Udemy

Platform

English

Language

Data Science

Category

Learn Automated Machine Learning (Auto ML) With 10 Projects

In the course you are going to learn about Automated Machine Learning (Auto ML) with python using google colab.

3.00 (42 reviews)

Learn Automated Machine Learning (Auto ML) With 10 Projects

Students

3.5 hours

Content

Jun 2021

Last Update
Regular Price


What you will learn

Learn to build model with AutoSklearn

Learn to build model with TPOT

Learn to build model with Auto Keras


Description

Automated Machine Learning(AutoML) is currently one of the explosive subfields within Data Science. It sounds great for those who are not fluent in machine learning and terrifying for current Data Scientists. In this course, we are going to provide students with knowledge of Automated Machine Learning (Auto ml). Students will learn to use Auto Sklearn, Auto Keras, TPOT in their real-world problems.

A groundbreaking study in 2020 reported 90% of the entirety of the world’s data has been created within the previous two years. Let that sink in. In just two years, we've collected and processed 9x the amount of information than the previous 92,000 years of humankind combined. And it isn’t slowing down. It’s projected we’ve already created 2.7 zettabytes of data, and by 2025, that number will balloon to an astounding 44 zettabytes.

What do we do with all of this data? How do we make it useful to us? What are it's real-world applications? These questions are the domain of data science.

Every company will say they’re doing a form of data science, but what exactly does that mean? The field is growing so rapidly, and revolutionizing so many industries, it's difficult to fence in its capabilities with a formal definition, but generally data science is devoted to the extraction of clean information from raw data for the formulation of actionable insights.

Commonly referred to as the “oil of the 21st century," our digital data carries the most importance in the field. It has incalculable benefits in business, research and our everyday lives. Your route to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even the health data from your fitness tracker are all important to different data scientists in different ways. Sifting through massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights and making our lives more convenient.A groundbreaking study in 2013 reported 90% of the entirety of the world’s data has been created within the previous two years. Let that sink in. In just two years, we've collected and processed 9x the amount of information than the previous 92,000 years of humankind combined. And it isn’t slowing down. It’s projected we’ve already created 2.7 zettabytes of data, and by 2020, that number will balloon to an astounding 44 zettabytes.

What do we do with all of this data? How do we make it useful to us? What are it's real-world applications? These questions are the domain of data science.

Every company will say they’re doing a form of data science, but what exactly does that mean? The field is growing so rapidly, and revolutionizing so many industries, it's difficult to fence in its capabilities with a formal definition, but generally data science is devoted to the extraction of clean information from raw data for the formulation of actionable insights.

Commonly referred to as the “oil of the 21st century," our digital data carries the most importance in the field. It has incalculable benefits in business, research and our everyday lives. Your route to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even the health data from your fitness tracker are all important to different data scientists in different ways. Sifting through massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights and making our lives more convenient.

In this course, we are going to work on

  • Credit card fraud detection using Auto-Sklearn

  • Mobile price prediction using Auto-Sklearn

  • Medical insurance cost prediction using TPOT

  • Red wine quality classification using TPOT

  • Image classification using Auto-Keras

  • Image classification using ANN(Auto-Keras)

  • Image classification using CNN(Auto-Keras)

  • Text classification using Auto-Keras

  • Object detection using Auto-Keras

  • Topic classification using Auto-Keras


Content

Auto Sklearn

Introduction to mobile price prediction

Download the project files

Data Preprocessing And Data Visualization

Building a model

Auto Sklearn Part2

Introduction to credit card fraud detection

Download the project code.

Data preprocessing and Data visulization

Model Building


Reviews

В
Валерий15 March 2021

This course is absolutely terrible. Approximately 1 out of 4 videos in each block are about creation of the google colab notebook. They last for 5 minutes each but they are the same :) Almost no information about example cases are provided, and results of the models are analyzed in a way like this "Look, we've build a model! Cool right?". I've scored this course by 1.5 out of 5 only because there are some tiny pieces of useful information.

B
Baseer19 December 2020

This is an excellent course giving great insights to theory, concepts and practical steps to Auto ML techniques. Topics are broken in to small capsules making learning easy and effective. Thanks a lot !


3708242

Udemy ID

12/16/2020

Course created date

12/17/2020

Course Indexed date
Angelcrc Seven
Course Submitted by