Automated Machine Learning - Auto ML, TPOT, H2O, Auto Keras
Last updated 6/2024
Duration: 2h8m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 971 MB
Genre: eLearning | Language: English
Exploring Automated ML Techniques: TPOTs, AutoML, AutoKeras, H2O for Streamlining ML workflow and Model Performance
What you'll learn
Learn various Automated Machine Learning Techniques - TPOTs, AutoML, AutoKeras, H20
Compare Stacked Machine Learning Models with Automated Machine Learning Models for optimization problems
Simplify Deep Learning Models for Object Detection with Autokeras
Learn H2O automated machine learning Framework
Requirements
Basics of Python required
Basics of Sklearn required
Description
Join this comprehensive course as we delve into the Automated Machine Learning (AutoML) Techniques. Throughout the program, we'll explore a variety of powerful tools including TPOTs, AutoML, AutoKeras, and H2O.
You'll learn to compare and contrast Stacked Machine Learning Models with Automated counterparts, gaining valuable insights into their efficacy for solving optimization problems.
Additionally, we will work on 5 excercises which includes:
AutoML using Credit Card Fraud dataset:
In this exercise, you'll leverage AutoML techniques to automate the process of building and optimizing machine learning models to detect credit card fraud. AutoML algorithms will automatically explore various models, feature engineering techniques, and hyperparameter configurations to identify the most effective solution for detecting fraudulent transactions within credit card data
AutoKeras on MNIST data:
MNIST is a classic dataset commonly used for handwritten digit recognition. With AutoKeras, a powerful AutoML library specifically designed for deep learning tasks, you'll automate the process of building and tuning deep neural networks for accurately classifying handwritten digits in the MNIST dataset.
TPOT for Insurance Predictions:
TPOT (Tree-based Pipeline Optimization Tool) is an AutoML tool that automatically discovers and optimizes machine learning pipelines. In this exercise, you'll apply TPOT to the task of predicting insurance-related outcomes, such as insurance claims or customer behavior.
Churn Prediction using H2O:
Churn prediction involves forecasting whether customers are likely to stop using a service or product. With H2O, an open-source machine learning platform, you'll build predictive models to identify potential churners within a customer base.
Sales Prediction using H2O:
Sales prediction involves forecasting future sales based on historical data and other relevant factors. In this exercise, you'll utilize H2O to develop predictive models for sales forecasting.
Whether you're a seasoned data scientist looking to streamline your workflow or a newcomer eager to grasp the latest advancements in machine learning, this course offers a practical and insightful journey into the world of Automated Machine Learning.
Who this course is for:
Beginner programmer enthusiast to become Data Scientist
Beginner for Automated Machine Learning Fundamentals
What you'll learn
Learn various Automated Machine Learning Techniques - TPOTs, AutoML, AutoKeras, H20
Compare Stacked Machine Learning Models with Automated Machine Learning Models for optimization problems
Simplify Deep Learning Models for Object Detection with Autokeras
Learn H2O automated machine learning Framework
Requirements
Basics of Python required
Basics of Sklearn required
Description
Join this comprehensive course as we delve into the Automated Machine Learning (AutoML) Techniques. Throughout the program, we'll explore a variety of powerful tools including TPOTs, AutoML, AutoKeras, and H2O.
You'll learn to compare and contrast Stacked Machine Learning Models with Automated counterparts, gaining valuable insights into their efficacy for solving optimization problems.
Additionally, we will work on 5 excercises which includes:
AutoML using Credit Card Fraud dataset:
In this exercise, you'll leverage AutoML techniques to automate the process of building and optimizing machine learning models to detect credit card fraud. AutoML algorithms will automatically explore various models, feature engineering techniques, and hyperparameter configurations to identify the most effective solution for detecting fraudulent transactions within credit card data
AutoKeras on MNIST data:
MNIST is a classic dataset commonly used for handwritten digit recognition. With AutoKeras, a powerful AutoML library specifically designed for deep learning tasks, you'll automate the process of building and tuning deep neural networks for accurately classifying handwritten digits in the MNIST dataset.
TPOT for Insurance Predictions:
TPOT (Tree-based Pipeline Optimization Tool) is an AutoML tool that automatically discovers and optimizes machine learning pipelines. In this exercise, you'll apply TPOT to the task of predicting insurance-related outcomes, such as insurance claims or customer behavior.
Churn Prediction using H2O:
Churn prediction involves forecasting whether customers are likely to stop using a service or product. With H2O, an open-source machine learning platform, you'll build predictive models to identify potential churners within a customer base.
Sales Prediction using H2O:
Sales prediction involves forecasting future sales based on historical data and other relevant factors. In this exercise, you'll utilize H2O to develop predictive models for sales forecasting.
Whether you're a seasoned data scientist looking to streamline your workflow or a newcomer eager to grasp the latest advancements in machine learning, this course offers a practical and insightful journey into the world of Automated Machine Learning.
Who this course is for:
Beginner programmer enthusiast to become Data Scientist
Beginner for Automated Machine Learning Fundamentals
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