Machine Learning Foundations: A Case Study Approach
This course provides practical experience with machine learning through case studies, concentrating on applying approaches across domains and laying the groundwork for deeper understanding of models and algorithms.
Description for Machine Learning Foundations: A Case Study Approach
Practical Case Analyses: Acquire practical experience through a series of case studies, including forecasting real estate values and suggesting items, to use machine learning methodologies across many sectors.
Machine Learning as a Black Box: Regard machine learning as a black box to concentrate on comprehending relevant problems, aligning them with suitable machine learning techniques, and assessing the quality of outcomes.
Overview of Machine Learning Tasks: Acquire knowledge on aligning certain machine learning tasks, such as sentiment analysis and document retrieval, with appropriate tools and methodologies.
Foundation for Future Learning: Establish a basis for subsequent courses, wherein participants will explore machine learning models, algorithms, and the elements of the machine learning pipeline in greater depth.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by Google Cloud
Duration: 3 weeks at 6 hours a week
Schedule: Flexible
Pricing for Machine Learning Foundations: A Case Study Approach
Use Cases for Machine Learning Foundations: A Case Study Approach
FAQs for Machine Learning Foundations: A Case Study Approach
Reviews for Machine Learning Foundations: A Case Study Approach
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Machine Learning Foundations: A Case Study Approach
Learn to apply advanced machine learning and deep learning models to real-world challenges by immersing yourself in the cutting-edge world of AI-powered finance and insurance.
An insightful introduction of the foundational models, generative AI concepts, and their applications on a variety of platforms.
In this course, students gain the skills necessary to use Python for data science, machine learning, and foundational applications of artificial intelligence.
Study the ethical consequences of AI development and implementation, emphasizing generative AI, AI governance, and pragmatic ethical decision-making in practical contexts.
Acquire practical expertise in the integration of machine learning models into pipelines, optimizing performance, and efficiently managing versioning and artifacts.
Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
A thorough grasp of artificial intelligence (AI) and machine learning, including its various forms, methods, and applications, is given in this course.
From fundamental concepts to advanced methods such as deep learning and ensemble techniques, this program provides a comprehensive examination of machine learning techniques.
A program that emphasizes the practical implementation of data science and machine learning to overcome obstacles through the use of Python.
Featured Tools
To enhance machine learning models, this course offers fundamental understanding of artificial intelligence, machine learning methods like classification, regression, and clustering.
Acquire an extensive understanding of reinforcement learning, deep neural networks, clustering, and dimensionality reduction to effectively address real-world machine learning challenges.
Discover AI terminology, ethical norms, and protocols for responsibly utilizing and citing Generative AI.
To address OpenAI Gym challenges and real-world problems, this course offers pragmatic artificial intelligence methods like Genetic Algorithms, Q-Learning, and neural network implementation.
Preparing students for a future in artificial intelligence security, this course offers AI hacking, vulnerability discovery, and attack mitigating techniques.
Compare & Review AI Tool Directory AI Course Marketplace
