Automated Machine Learning – Auto ML, TPOT, H2O, Auto Keras
Automated Machine Learning – Auto ML, TPOT, H2O, Auto Keras, available at $54.99, with 26 lectures, and has 3 subscribers.
You will learn about 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 This course is ideal for individuals who are Beginner programmer enthusiast to become Data Scientist or Beginner for Automated Machine Learning Fundamentals It is particularly useful for Beginner programmer enthusiast to become Data Scientist or Beginner for Automated Machine Learning Fundamentals.
Enroll now: Automated Machine Learning – Auto ML, TPOT, H2O, Auto Keras
Summary
Title: Automated Machine Learning – Auto ML, TPOT, H2O, Auto Keras
Price: $54.99
Number of Lectures: 26
Number of Published Lectures: 26
Number of Curriculum Items: 26
Number of Published Curriculum Objects: 26
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- Beginner programmer enthusiast to become Data Scientist
- Beginner for Automated Machine Learning Fundamentals
Target Audiences
- Beginner programmer enthusiast to become Data Scientist
- Beginner for Automated Machine Learning Fundamentals
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:
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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
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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.
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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.
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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.
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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.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction to Auto ML
Chapter 2: Excercise 1 – AutoML on Credit Card Fraud
Lecture 1: Load Dataset
Lecture 2: Visualize the Dataset – Perform Distribution Plot on Fraud Data
Lecture 3: Scale Data using RobustScaler
Lecture 4: Remove Data Outliers
Lecture 5: Ensemble and AutoML Predictions
Chapter 3: Introduction to AutoKeras
Lecture 1: Introduction to AutoKeras
Chapter 4: Excercise 2 – AutoKeras on MNIST Dataset
Lecture 1: Implementing AutoKeras on MNIST Dataset
Chapter 5: AutoKeras using StructuredDataRegressor
Lecture 1: AutoKeras using StructuredDataRegressor Part 1
Lecture 2: AutoKeras using StructuredDataRegressor Part 2
Chapter 6: Introduction to TPOT
Lecture 1: TPOT Introduction
Lecture 2: TPOT Classifier
Chapter 7: Excercise 3 – TPOT for Insurance Predictions
Lecture 1: Insurance Predictions using TPOT
Lecture 2: Visualize Data
Lecture 3: Ensemble Model Predictions
Lecture 4: TPOT Regressor
Lecture 5: Stacked Model
Chapter 8: Introduction to H2O
Lecture 1: Introduction to H2O
Chapter 9: Excercise 4 – Churn Prediction using H2O
Lecture 1: Introduction to Churn Prediction using H2O
Lecture 2: Train the Dataset
Lecture 3: H2O Leaderboard and Model Performance
Lecture 4: Making Predictions
Chapter 10: Excercise 5 – Sales Prediction using H2O
Lecture 1: Introduction to Sales Prediction using H2O
Lecture 2: Preprocessing the Dataset
Lecture 3: Training and Predictions using Decision Trees
Lecture 4: Training and Making Predictions using H2O
Instructors
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Spaark Hub
Instructor at Udemy -
Gaurav Shandilya
Instructor at Udemy
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