Complete PySpark & Google Colab Primer For Data Science

Overview

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.

Syllabus

• Introduction to the Course
Overview of PySpark
Overview of Google Colab
Course objectives and structure
• Setting Up Your Environment
Installing PySpark
Introduction to Google Colab
Integrating PySpark with Google Colab
• Understanding PySpark
PySpark Architecture
Resilient Distributed Datasets (RDDs)
Transformations and Actions
• Working with DataFrames and SQL
Creating DataFrames
DataFrame Operations
SQL Queries in PySpark
• Advanced PySpark Techniques
Working with PySpark MLlib for Machine Learning
Using PySpark Streaming for Real-time Data
Graph Processing with GraphFrames
• Handling Big Data in PySpark
Partitioning and Shuffling in PySpark
Optimizing PySpark Performance
Best Practices for Big Data Processing
• Exploring Google Colab Features
Using GPUs and TPUs in Colab
Collaborating in Real-time
Integrating Google Drive with Colab
• Building AI Models with PySpark and Colab
Preprocessing Data for AI Models
Training and Evaluating Machine Learning Models
Deploying PySpark Models in Colab
• Real-world Projects
Project 1: Building a PySpark Data Pipeline
Project 2: Real-time Data Analysis with PySpark Streaming
Project 3: Scalably Training an AI Model with PySpark on Colab
• Course Conclusion
Recap of Key Concepts
Additional Resources and Next Steps
Final Q&A Session
• Assessments and Final Project
Weekly Quizzes
Capstone Project: End-to-End Data Science Solution Using PySpark and Colab

Taught by

Minerva Singh

Subjects

Programming