Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | VTT | Size: 1.93 GB | Duration: 4h 15m
What you’ll learn
Get started with Google Colab- A powerful GPU powered cloud based environment for Python AI
Get Familiar With PySpark- Its Uses and Functioning
Work With PySpark Within the Google Colab Environment
Carry out Data Processing Using PySpark
Implement Common Statistical Analysis using PySpark
Implement Common Machine Learning Techniques- Classification and Regression on Real Data
Implement Deep Learning Models Within PySpark
Description
YOUR COMPLETE GUIDE TO PYSPARK AND GOOGLE COLAB: POWERFUL FRAMEWORK FOR ARTIFICIAL INTELLIGENCE (AI)
This course covers the main aspects of 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 in Python, 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 Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I finished a PhD at Cambridge University, UK, where I specialized in data science models.
I have +5 years of experience 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 there do not account for the multidimensional nature of the topic.
This course will give you a robust grounding in the main aspects of working with PySpark- your gateway to Big Data
Unlike other instructors, I dig deep into the data science features 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 performance using 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 data
You will learn how to implement both supervised and unsupervised algorithms using the Pyspark framework
Implement both Artificial Neural Networks (ANN) and Deep Neural Networks (DNNs) with the Pyspark framework
Work with real data within the framework
NO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING OR BIG DATA KNOWLEDGE IS REQUIRED:
You’ll start by absorbing the most valuable Pyspark Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.
My course will help you implement the methods using real data obtained 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.
Who this course is for:
Students With a Basic Exposure To/Interest In Python Data Science
Students Wanting to Leverage the Power of Google Colab For Python based AI Modelling
Students Wanting to Start Using PySpark For Machine Learning Applications
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