Practical Machine Learning
Learn to construct and implement prediction functions, understand overfitting and error rates, and grasp machine learning techniques like classification trees and regression.
Description for Practical Machine Learning
Features of Course
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by Johns Hopkins University
Duration: 8 hours (approximately)
Schedule: Flexible
Pricing for Practical Machine Learning
Use Cases for Practical Machine Learning
FAQs for Practical Machine Learning
Reviews for Practical Machine Learning
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Practical Machine Learning
The AI tool empowers non-programmers to construct and deploy AI, featuring data transformation, insights generation, identification of critical drivers, and prediction and forecasting functionalities to enhance business decision-making and planning processes.
Utilize generative AI to advance in the field of data science. Develop hands-on generative AI skills that are in high demand to accelerate your data science career in under one month.
Learn to leverage Generative AI for automation, software development, and optimizing outputs with Prompt Engineering.
Learn machine learning with Google Cloud. End-to-end machine learning experimentation in the real world
Utilize Generative AI to optimize marketing creativity. Explore the potential of Generative AI to revolutionize and influence your marketing organization.
This course covers the development, impact, and future of Generative AI through lectures, critical AI technologies, and interactive assessments.
Learn the significance, use cases, history, and pros and cons of generative AI in a business context, with a focus on its relationship to machine learning and services at Amazon.
Acquire practical skills to build a generative AI application by constructing a retrieval augmented generation (RAG) system using data, Qdrant, and LLMs.
The "Introduction to Vertex AI" course provides a four-hour, practical, and fundamental overview of Vertex AI, ideal for professionals and enthusiasts aiming to leverage AI effectively.
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.
Featured Tools
This course teaches aspiring data scientists to train and compare classification models using supervised machine learning techniques, focusing on practical applications and best practices.
Explore the multidisciplinary field of digital health, covering technologies like mobile apps, wearables, AI, and big data, emphasizing their role in public health and healthcare systems, and prepare learners to design, implement, and evaluate digital health interventions.
Outlines methods to determine main products, develop streaming pipelines, explore alternatives, and define essential steps for machine learning workflows on Google Cloud.
Master inventive thinking techniques and their application in routine problem-solving and addressing global challenges by selecting and implementing the appropriate approach for each situation.
Learn to implement and apply unsupervised learning techniques, focusing on clustering and dimension reduction algorithms, in a business environment.