AI in Scientific Research Specialization
By learning how to analyze health data and sequence genomes using AI, this course equips students with the tools they need to contribute to medical research.
Description for AI in Scientific Research Specialization
- Python for Data Science: Introduces the Python programming language for data science applications, incorporating modules for dataset analysis and a classification model to forecast heart disease based on health data.
- The Machine Learning Pipeline: It comprises the complete ML process, including data preprocessing, implementation of fundamental and sophisticated algorithms, culminating in a final project that evaluates ML models using Python.
Advanced AI Techniques: Assesses complicated AI models beyond fundamental methods, employing a project to forecast patient similarities utilizing random forests.
Capstone Genome Project: Delivers a conclusive project on genome sequence analysis to pinpoint possible therapeutic targets for COVID-19 mutations.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 21
Offered by: On Coursera provided by LearnQuestDuration: 1 month at 10 hours a week (approximately)
Schedule: Project-based
Pricing for AI in Scientific Research Specialization
Use Cases for AI in Scientific Research Specialization
FAQs for AI in Scientific Research Specialization
Reviews for AI in Scientific Research Specialization
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for AI in Scientific Research Specialization
In-depth study of the applications of machine learning and computer vision, as well as practical experience in data analysis and Python programming.
A thorough grasp of artificial intelligence (AI) and machine learning, including its various forms, methods, and applications, is given in this course.
To enhance machine learning models, this course offers fundamental understanding of artificial intelligence, machine learning methods like classification, regression, and clustering.
Preparing students for a future in artificial intelligence security, this course offers AI hacking, vulnerability discovery, and attack mitigating techniques.
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.
An extensive study of the applications of AI in marketing, ranging from competitive analysis to content optimization and conversion enhancement.
A practical guide to the use of generative AI for the purpose of composing, refining, and planning, utilizing structured and context-driven inputs.
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
This program instructs instructors on the ethical and successful integration of AI, while promoting innovation and critical thinking among students.
Preparing students for a future in artificial intelligence security, this course offers AI hacking, vulnerability discovery, and attack mitigating techniques.
An extensive study of the applications of AI in marketing, ranging from competitive analysis to content optimization and conversion enhancement.
Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.
Learn the fundamental techniques of supervised and unsupervised learning and apply them to real-world problems to unlock the potential of machine learning.