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:

1. 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

2. 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.

3. 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.

4. 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.

5. 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

Spaark Hub
Instructor at Udemy

Gaurav Shandilya
Instructor at Udemy

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