- What Is This Course About?
- Data and Code
- Python Installation
- Start With Google Colaboratory Environment
- Google Colabs and GPU
- Google Colab Packages
- What is PySpark?
- Run PySpark Within Google CoLab
Online
₹ 449 3,499
Quick facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Google Colaboratory is a free, in-browser interactive programming environment that supplies deep learning researchers and engineers with an immersive and simple platform to collaborate on their data science projects. Google Colab allows anyone to compose and run arbitrary Python scripts in the browser, making it ideal for machine learning and data analysis. Minerva Singh - Instructor & Data Scientist developed the Complete PySpark & Google Colab Primer For Data Science certification course, which is available on Udemy.
Complete PySpark & Google Colab Primer For Data Science online training incorporates 4.5 hours of comprehensive lecture accompanied by articles and downloadable resources and is intended for individuals who want to use the capabilities of Google Colab for working with PySpark and Python-based AI modeling. The Complete PySpark & Google Colab Primer For Data Science online course discusses the methodologies involved with data processing, statistical analysis, correlation analysis, statistical modeling, and much more.
The highlights
- Certificate of completion
- Self-paced course
- 4.5 hours of pre-recorded video content
- 1 article
- 1 downloadable resource
Program offerings
- Online course
- Learning resources
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
certificate availability
certificate providing authority
What you will learn
After completing the Complete PySpark & Google Colab Primer For Data Science online certification, individuals will gather a comprehensive knowledge of the functionalities of PySpark and Google Colab Primer for data science activities. Individuals will explore the fundamentals associated with deep learning, machine learning, artificial intelligence, artificial neural networks, classification, regression, deep neural networks, and logistic regression as well as will learn about the strategies involved with statistical analysis and correlation analysis. Individuals will learn about logistic regression models, deep learning models, and methodologies involved with statistical modeling.
The syllabus
Welcome To The Course
Get Your Data Into Google Drive
- Mount Your Drive
- Opening a Jupyter Notebook
- Accessing Data Within the Drive
- Upload Data From a Local Drive
- Install New Packages
Getting Started With Spark Within Google Colab
- Let's Start Sparkling
- Troubleshoot
- In Case Everything Is Properly Installed.
- Read CSV into the Spark Framework
- Basic Data Exploration
- Data Summarisation
- Data Standardisation
- User Defined Functions (UDF)
Basic Statistical Modelling
- Correlation Theory
- Implement a Correlation Analysis
- OLS
- Implement an OLS Model
- ElasticNet Regression
- What are GLMs?
- Implement a Logistic Regression Model
- Theory of Accuracy Assessment
- The Anatomy of a PySpark Model
- Dealing With a Mixed Dataset
Welcome to Machine Learning
- What Is Machine Learning?
- ML description
- RF Theory
- Implement a Multi-Class Random Forest Model
- Evaluate the RF Model Accuracy
- Random Forest Regression
- Introduction to Pipelines
- Using Pipelines
- Unsupervised Classification-k means theory
- Implement a K-Means Model
Introduction To Artificial Intelligence (AI)
- What Is AI?
- Theory Behind ANN and DNN
- Set Up a Neural Network Problem
- Model Fitting
- ANN With a Mixed Dataset
- Activation Function
Instructors
Ms Minerva Singh
Data Scientist
Udemy
Other Masters, Ph.D, M.Phil.