Hyperparameter Optimization for Machine Learning

Hyperparameter Optimization for Machine Learning, available at $89.99, has an average rating of 4.52, with 95 lectures, based on 648 reviews, and has 8604 subscribers.

You will learn about Hyperparameter tunning and why it matters Cross-validation and nested cross-validation Hyperparameter tunning with Grid and Random search Bayesian Optimisation Tree-Structured Parzen Estimators, Population Based Training and SMAC Hyperparameter tunning tools, i.e., Hyperopt, Optuna, Scikit-optimize, Keras Turner and others This course is ideal for individuals who are Students who want to know more about hyperparameter optimization algorithms or Students who want to understand advanced techniques for hyperparameter optimization or Students who want to learn to use multiple open source libraries for hyperparameter tuning or Students interested in building better performing machine learning models or Students interested in participating in data science competitions or Students seeking to expand their breadth of knowledge on machine learning It is particularly useful for Students who want to know more about hyperparameter optimization algorithms or Students who want to understand advanced techniques for hyperparameter optimization or Students who want to learn to use multiple open source libraries for hyperparameter tuning or Students interested in building better performing machine learning models or Students interested in participating in data science competitions or Students seeking to expand their breadth of knowledge on machine learning.

Enroll now: Hyperparameter Optimization for Machine Learning

Summary

Title: Hyperparameter Optimization for Machine Learning

Price: $89.99

Average Rating: 4.52

Number of Lectures: 95

Number of Published Lectures: 95

Number of Curriculum Items: 95

Number of Published Curriculum Objects: 95

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Hyperparameter tunning and why it matters

Cross-validation and nested cross-validation

Hyperparameter tunning with Grid and Random search

Bayesian Optimisation

Tree-Structured Parzen Estimators, Population Based Training and SMAC

Hyperparameter tunning tools, i.e., Hyperopt, Optuna, Scikit-optimize, Keras Turner and others

Who Should Attend

Students who want to know more about hyperparameter optimization algorithms

Students who want to understand advanced techniques for hyperparameter optimization

Students who want to learn to use multiple open source libraries for hyperparameter tuning

Students interested in building better performing machine learning models

Students interested in participating in data science competitions

Students seeking to expand their breadth of knowledge on machine learning

Target Audiences

Students who want to know more about hyperparameter optimization algorithms

Students who want to understand advanced techniques for hyperparameter optimization

Students who want to learn to use multiple open source libraries for hyperparameter tuning

Students interested in building better performing machine learning models

Students interested in participating in data science competitions

Students seeking to expand their breadth of knowledge on machine learning

Welcome to Hyperparameter Optimization for Machine Learning. In this course, you will learn multiple techniques to select the best hyperparameters and improve the performance of your machine learning models.

If you are regularly training machine learning models as a hobby or for your organization and want to improve the performance of your models, if you are keen to jump up in the leader board of a data science competition, or you simply want to learn more about how to tune hyperparameters of machine learning models, this course will show you how.

We’ll take you step-by-step through engaging video tutorials and teach you everything you need to know about hyperparameter tuning. Throughout this comprehensive course, we cover almost every available approach to optimize hyperparameters, discussing their rationale, their advantages and shortcomings, the considerations to have when using the technique and their implementation in Python.

Specifically, you will learn:

What hyperparameters are and why tuning matters

The use of cross-validation and nested cross-validation for optimization

Grid search and Random search for hyperparameters

Bayesian Optimization

Tree-structured Parzen estimators

SMAC, Population Based Optimization and other SMBO algorithms

How to implement these techniques with available open source packages including Hyperopt, Optuna, Scikit-optimize, Keras Turner and others.

By the end of the course, you will be able to decide which approach you would like to follow and carry it out with available open-source libraries.

This comprehensive machine learning course includes over 50 lectures spanning about 8 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects.

