Introduction complète à PySpark & Google Colab pour la science des données

Aperçu

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.

Programme

• Introduction au cours
Aperçu de PySpark
Aperçu de Google Colab
Objectifs et structure du cours
• Configuration de votre environnement
Installation de PySpark
Introduction à Google Colab
Intégration de PySpark avec Google Colab
• Comprendre PySpark
Architecture de PySpark
Jeux de données distribués résilients (RDD)
Transformations et actions
• Travailler avec les DataFrames et SQL
Création de DataFrames
Opérations sur les DataFrames
Requêtes SQL dans PySpark
• Techniques avancées de PySpark
Travailler avec PySpark MLlib pour l'apprentissage automatique
Utiliser PySpark Streaming pour les données en temps réel
Traitement de graphes avec GraphFrames
• Gérer les Big Data dans PySpark
Partitionnement et réorganisation dans PySpark
Optimisation des performances de PySpark
Meilleures pratiques pour le traitement des Big Data
• Explorer les fonctionnalités de Google Colab
Utilisation de GPU et TPU dans Colab
Collaboration en temps réel
Intégration de Google Drive avec Colab
• Construire des modèles d'IA avec PySpark et Colab
Prétraitement des données pour les modèles d'IA
Entraînement et évaluation des modèles d'apprentissage automatique
Déploiement de modèles PySpark dans Colab
• Projets réels
Projet 1 : Construction d'un pipeline de données PySpark
Projet 2 : Analyse de données en temps réel avec PySpark Streaming
Projet 3 : Entraîner un modèle d'IA à l'échelle avec PySpark sur Colab
• Conclusion du cours
Récapitulatif des concepts clés
Ressources supplémentaires et prochaines étapes
Session finale de questions-réponses
• Évaluations et projet final
Quiz hebdomadaires
Projet de synthèse : Solution de science des données de bout en bout utilisant PySpark et Colab

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Enseigné par

Minerva Singh

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Matières

Programming