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Keep shoppingMastering ML:Hyperparameter Optimization & Feature Selection
216 students
Mastering ML:Hyperparameter Optimization & Feature Selection
Advanced ML Techniques: Hyperparameter Optimization, Feature Selection, Hands-on Python Practice Utilizing Key Libraries
Created byAlejandro Ruiz Olivares
Last updated 5/2024
English
What you'll 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
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Course content
10 sections • 37 lectures • 6h 26m total length
Requirements
- Basic Knowledge of Machine Learning: While not required, a general grasp of the ideas involved in machine learning is beneficial. Python Programming: While some familiarity with Python programming is advised, we'll nonetheless cover the most important code. Access to a Computer: For practical work and hands-on exercises, students should have a laptop or computer. Internet connection: In order to access online resources and course materials, a dependable internet connection is necessary.
- Basic Python programming experience: While not required, it is desirable. However, the course have a quick python programming recap
- Basic Math Skills
Description
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.
Who this course is for:
- 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