into DataFrames, Basic API\u003cbr\u003e ___Converting DataFrame to List, Dictionary, and NumPy ndarray\u003cbr\u003e ___Creating and modifying column data sets in DataFrame\u003cbr\u003e ___Delete DataFrame data\u003cbr\u003e ___Index object\u003cbr\u003e ___Data Selection and Filtering\u003cbr\u003e ___Sorting, Aggregation function, GroupBy application\u003cbr\u003e ___Handling missing data\u003cbr\u003e Process data using ___apply lambda expression\u003cbr\u003e 05.\u003cbr\u003e organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 2: Machine Learning with Scikit-Learn\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 01.\u003cbr\u003e Introduction and Features of Scikit-learn\u003cbr\u003e 02.\u003cbr\u003e Building Your First Machine Learning Model - Predicting Iris Species\u003cbr\u003e 03.\u003cbr\u003e Learn the basic framework of scikit-learn \u003cbr\u003eUnderstanding ___Estimator and fit( ), predict( ) methods\u003cbr\u003e ___Main modules of scikit-learn\u003cbr\u003e ___Built-in example data sets\u003cbr\u003e 04.\u003cbr\u003e Introducing the Model Selection Module\u003cbr\u003e ___Separate training\/test data sets - train_test_split()\u003cbr\u003e ___Cross-validation\u003cbr\u003e ___GridSearchCV - Cross-validation and optimal hyperparameter tuning in one go 111\u003cbr\u003e 05.\u003cbr\u003e Data preprocessing\u003cbr\u003e ___data encoding\u003cbr\u003e ___Feature scaling and normalization\u003cbr\u003e ___StandardScaler\u003cbr\u003e ___MinMaxScaler\u003cbr\u003e ___Things to keep in mind when scaling training and test data\u003cbr\u003e 06.\u003cbr\u003e Predicting Titanic Survivors with Scikit-learn\u003cbr\u003e 07.\u003cbr\u003e organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 3: Evaluation\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 01.\u003cbr\u003e Accuracy\u003cbr\u003e 02.\u003cbr\u003e Error matrix\u003cbr\u003e 03.\u003cbr\u003e Precision and recall\u003cbr\u003e ___Precision\/Recall Tradeoff\u003cbr\u003e ___Blind spots in precision and recall\u003cbr\u003e 04.\u003cbr\u003e F1 score\u003cbr\u003e 05. ROC Curve and AUC\u003cbr\u003e 06.\u003cbr\u003e Pima Indian Diabetes Prediction\u003cbr\u003e 07.\u003cbr\u003e organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 4: Classification\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 01.\u003cbr\u003e Overview of Classification\u003cbr\u003e 02.\u003cbr\u003e Decision tree\u003cbr\u003e ___Features of the decision tree model\u003cbr\u003e ___Decision Tree Parameters\u003cbr\u003e ___Visualization of decision tree models \u003cbr\u003e___Decision Tree Overfitting\u003cbr\u003e ___Decision Tree Practice - User Behavior Recognition Dataset\u003cbr\u003e 03.\u003cbr\u003e ensemble learning\u003cbr\u003e ___Ensemble Learning Overview\u003cbr\u003e ___Voting Types - Hard Voting and Soft Voting\u003cbr\u003e ___Voting Classifier\u003cbr\u003e 04.\u003cbr\u003e Random Forest\u003cbr\u003e ___Overview and Practice of Random Forests\u003cbr\u003e ___Random Forest Hyperparameters and Tuning\u003cbr\u003e ___GBM Overview and Practice\u003cbr\u003e 05. GBM (Gradient Boosting Machine)\u003cbr\u003e ___Introducing GBM Hyperparameters\u003cbr\u003e ___XGBoost Overview\u003cbr\u003e 06. XGBoost (eXtra Gradient Boost)\u003cbr\u003e ___Installing XGBoost\u003cbr\u003e ___Python Wrapper for XGBoost Hyperparameters\u003cbr\u003e ___Applying Python Wrapper XGBoost - Predicting Breast Cancer in Wisconsin\u003cbr\u003e ___Overview and Application of XGBoost, a Scikit-Learn Wrapper\u003cbr\u003e 07.\u003cbr\u003e LightGBM\u003cbr\u003e ___Installing LightGBM\u003cbr\u003e ___LightGBM hyperparameters\u003cbr\u003e ___Hyperparameter tuning method\u003cbr\u003e ___Python wrapper LightGBM and scikit-learn wrapper XGBoost,\u003cbr\u003e ___LightGBM hyperparameter comparison\u003cbr\u003e ___LightGBM Application - Wisconsin Breast Cancer Prediction\u003cbr\u003e 08. \u003cbr\u003eHyperparameter tuning using Bayesian optimization-based HyperOpt\u003cbr\u003e ___Bayesian Optimization Overview\u003cbr\u003e ___Using HyperOpt\u003cbr\u003e ___XGBoost hyperparameter optimization using HyperOpt\u003cbr\u003e 09.\u003cbr\u003e Classification Practice - Kaggle Santander Customer Satisfaction Prediction\u003cbr\u003e ___Data preprocessing\u003cbr\u003e ___XGBoost model training and hyperparameter tuning\u003cbr\u003e ___LightGBM model training and hyperparameter tuning\u003cbr\u003e 10.