Mastering ML-Hyperparameter Optimization Feature Selection

Mastering ML:Hyperparameter Optimization & Feature Selection, available at $54.99, has an average rating of 4.86, with 37 lectures, 6 quizzes, based on 7 reviews, and has 141 subscribers.

You will learn about Master Hyperparameter Tuning: Enhance machine learning outcomes by optimizing model performance with hyperparameter fine-tuning Proficiency in Feature Selection: Choose relevant data attributes to build accurate and efficient machine learning models. Optimal Methodologies and Issue Resolution: Discover the best approaches for model optimization and address typical issues in ML projects. Advanced Application for finances: Real time Stock Market prediction with optimized ML models Use scikit-learn, scikit-optimize, Keras, Optuna, and TensorFlow for advanced machine learning techniques Advanced Application in Image recognition with optimized CNN Optimization Beyond ML: Neural Networks Optimization Learn both Cloud-Based and Desktop ML Optimization Python ML libraries: Scikit learn, Scikit optimize Python Deep Learning libraries: Keras, Tensorflow, Optuna Additional content: Optimization of Non-Supervised algorithms This course is ideal for individuals who are Professionals and students interested in the intricacies of machine learning, eager to delve deep into model optimization techniques. or Business who want to improve Machine Learning models or Advanced Professionals in the fields of Machine Learning and Artificial Intelligence It is particularly useful for Professionals and students interested in the intricacies of machine learning, eager to delve deep into model optimization techniques. or Business who want to improve Machine Learning models or Advanced Professionals in the fields of Machine Learning and Artificial Intelligence.

Enroll now: Mastering ML:Hyperparameter Optimization & Feature Selection

Summary

Title: Mastering ML:Hyperparameter Optimization & Feature Selection

Price: $54.99

Average Rating: 4.86

Number of Lectures: 37

Number of Quizzes: 6

Number of Published Lectures: 37

Number of Published Quizzes: 6

Number of Curriculum Items: 47

Number of Published Curriculum Objects: 47

Number of Practice Tests: 2

Number of Published Practice Tests: 2

Original Price: MX$579

Quality Status: approved

Status: Live

What You Will Learn

Master Hyperparameter Tuning: Enhance machine learning outcomes by optimizing model performance with hyperparameter fine-tuning

Proficiency in Feature Selection: Choose relevant data attributes to build accurate and efficient machine learning models.

Optimal Methodologies and Issue Resolution: Discover the best approaches for model optimization and address typical issues in ML projects.

Advanced Application for finances: Real time Stock Market prediction with optimized ML models

Use scikit-learn, scikit-optimize, Keras, Optuna, and TensorFlow for advanced machine learning techniques

Advanced Application in Image recognition with optimized CNN

Optimization Beyond ML: Neural Networks Optimization

Learn both Cloud-Based and Desktop ML Optimization

Python ML libraries: Scikit learn, Scikit optimize

Python Deep Learning libraries: Keras, Tensorflow, Optuna

Additional content: Optimization of Non-Supervised algorithms

Who Should Attend

Professionals and students interested in the intricacies of machine learning, eager to delve deep into model optimization techniques.

Business who want to improve Machine Learning models

Advanced Professionals in the fields of Machine Learning and Artificial Intelligence

Target Audiences

Professionals and students interested in the intricacies of machine learning, eager to delve deep into model optimization techniques.

Business who want to improve Machine Learning models

Advanced Professionals in the fields of Machine Learning and Artificial Intelligence

The in-depth course “Mastering ML: Hyperparameter Tuning & Feature Selection” is designed to take your machine learning skills to new heights. It is immersive and comprehensive. Explore the complex worlds of feature selection and hyperparameter optimization, two essential methods that are the key to achieving the best possible model performance and effectiveness. You’ll gain important skills in fine-tuning models and detecting the most salient features by unraveling the complexities of cutting-edge algorithms and approaches through a combination of theoretical insights, practical demonstrations, and hands-on activities.

With the help of practical examples and industry best practices, this enlightening journey is enhanced and gives you a strong foundation for confidently and accurately navigating large data landscapes. By the end of the course, you will have acquired the abilities and know-how required to create machine learning systems that are extremely precise, effective, and produce significant results. Boost your machine learning skills and take on an immersive learning journey that will push limits and ignite your potential for innovation and success in the ever-evolving field of machine learning.

This course covers fundamentals of machine learning through practical application with libraries such as scikit-learn, scikit-optimize, Keras, Optuna, and TensorFlow. You’ll discover how to effectively construct, adjust, and optimize models, ranging from simple models to sophisticated neural nets. Regardless of experience level, this course equips you with useful techniques to advance your machine learning knowledge and foster creativity in your work and projects.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction and Course Overview

Lecture 2: Machine Learning Fundamentals: Quick Recap I

Lecture 3: Machine Learning Fundamentals: Quick Recap II

Chapter 2: Hyperparameter Optimization

Lecture 1: Hyperparameter Optimization Fundamentals

Lecture 2: Grid, Random and Manual Search

Lecture 3: Principal Python Libraries

Lecture 4: Introduction to Practice

Lecture 5: Data management with Python: Very Quick Recap

Lecture 6: Practice I: Grid, Random and Manual Search

Lecture 7: Bayesian Optimization Theory

Lecture 8: Bayesian Optimization I: Good Practices

Lecture 9: Bayesian Optimization II: Hands on Python for ML, SVM and XGB

Lecture 10: Bayesian Optimization III: Hands on Pyton for Neural Networks

Lecture 11: Hyperparameter Optimization in Desktop with Visual Studio Code

Chapter 3: In-Depth Feature Selection

Lecture 1: Feature Selection Fundamentals

Lecture 2: Filtering: Pearson correlation

Lecture 3: Filtering: Kendall rank

Lecture 4: Filtering: Chi – squared

Lecture 5: Filtering: Principal Component Analysis (PCA)

Lecture 6: Wrapping: RFE, RFECV and Meta Selector

Lecture 7: Embedded: Random Forest

Lecture 8: Practical Feature Selection I: Filtering

Lecture 9: Practical Feature Selection II: Wrapping

Lecture 10: Practical Feature Selection II: Wrapping (Meta Selector)

Lecture 11: Practical Feature Selection III: Embedded

Lecture 12: Feature Selection in Visual Studio Code

Chapter 4: Evaluation Metrics

Lecture 1: Fundamentals

Lecture 2: Classification Models Evaluation

Lecture 3: Regression Models Evaluation

Lecture 4: Clustering Methods Evaluation

Chapter 5: Advanced Applications for finances: Stock Market Prediction

Lecture 1: Bayesian Optimization of Gradient Boosting and Xtreme Gradient Boosting

Chapter 6: Advanced Applications: Artificial Vision

Lecture 1: Bayesian Optimization of a CNN for Image Classification

Chapter 7: Optimization with Python Optuna library

Lecture 1: ML Optimization

Lecture 2: Convolutional Neural Network Optimization

Chapter 8: Additional content

Lecture 1: Optimization of non supervised algorithms

Chapter 9: Books and Resources

Lecture 1: Books to Master Machine Learning

Chapter 10: Conclusion and Next Steps

Lecture 1: Conclusions and Next Steps

Instructors

Alejandro Ruiz Olivares
Instructor at Udemy / PhD / Machine Learning Engineer

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4 stars: 2 votes

5 stars: 5 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!