Materias:

Materias:

Tabla de Contenidos: “…-- Technology for data analytics -- The data analytics toolbox -- From data to business value -- Summary -- Chapter 2: Getting Started with KNIME -- KNIME in a nutshell -- Moving around in KNIME -- Nodes -- Hello World in KNIME -- CSV Reader -- Sorter -- Excel Writer -- Cleaning data -- Excel Reader -- Duplicate Row Filter -- String Manipulation -- Row Filter -- Missing Value -- Column Filter -- Column Rename -- Column Resorter -- CSV Writer -- Summary -- Chapter 3: Transforming Data -- Modeling your data -- Combining tables -- Joiner -- Aggregating values -- GroupBy -- Pivoting -- Tutorial: Sales report automation -- Concatenate -- Number To String -- Math Formula -- Group Loop Start -- Loop End -- String to Date&amp -- Time -- Date&amp -- Time-based Row Filter -- Table Row to Variable -- Extract Date&amp -- Time Fields -- Line Plot -- Image Writer (Port) -- Summary -- Chapter 4: What is Machine Learning? …”

Tabla de Contenidos: “…Henry's Ecumenical Art Chapel / Matti Sanaksenaho, Sanaksenaho Architects ; Viikki Church / Samuli Miettinen, JKMM Architects ; Hansarinne Row Houses / ARK-house Architects ; The Swedish School of Social Sciences, University of Helsinki / Juha Leiviskä ; Enter, Sipoo Upper Secondary School / K2S Architects ; Turku Central Library / Asmo Jaaksi, JKMM Architects ; Villa Nuotta / Tuomo Siitonen Architects…”

Tabla de Contenidos: “…Chapter 2: Build a Breast Cancer Prognosis Pipeline with the Power of Spark and Scala -- Breast cancer classification problem -- Breast cancer dataset at a glance -- Logistic regression algorithm -- Salient characteristics of LR -- Binary logistic regression assumptions -- A fictitious dataset and LR -- LR as opposed to linear regression -- Formulation of a linear regression classification model -- Logit function as a mathematical equation -- LR function -- Getting started -- Setting up prerequisite software -- Implementation objectives -- Implementation objective 1 - getting the breast cancer dataset -- Implementation objective 2 - deriving a dataframe for EDA -- Step 1 - conducting preliminary EDA -- Step 2 - loading data and converting it to an RDD[String] -- Step 3 - splitting the resilient distributed dataset and reorganizing individual rows into an array -- Step 4 - purging the dataset of rows containing question mark characters -- Step 5 - running a count after purging the dataset of rows with questionable characters -- Step 6 - getting rid of header -- Step 7 - creating a two-column DataFrame -- Step 8 - creating the final DataFrame -- Random Forest breast cancer pipeline -- Step 1 - creating an RDD and preprocessing the data -- Step 2 - creating training and test data -- Step 3 - training the Random Forest classifier -- Step 4 - applying the classifier to the test data -- Step 5 - evaluating the classifier -- Step 6 - running the pipeline as an SBT application -- Step 7 - packaging the application -- Step 8 - deploying the pipeline app into Spark local -- LR breast cancer pipeline -- Implementation objectives -- Implementation objectives 1 and 2 -- Implementation objective 3 - Spark ML workflow for the breast cancer classification task -- Implementation objective 4 - coding steps for building the indexer and logit machine learning model…”

Tabla de Contenidos:

Tabla de Contenidos: “…Discovering associations between continuous and discrete variables -- Expressing dependencies with a linear regression formula -- Summary -- Chapter 7: Unsupervised Machine Learning -- Installing ML services (In-Database) packages -- Performing market-basket analysis -- Finding clusters of similar cases -- Principal components and factor analyses -- Summary -- Chapter 8: Supervised Machine Learning -- Evaluating predictive models -- Using the Naive Bayes algorithm -- Predicting with logistic regression -- Trees, forests, and more trees -- Predicting with T-SQL -- Summary -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…-- Importing data -- Importing data into pandas from Python data structures -- Importing data into pandas from a flat file -- Importing data into pandas from a database -- Common operations on DataFrames -- Adding columns -- Adding blank or user-initialized columns -- Adding new columns by transforming existing columns -- Dropping columns -- Applying functions to multiple columns -- Combining DataFrames -- Converting DataFrame columns to lists -- Getting and setting DataFrame values -- Getting/setting values using label-based indexing with loc -- Getting/setting values using integer-based labeling with iloc -- Getting/setting multiple contiguous values using slicing -- Fast getting/setting of scalar values using at and iat -- Other operations -- Filtering rows using Boolean indexing -- Sorting rows -- SQL-like operations -- Getting aggregate row COUNTs -- Joining DataFrames -- Introduction to scikit-learn -- Sample data -- Data preprocessing -- One-hot encoding of categorical variables -- Scaling and centering -- Binarization -- Imputation -- Feature-selection -- Machine learning algorithms -- Generalized linear models -- Ensemble methods -- Additional machine learning algorithms…”

Tabla de Contenidos: “…. -- See also -- Predicting forest coverage types -- Getting ready -- How to do it... -- How it works... -- There's more... -- Estimating forest elevation -- Getting ready -- How to do it... -- How it works... -- There's more... -- Clustering forest cover types -- Getting ready -- How to do it... -- How it works... -- See also -- Tuning hyperparameters -- Getting ready -- How to do it... -- How it works... -- There's more... -- Extracting features from text -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Discretizing continuous variables -- Getting ready -- How to do it... -- How it works... -- Standardizing continuous variables -- Getting ready -- How to do it... -- How it works... -- Topic mining -- Getting ready -- How to do it... -- How it works... -- Chapter 7: Structured Streaming with PySpark -- Introduction -- Understanding Spark Streaming…”

Tabla de Contenidos: “…Aggregating transforms -- 5.3.1. Combining many rows into summary rows -- 5.4. Multitable data transforms -- 5.4.1. …”

Tabla de Contenidos: “…Introduction to Pandas -- D.1 Pandas -- D.1.1 DataFrame -- D.1.2 Series -- D.1.3 Index -- D.1.4 Accessing rows -- D.1.5 Splitting a DataFrame -- D.2 Operations -- D.2.1 Element-wise operations -- D.2.2 Filtering -- D.2.3 String operations -- D.2.4 Summarizing operations -- D.2.5 Missing values -- D.2.6 Sorting -- D.2.7 Grouping -- Appendix E. …”

Tabla de Contenidos: “…Chapter 9: Understanding Feature Engineering and Preparing Data for Modeling -- Chapter 10: Mastering Machine Learning Concepts -- Introducing the machine learning workflow -- Problem statement -- Model selection -- Model tuning -- Model predictions -- Getting started with supervised machine learning -- Regression versus classification -- Linear regression - regression -- Logistic regression -- k-nearest neighbors (k-NN) -- Random forest -- Extreme Gradient Boosting (XGBoost) -- Getting started with unsupervised machine learning -- K-means -- Density-based spatial clustering of applications with noise (DBSCAN) -- Other clustering algorithms -- Evaluating clusters -- Summarizing other notable machine learning models -- Understanding the bias-variance trade-off -- Tuning with hyperparameters -- Grid search -- Random search -- Bayesian optimization -- Summary -- Chapter 11: Building Networks with Deep Learning -- Introducing neural networks and deep learning -- Weighing in on weights and biases -- Introduction to weights -- Introduction to biases -- Activating neurons with activation functions -- Common activation functions -- Choosing the right activation function -- Unraveling backpropagation -- Gradient descent -- What is backpropagation? …”