\u003cbr\u003e Classification Practice - Kaggle Credit Card Fraud Detection\u003cbr\u003e ___Understanding Undersampling and Oversampling\u003cbr\u003e ___Data primary processing and model learning\/prediction\/evaluation\u003cbr\u003e ___Model learning\/prediction\/evaluation after data distribution transformation\u003cbr\u003e ___Model training\/prediction\/evaluation after removing outlier data\u003cbr\u003e ___Model training\/prediction\/evaluation after applying SMOTE oversampling\u003cbr\u003e 11.\u003cbr\u003e Stacking ensemble\u003cbr\u003e ___Basic Stacking Model\u003cbr\u003e ___CV set-based stacking\u003cbr\u003e 12.\u003cbr\u003e organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 5: Regression\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 01.\u003cbr\u003e Introduction to Regression\u003cbr\u003e 02.\u003cbr\u003e Understanding Regression through Simple Linear Regression\u003cbr\u003e 03.\u003cbr\u003e Minimizing Costs - Introducing Gradient Descent\u003cbr\u003e 04. \u003cbr\u003ePredicting Boston Housing Prices Using Scikit-Learn LinearRegression\u003cbr\u003e ___LinearRegression Class - Ordinary Least Squares\u003cbr\u003e ___Regression Evaluation Index\u003cbr\u003e ___Implementing Boston Housing Price Regression Using LinearRegression\u003cbr\u003e 05.\u003cbr\u003e Understanding Polynomial Regression and Overfitting\/Underfitting\u003cbr\u003e ___Understanding Polynomial Regression\u003cbr\u003e ___Understanding Underfitting and Overfitting Using Multinomial Regression\u003cbr\u003e ___Bias-Variance Tradeoff\u003cbr\u003e 06.\u003cbr\u003e Regularized Linear Models - Ridge, Lasso, ElasticNet\u003cbr\u003e ___Overview of the Regulatory Linear Model\u003cbr\u003e ___ridge regression\u003cbr\u003e ___Lasso regression\u003cbr\u003e ___ElasticNet Regression\u003cbr\u003e ___Data Transformation for Linear Regression Models\u003cbr\u003e 07.\u003cbr\u003e logistic regression\u003cbr\u003e 08.\u003cbr\u003e Regression tree\u003cbr\u003e 09.\u003cbr\u003e Regression Exercise - Predicting Bike Rental Demand\u003cbr\u003e ___Data cleansing and processing and data visualization\u003cbr\u003e ___Log transformation, feature encoding, and model training\/prediction\/evaluation\u003cbr\u003e 10.\u003cbr\u003e Regression Practice - Kaggle House Prices: Advanced Regression Techniques\u003cbr\u003e ___Data Preprocessing\u003cbr\u003e ___Linear regression model training\/prediction\/evaluation\u003cbr\u003e ___Regression tree model training\/prediction\/evaluation \u003cbr\u003eFinal prediction through mixing the prediction results of the ___ regression model\u003cbr\u003e ___Regression prediction using stacking ensemble models\u003cbr\u003e 11.\u003cbr\u003e organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 6: Dimensionality Reduction\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 01.\u003cbr\u003e Dimension Reduction Overview\u003cbr\u003e 02. PCA (Principal Component Analysis)\u003cbr\u003e ___PCA Overview\u003cbr\u003e 03. LDA (Linear Discriminant Analysis)\u003cbr\u003e ___LDA Overview\u003cbr\u003e 04. SVD(Singular Value Decomposition)\u003cbr\u003e ___SVD Overview\u003cbr\u003e ___Transformation using scikit-learn TruncatedSVD class\u003cbr\u003e 05. NMF (Non-Negative Matrix Factorization)\u003cbr\u003e ___NMF Overview\u003cbr\u003e 06.\u003cbr\u003e organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 7: Clustering\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 01.\u003cbr\u003e Understanding the K-Means Algorithm\u003cbr\u003e ___Introducing the scikit-learn KMeans class\u003cbr\u003e Clustering the Iris Dataset Using ___K-Means\u003cbr\u003e ___Generate data for testing clustering algorithms\u003cbr\u003e 02.\u003cbr\u003e Cluster Evaluation\u003cbr\u003e ___Overview of Silhouette Analysis\u003cbr\u003e Cluster evaluation using the ___iris data set\u003cbr\u003e ___A method for optimizing the number of clusters by visualizing the average silhouette coefficient per cluster.\u003cbr\u003e 03.\u003cbr\u003e moving average\u003cbr\u003e ___Overview of Mean Shift\u003cbr\u003e 04. GMM (Gaussian Mixture Model)\u003cbr\u003e Introduction to ___GMM (Gaussian Mixture Model) \u003cbr\u003eClustering the Iris Dataset Using ___GMM\u003cbr\u003e ___Comparison of GMM and K-Means\u003cbr\u003e 05. DBSCAN\u003cbr\u003e ___DBSCAN Overview\u003cbr\u003e ___Applying DBSCAN - Iris Dataset\u003cbr\u003e ___Applying DBSCAN - make_circles( ) data set\u003cbr\u003e 06.