So what are you waiting for? Enroll today, learn how to tune the hyperparameters of your models and build better machine learning models.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course curriculum

Lecture 3: Course aim and knowledge requirements

Lecture 4: Course material

Lecture 5: Jupyter notebooks

Lecture 6: Presentations

Lecture 7: Datasets

Lecture 8: Set up your computer – required packages

Lecture 9: Resources to learn machine learning skills

Chapter 2: Hyperparameter Tuning – Overview

Lecture 1: Parameters and Hyperparameters

Lecture 2: Hyperparameter Optimization

Chapter 3: Performance metrics

Lecture 1: Performance Metrics – Introduction

Lecture 2: Classification Metrics (Optional)

Lecture 3: Regression Metrics (Optional)

Lecture 4: Creating your own metrics

Lecture 5: Using Scikit-learn metrics

Chapter 4: Cross-Validation

Lecture 1: Cross-Validation

Lecture 2: Bias vs Variance (Optional)

Lecture 3: Cross-Validation schemes

Lecture 4: Estimating the model generalization error with CV – Demo

Lecture 5: Cross-Validation for Hyperparameter Tuning – Demo

Lecture 6: Special Cross-Validation schemes

Lecture 7: Group Cross-Validation – Demo

Chapter 5: Basic Search Algorithms

Lecture 1: Basic Search Algorithms – Introduction

Lecture 2: Manual Search

Lecture 3: Grid Search

Lecture 4: Grid Search – Demo

Lecture 5: Grid Search with different hyperparameter spaces

Lecture 6: Random Search

Lecture 7: Random Search with Scikit-learn

Lecture 8: Random Search with Scikit-Optimize

Lecture 9: Random Search with Hyperopt

Lecture 10: More examples

Chapter 6: Bayesian Optimization

Lecture 1: Sequential Search

Lecture 2: Bayesian Optimization

Lecture 3: Bayesian Inference – Introduction

Lecture 4: Joint and Conditional Probabilities

Lecture 5: Bayes Rule

Lecture 6: Sequential Model-Based Optimization

Lecture 7: Gaussian Distribution

Lecture 8: Multivariate Gaussian Distribution

Lecture 9: Gaussian Process

Lecture 10: Kernels

Lecture 11: Acquisition Functions

Lecture 12: Additional Reading Resources

Lecture 13: Scikit-Optimize – 1-Dimension

Lecture 14: Scikit-Optimize – Manual Search

Lecture 15: Scikit-Optimize – Automatic Search

Lecture 16: Scikit-Optimize – Alternative Kernel

Lecture 17: Scikit-Optimize – Neuronal Networks

Lecture 18: Scikit-Optimize – CNN – Search Analysis

Chapter 7: Other SMBO Algorithms

Lecture 1: Other SMBO Algorithms

Lecture 2: SMAC

Lecture 3: SMAC Demo

Lecture 4: Tree-structured Parzen Estimators – TPE

Lecture 5: TPE Procedure

Lecture 6: TPE hyperparameters

Lecture 7: TPE – why tree-structured?

Lecture 8: TPE with Hyperopt

Lecture 9: Discussion: Bayesian Optimization and Basic Search

Chapter 8: Scikit-Optimize

Lecture 1: Scikit-Optimize

Lecture 2: Section content

Lecture 3: Hyperparameter Distributions

Lecture 4: Defining the hyperparameter space

Lecture 5: Defining the objective function

Lecture 6: Random search

Lecture 7: Bayesian search with Gaussian processes

Lecture 8: Bayesian search with Random Forests

Lecture 9: Bayesian search with GBMs

Lecture 10: Parallelizing a Bayesian search

Lecture 11: Bayesian search with Scikit-learn wrapper

Lecture 12: Changing the kernel of a Gaussian Process

Lecture 13: Optimizing xgboost

Lecture 14: Optimizing Hyperparameters of a CNN

Lecture 15: Analyzing the CNN search

Chapter 9: Hyperopt

Lecture 1: Hyperopt

Lecture 2: Section content

Lecture 3: Search space configuration and distributions

Lecture 4: Sampling from nested spaces

Lecture 5: Search algorithms

Lecture 6: Evaluating the search

Lecture 7: Optimizing multiple ML models simultaneously

Lecture 8: Optimizing Hyperparameters of a CNN

Lecture 9: References

Chapter 10: Optuna

Lecture 1: Optuna

Lecture 2: Optuna main functions

Lecture 3: Section content

Lecture 4: Search algorithms

Lecture 5: Optimizing multiple ML models with simultaneously

Lecture 6: Optimizing hyperparameters of a CNN

Instructors

Soledad Galli
Data scientist | Instructor | Software developer

Train in Data Team
Data scientists | Instructors | Software engineers

Rating Distribution

1 stars: 3 votes

2 stars: 4 votes

3 stars: 28 votes

4 stars: 183 votes

5 stars: 430 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!