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