Guía Completa de PySpark y Google Colab para Ciencia de Datos

Resumen

Develop Practical Machine Learning & Neural Network Models With PySpark and Google Colab What you'll learn:

Get started with Google Colab- A powerful GPU powered cloud based environment for Python AIGet Familiar With PySpark- Its Uses and FunctioningWork With PySpark Within the Google Colab EnvironmentCarry out Data Processing Using PySparkImplement Common Statistical Analysis using PySparkImplement Common Machine Learning Techniques- Classification and Regression on Real DataImplement Deep Learning Models Within PySpark YOUR COMPLETE GUIDE TO PYSPARK AND GOOGLE COLAB:

POWERFUL FRAMEWORKFORARTIFICIALINTELLIGENCE (AI) This course coversthe main aspectsof the PySpasrk Big Data ecosystem within the Google CoLab framework. If you take this course, you can do away with taking other courses or buying books on PySpark based analytics as my course has the most updated information and syntax.

Plus, you learn to channelise the power of PySpark within a powerful Python AI framework- Google Colab. In this age of big data, companies across the globe use Pyspark to sift through the avalanche of information at their disposal, courtesy Big Data.

By becoming proficient in machine learning, neural networks and deep learning via a powerful framework, H2O inPython, you can give your company a competitive edge and boost your career to the next level!LEARN FROM AN EXPERT DATA SCIENTIST:

My name is Minerva Singhand Iam an Oxford University MPhil (Geography and Environment), graduate. I finished aPhD at Cambridge University, UK, where I specialized in data science models.

I have +5 yearsofexperience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals.Over the course of my research, I realized almost all the data science courses and books out theredo not account for the multidimensional nature of the topic. This course will give you a robust grounding in the mainaspects of working with PySpark- your gateway to Big Data Unlike other instructors, I dig deep into the data sciencefeatures of Pyspark and their implementation via Google Colab and give you a one-of-a-kind grounding You will go all the way from carrying out data reading & cleaning to finally implementing powerful machine learning and neural networks algorithms and evaluating their performanceusing Pyspark.Among other things:

You will be introduced to Google Colab, a powerful framework for implementing data science via your browser.

You will be introduced to important concepts of machine learning without jargon. Learn to install PySpark within the Colab environment and use it for working with dataYou will learn how to implement both supervised and unsupervised algorithms using the Pyspark frameworkImplement both Artificial Neural Networks (ANN) and Deep Neural Networks (DNNs) with the Pyspark frameworkWork with real data within the frameworkNO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING ORBIGDATA KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable Pyspark Data Science basics and techniques.

I useeasy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python. My course willhelp youimplement the methods using real dataobtained from different sources.

Many courses use made-up data that does not empower students to implement Pyspark-based data science in real-life.After taking this course, you’ll easily use the latest Pyspark techniques to implement novel data science techniques straight from your browser. You will get your hands dirty with real-life data and problems You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.

We will also work with real data and you will have access to all the code and data used in the course. JOIN MY COURSE NOW!IAMHERETOSUPPORTYOUTHROUGHOUTYOURJOURNEYINCASEYOUARENOTSATISFIED, THEREISA30-DAYNOQUIBBLEMONEYBACKGUARANTEE.

Programa

• Introducción al Curso
Resumen de PySpark
Resumen de Google Colab
Objetivos y estructura del curso
• Configuración de tu Entorno
Instalación de PySpark
Introducción a Google Colab
Integración de PySpark con Google Colab
• Entendiendo PySpark
Arquitectura de PySpark
Conjuntos de Datos Distribuidos Resilientes (RDDs)
Transformaciones y Acciones
• Trabajo con DataFrames y SQL
Creación de DataFrames
Operaciones con DataFrames
Consultas SQL en PySpark
• Técnicas Avanzadas de PySpark
Uso de PySpark MLlib para Aprendizaje Automático
Uso de PySpark Streaming para Datos en Tiempo Real
Procesamiento de Grafos con GraphFrames
• Manejo de Big Data en PySpark
Particionamiento y Reorganización en PySpark
Optimización del Rendimiento de PySpark
Mejores Prácticas para el Procesamiento de Big Data
• Explorando las Características de Google Colab
Uso de GPUs y TPUs en Colab
Colaboración en Tiempo Real
Integración de Google Drive con Colab
• Creación de Modelos de IA con PySpark y Colab
Preprocesamiento de Datos para Modelos de IA
Entrenamiento y Evaluación de Modelos de Aprendizaje Automático
Despliegue de Modelos PySpark en Colab
• Proyectos del Mundo Real
Proyecto 1: Creación de una Canalización de Datos con PySpark
Proyecto 2: Análisis de Datos en Tiempo Real con PySpark Streaming
Proyecto 3: Entrenamiento Escalable de un Modelo de IA con PySpark en Colab
• Conclusión del Curso
Resumen de Conceptos Clave
Recursos Adicionales y Próximos Pasos
Sesión Final de Preguntas y Respuestas
• Evaluaciones y Proyecto Final
Cuestionarios Semanales
Proyecto Final: Solución Completa de Ciencia de Datos Usando PySpark y Colab

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Impartido por

Minerva Singh

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Materias

Programming