Udemy

Platform

English

Language

Data Science

Category

Practical Automated Machine Learning (Auto ML) : 10 Projects

Learn about Automated Machine Learning (AutoML) with python using google colab. Data Science, Artificial intelligence.

4.10 (10 reviews)

Practical Automated Machine Learning (Auto ML) : 10 Projects

Students

3.5 hours

Content

Aug 2021

Last Update
Regular Price


What you will learn

Build machine learning models

Data Preprocessing and data visualization

Learn to build model with AutoSklearn

Learn to build model with Auto Keras

Learn to build model with TPOT

Learn about Auto ML


Description

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.

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.

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

Introduction

Introduction

Case Study

Download the code

Importing data

Exploratory data analysis

Model building


Reviews

R
Rolando3 March 2021

It has been a real pleasure experiencing this long journey through data scince. It's a very well-structured and complete course, with a perfectly balanced amount of theory and exercises. Thank you so much for this great work!


3748338

Udemy ID

1/4/2021

Course created date

1/22/2021

Course Indexed date
Bot
Course Submitted by