Materias:

Materias:

Materias: “…Mines and mineral resources Canada Periodicals…”

“…Learn how to use Instagram's developer platform, API, and sandbox Explore basic techniques used in computer vision and artificial intelligence Gain experience using Google's deep learning APIs to automatically label images and detect faces Enhance your ability to build applications that can mine data from social media platforms Mikhail Klassen refined his expertise in data mining, data analysis, and machine learning during his work as a PhD in astrophysics. …”

Materias: “…Copper mines and mining Political aspects Congo (Democratic Republic)…”

Materias: “…Data mining…”

Materias:

“…Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. …”

Tabla de Contenidos: “…Some areas of the application of mathematical statistics in mining; 3. Analysis of data; 4. Synthesis of data; 5. …”

Materias: “…Enginyeria de mines…”

Materias: “…Mining engineering Periodicals…”

Materias: “…Data mining…”

Materias:

Tabla de Contenidos: “…The interface between the engine and the data warehouse -- The data mining algorithms -- User interface -- Clarity -- Clarity and mystery -- Clarity and simplicity -- Efficiency -- Consistency -- Syntax highlight -- Auto-completion -- How to build a data mining architecture in R -- Data sources -- The data warehouse -- The data mining engine -- The interface between the engine and the data warehouse -- The data mining algorithms -- The user interface -- Further references -- Summary -- Chapter 5: How to Address a Data Mining Problem - Data Cleaning and Validation -- On a quiet day -- Data cleaning -- Tidy data -- Analysing the structure of our data -- The str function -- The describe function -- head, tail, and View functions -- Evaluating your data tidiness -- Every row is a record -- Every column shows an attribute -- Every table represents an observational unit -- Tidying our data -- The tidyr package -- Long versus wide data -- The spread function -- The gather function -- The separate function -- Applying tidyr to our dataset -- Validating our data -- Fitness for use -- Conformance to standards -- Data quality controls -- Consistency checks -- Data type checks -- Logical checks -- Domain checks -- Uniqueness checks -- Performing data validation on our data -- Data type checks with str() -- Domain checks -- The final touch - data merging -- left_join function -- moving beyond left_join -- Further references -- Summary -- Chapter 6: Looking into Your Data Eyes - Exploratory Data Analysis -- Introducing summary EDA -- Describing the population distribution -- Quartiles and Median -- Mean -- The mean and phenomenon going on within sub populations -- The mean being biased by outlier values -- Computing the mean of our population -- Variance -- Standard deviation -- Skewness -- Measuring the relationship between variables -- Correlation…”