\u003cbr\u003e Clustering Practice - Customer Segmentation\u003cbr\u003e ___Definition and techniques of customer segmentation\u003cbr\u003e ___Dataset loading and data cleansing\u003cbr\u003e ___RFM-based data processing\u003cbr\u003e ___RFM-based customer segmentation\u003cbr\u003e 07.\u003cbr\u003e organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 8 Text Analysis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ___NLP or text analysis?\u003cbr\u003e 01.\u003cbr\u003e Understanding Text Analysis\u003cbr\u003e ___Text Analysis Process\u003cbr\u003e ___Python-based NLP and text analysis package\u003cbr\u003e 02.\u003cbr\u003e Text Preprocessing - Text Normalization\u003cbr\u003e ___Cleansing\u003cbr\u003e ___Text tokenization\u003cbr\u003e ___Remove stop words\u003cbr\u003e ___Stemming and Lemmatization\u003cbr\u003e 03.\u003cbr\u003e Bag of Words - BOW\u003cbr\u003e ___BOW feature vectorization\u003cbr\u003e ___Scikit-learn's implementation of Count and TF-IDF vectorization: CountVectorizer, TfidfVectorizer\u003cbr\u003e ___Sparse matrices for BOW vectorization\u003cbr\u003e ___Sparse matrix - COO format\u003cbr\u003e ___Sparse matrix - CSR format\u003cbr\u003e 04. \u003cbr\u003eText Classification Practice - Classifying 20 Newsgroups\u003cbr\u003e ___Text Normalization\u003cbr\u003e ___Feature vectorization transformation and machine learning model training\/prediction\/evaluation\u003cbr\u003e ___Using scikit-learn pipelines and combining them with GridSearchCV\u003cbr\u003e 05.\u003cbr\u003e Sentiment Analysis\u003cbr\u003e ___Introduction to Sentiment Analysis\u003cbr\u003e ___Supervised Learning-Based Sentiment Analysis Practice - IMDB Movie Reviews\u003cbr\u003e ___Introduction to Unsupervised Learning-Based Sentiment Analysis\u003cbr\u003e Sentiment Analysis Using SentiWordNet\u003cbr\u003e Sentiment Analysis Using ___VADER\u003cbr\u003e 06.\u003cbr\u003e Topic Modeling - 20 Newsgroups\u003cbr\u003e 07.\u003cbr\u003e Introduction and Practice of Document Clustering (Opinion Review Dataset)\u003cbr\u003e ___Document clustering concept\u003cbr\u003e ___Performing document clustering using the Opinion Review dataset\u003cbr\u003e ___Extracting key words by cluster\u003cbr\u003e 08.\u003cbr\u003e Document similarity\u003cbr\u003e ___Method for Measuring Document Similarity - Cosine Similarity\u003cbr\u003e ___Angle between two vectors\u003cbr\u003e ___Measuring Document Similarity Using the Opinion Review Dataset\u003cbr\u003e 09.\u003cbr\u003e Korean Text Processing - Naver Movie Rating Sentiment Analysis\u003cbr\u003e ___Difficulties in Korean NLP Processing\u003cbr\u003e ___Introducing KoNLPy\u003cbr\u003e ___Data loading\u003cbr\u003e 10. \u003cbr\u003eText Analysis Practice - Kaggle Mercari Price Suggestion Challenge\u003cbr\u003e ___Data preprocessing\u003cbr\u003e ___Feature encoding and feature vectorization\u003cbr\u003e Building and Evaluating a Ridge Regression Model\u003cbr\u003e ___Building a LightGBM regression model and evaluating the final predictions using an ensemble\u003cbr\u003e 11.\u003cbr\u003e organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 9: Recommender Systems\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 01.\u003cbr\u003e Overview and Background of Recommender Systems\u003cbr\u003e ___Overview of the recommendation system\u003cbr\u003e ___An Essential Element of Online Stores: Recommendation Systems\u003cbr\u003e ___Types of Recommendation Systems\u003cbr\u003e 02.\u003cbr\u003e Content-based filtering recommendation system\u003cbr\u003e 03.\u003cbr\u003e Nearest Neighbor Collaborative Filtering\u003cbr\u003e 04.\u003cbr\u003e Latent Factor Collaborative Filtering\u003cbr\u003e ___Understanding Latent Factor Collaborative Filtering\u003cbr\u003e Understanding ___Matrix Decomposition\u003cbr\u003e ___Matrix decomposition using stochastic gradient descent\u003cbr\u003e 05.\u003cbr\u003e Content-Based Filtering Practice - TMDB 5000 Movie Dataset\u003cbr\u003e Movie content-based filtering using ___genre attributes\u003cbr\u003e ___Data loading and processing\u003cbr\u003e ___Genre Content Similarity Measurement\u003cbr\u003e Movie recommendations using ___genre content filtering\u003cbr\u003e 06.