Data in Machine Learning
In brief, this course instructs students on the effective management of data biases, the prevention of overfitting, and the enhancement of model accuracy through the implementation of appropriate testing methods and feature engineering.
Description for Data in Machine Learning
Essential Components of Data in Model Stages: Recognize the significance of data throughout various phases of model building, encompassing learning, training, and operation.
Prejudices and Data Origins: Acquire the ability to recognize biases in data and the sources that could affect the model's precision and equity.
Enhancing Model Generalization: Apply techniques to improve the generalization of your model, hence enhancing its performance on unfamiliar data.
Overfitting, Mitigation Strategies, and Evaluation Metrics: Comprehend the ramifications of overfitting and implement suitable mitigation solutions, in conjunction with efficient testing and validation techniques.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by Alberta Machine Intelligence Institute
Duration: 3 weeks at 3 hours a week
Schedule: Flexible
Pricing for Data in Machine Learning
Use Cases for Data in Machine Learning
FAQs for Data in Machine Learning
Reviews for Data in Machine Learning
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Data in Machine Learning
Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.
Understand foundational knowledge of AI and RegTech, their societal implications, and the discourse around their future integration and obstacles.
Gain extensive knowledge in AI technologies relevant to digital marketing, involving precise data analysis, content creation, and tools for optimizing social media and consumer segmentation.
Develop expertise in the exposure and deployment of large language models via application programming interfaces (APIs), configure server environments, and incorporate natural language processing (NLP) functionalities into applications.
Learn proficiency in the construction, deployment, and safeguarding of large language models at scale, utilizing Rust, Amazon Web Services (AWS), and established DevOps best practices.
This course is dedicated to the setting up of GPU-based environments, the deployment of local large language models (LLMs), and their integration into Python applications utilizing open-source tools.
In this course, students gain the skills necessary to use Python for data science, machine learning, and foundational applications of artificial intelligence.
Gain an extensive understanding of TinyML applications, fundamental principles, and the ethical development of artificial intelligence.
Learn the skills necessary to operate, optimize, and implement large language models through practical experience with state-of-the-art LLM architectures and open-source resources.
Acquire practical expertise in the integration of machine learning models into pipelines, optimizing performance, and efficiently managing versioning and artifacts.
Featured Tools
Acquire practical expertise in the integration of machine learning models into pipelines, optimizing performance, and efficiently managing versioning and artifacts.
Develop your skills in image processing, augmented reality, and object recognition to prepare yourself to create cutting-edge AI-powered visual apps.
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.
An insightful introduction of the foundational models, generative AI concepts, and their applications on a variety of platforms.
Explore the topic of AI-powered personalization by acquiring the skills necessary to utilize LangChain and ChatGPT.
Compare & Review AI Tool Directory AI Course Marketplace
