ML Classification
Learn through case studies, techniques, challenges, and objectives to master classification tasks, techniques, and metrics in Python for effective machine learning on various datasets.
Description for ML Classification
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by University of Washington
Duration: 21 hours (approximately)
Schedule: Flexible
Pricing for ML Classification
Use Cases for ML Classification
FAQs for ML Classification
Reviews for ML Classification
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for ML Classification
Develop and evaluate machine learning models using regression, trees, and unsupervised techniques to address various business challenges.
Investigate the field of artificial intelligence and machine learning. While investigating the transformative disciplines of artificial intelligence, machine learning, and deep learning, enhance your Python abilities.
Specialization in Machine Learning at BreakIntoAI. Master the fundamental AI concepts and cultivate practical machine learning skills in the beginner-friendly, three-course program by AI visionary Andrew Ng.
Develop applications that are intelligent. In four practical courses, acquire a comprehensive understanding of the fundamentals of machine learning.
Set up for a profession in machine learning. To become job-ready in less than three months, acquire the skills and practical experience that are in high demand.
Learn to build and train supervised machine learning models for binary classification and prediction tasks using Python with NumPy and scikit-learn libraries.
Real-World Applications of Machine Learning. Develop proficiency in the implementation of a machine learning undertaking.
This course teaches aspiring data scientists to train and compare classification models using supervised machine learning techniques, focusing on practical applications and best practices.
Become an expert in the field of artificial intelligence. Develop effective strategies for the application of Artificial Intelligence techniques to address business challenges.
Featured Tools
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
Acquire proficiency in machine learning and deep learning methodologies, such as TensorFlow, CNNs, RNNs, LSTMs, and NLP, to facilitate efficient data analysis.
In addition to addressing parameter estimation and structure learning, this course covers learning probabilistic graphical models from data and contains practical programming tasks for practical use.
Develop a comprehensive understanding of AI and machine learning, as well as practical experience in the development of models and the resolution of healthcare-related issues.
This program offers training and tools in cloud engineering to prepare for the Google Cloud Associate Cloud Engineer certification test, enhancing skills and confidence in cloud computing.