Machine Learning Techniques
The program builds upon the fundamental concepts of "Machine Learning Foundations," with an emphasis on practical and advanced models. It investigates the integration of a variety of features, the distillation of concealed features, and the combination of predictive features to improve the capabilities of machine learning.
Description for Machine Learning Techniques
Embedding a Large Number of Features: Acquire the skills necessary to represent data with multiple embedded features in order to improve predictive modeling.
Merging Predictive Features: Comprehend the process of combining a variety of predictive features to enhance the accuracy and efficacy of the model.
Uncovering and Utilizing latent Features: Acquire a deeper understanding of the process of identifying and leveraging latent features in datasets to enhance performance.
Constructing Realistic Models: Create machine learning models that are practical and that utilize sophisticated feature representation and manipulation techniques.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 21
Offered by: On Coursera provided by National Taiwan University
Duration: 3 weeks at 6 hours a week
Schedule: Flexible
Pricing for Machine Learning Techniques
Use Cases for Machine Learning Techniques
FAQs for Machine Learning Techniques
Reviews for Machine Learning Techniques
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Machine Learning Techniques
Use AI skills to advance your engineering career. Acquire practical knowledge regarding deep learning methodologies for computer vision.
Understand AI, its applications, concepts, ethical concerns, and receive expert career guidance.
The Deep Learning Specialization offers a comprehensive foundation in deep learning, practical skills in constructing neural networks, and prepares individuals to integrate machine learning into professional endeavors, advancing careers in AI.
The course's topics including the distinction between deep learning, machine learning, and artificial intelligence, the process of developing machine learning models, the difference between supervised and unsupervised learning, and the use of metrics for evaluating classification models.
Prepare for a vocation as a data scientist. Acquire hands-on experience and in-demand skills to become job-ready in as little as five months. No prior experience is necessary.
Begin your professional journey as an AI engineer. Master the art of generating business insights from large datasets by employing deep learning and machine learning models.
Learn to differentiate between deep learning, machine learning, and artificial intelligence (AI), select the appropriate AWS machine learning service for specific use cases, and understand the process of developing, training, and deploying machine learning models.
Learn to develop, assess, and enhance machine learning models using Python libraries, covering introductory deep learning, supervised, and unsupervised learning algorithms.
Learn to apply image processing, analysis methods, and supervised learning techniques using Python, Pillow, and OpenCV to address computer vision issues across various industries.
Learn to develop, train, and assess neural networks using TensorFlow to resolve classification issues by understanding the fundamental principles of neural networks.
Featured Tools
Acquire practical expertise in the integration of machine learning models into pipelines, optimizing performance, and efficiently managing versioning and artifacts.
Use AI skills to advance your engineering career. Acquire practical knowledge regarding deep learning methodologies for computer vision.
The course "Building a Generative AI Ready Organization" offers the necessary components for the successful adoption of Generative AI within an organization. This course concentrates on business leaders and other decision-makers who are currently or potentially involved in Generative AI initiatives.
Create a final presentation to evaluate peer projects, train neural networks for regression and classification, and develop Python-based recommender systems. Additionally, employ KNN, PCA, and collaborative filtering.
Explore the fundamentals, applications, ethical implications, and future trends of generative AI in human resources.