\u003cbr\u003e Item-based nearest neighbor collaborative filtering practice \u003cbr\u003e___Data processing and conversion\u003cbr\u003e Calculating similarity between ___movies\u003cbr\u003e Personalized movie recommendations using item-based nearest neighbor collaborative filtering.\u003cbr\u003e 07.\u003cbr\u003e Latent Factor Collaborative Filtering Practice Using Matrix Factorization\u003cbr\u003e ___Introducing the Surprise Package\u003cbr\u003e 08.\u003cbr\u003e Python Recommender System Package - Surprise\u003cbr\u003e Building a Recommendation System Using ___Surprise\u003cbr\u003e ___Introducing the main modules of Surprise\u003cbr\u003e ___Surprise Recommendation Algorithm Class\u003cbr\u003e ___Baseline Score\u003cbr\u003e ___Cross-validation and hyperparameter tuning\u003cbr\u003e Building a personalized movie recommendation system using ___Surprise\u003cbr\u003e 09.\u003cbr\u003e organize\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 10: Visualization\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 01.\u003cbr\u003e Getting Started with Visualization - An Overview of Matplotlib and Seaborn\u003cbr\u003e 02.\u003cbr\u003e Matplotlib\u003cbr\u003e ___Understanding Matplotlib's pyplot module\u003cbr\u003e Understanding Two Key Elements of ___pyplot - Figures and Axes\u003cbr\u003e ___Using Figure and Axis\u003cbr\u003e ___Creating subplots with multiple plots\u003cbr\u003e Draw a line graph using ___pyplot's plot( ) function\u003cbr\u003e ___Set the axis name, rotate the axis tick values, and set the legend. \u003cbr\u003e___Visualize individual graphs by subplot using multiple subplots\u003cbr\u003e 03.\u003cbr\u003e Seaborn\u003cbr\u003e ___Chart\/Graph Types for Visualization\u003cbr\u003e ___Types of visualization charts based on the type of information\u003cbr\u003e ___Histogram\u003cbr\u003e ___count plot\u003cbr\u003e ___barplot\u003cbr\u003e Use the hue argument of the ___barplot( ) function to further refine the visualization information.\u003cbr\u003e ___box plot\u003cbr\u003e ___violin plot\u003cbr\u003e Visualize various graphs in Seaborn using ___subplots\u003cbr\u003e ___Scatter plot\u003cbr\u003e ___Correlation Heatmap\u003cbr\u003e 04.\u003cbr\u003e organize\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate3810\/MidCate007\/380960059(1).jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eFeatures of this book\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ◎ In-depth explanations of core machine learning algorithms, including classification, regression, dimensionality reduction, and clustering.\u003cbr\u003e ◎ Presentation of optimal machine learning model configuration methods, including data preprocessing, machine learning algorithm application, hyperparameter tuning, and performance evaluation. \u003cbr\u003e◎ Detailed explanations and usage methods for the latest machine learning techniques, such as XGBoost, LightGBM, and stacking.\u003cbr\u003e ◎ Learn practical machine learning application development methods by solving challenging Kaggle problems (e.g., predicting customer satisfaction at Santander Bank, detecting credit card fraud, using advanced regression techniques to predict real estate prices, and predicting prices at Mercari shopping malls).\u003cbr\u003e ◎ Provides basic theories and various practical examples for text analysis and NLP (text classification, sentiment analysis, topic modeling, document similarity, document clustering and similarity, sentiment analysis of Naver movies using KoNLPy, etc.)\u003cbr\u003e Provides instructions for building various recommendation systems directly with Python code. \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e April 21, 2022\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 724 pages | 188*240*29mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791158393229\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1158393229 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893203566634,"sku":"138142","price":48.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/53ab7714f09d7d91f5d0dd17769d4f42.jpg?v=1765391834","url":"https:\/\/librairie.coreenne.fr\/en\/products\/138142","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}