Bioinformatics Specialization
Genome sequencing, disease gene discovery, computational Tree of Life construction, bioinformatics' impact on current biology, computational biology software, and an Honors Track for software programming and algorithm implementation are covered in the course.
Description for Bioinformatics Specialization
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by University of California San Diego
Duration: 3 months at 10 hours a week
Schedule: Flexible
Pricing for Bioinformatics Specialization
Use Cases for Bioinformatics Specialization
FAQs for Bioinformatics Specialization
Reviews for Bioinformatics Specialization
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Bioinformatics Specialization
Learn to leverage Generative AI for automation, software development, and optimizing outputs with Prompt Engineering.
Welcome to the 'Gen AI for Code Generation for Python' course, where you will begin a journey to hone and expand your abilities in the field of code generation using Generative AI.
Learn to use the latest LLM APIs, the LangChain Expression Language (LCEL), and develop a conversational agent.
Master coding basics and create a Hangman game using generative AI tools like Google Bard in a beginner-friendly, 1.5-hour guided project.
Learn about AI principles and platforms like IBM Watson and Hugging Face, integrate RAG technology for chatbot intelligence, create web apps using Python libraries, and develop interfaces with generative AI models and Python frameworks.
Acquire practical skills to build a generative AI application by constructing a retrieval augmented generation (RAG) system using data, Qdrant, and LLMs.
Gain expertise in deploying and managing LLMs on Azure, optimizing interactions with Semantic Kernel, and applying Retrieval Augmented Generation (RAG) techniques.
Gain expertise in deploying and managing LLMs on Azure, optimizing interactions with Semantic Kernel, and applying Retrieval Augmented Generation (RAG) techniques.
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.
Featured Tools
Master the AI and machine learning toolkit. Mathematics for Machine Learning and Data Science is a Specialization that is accessible to beginners. In this program, you will acquire the basic mathematics tools of machine learning, including calculus, linear algebra, statistics, and probability.
This course provides learners with the necessary skills to utilize AI tools for innovation, automation, and decision-making, with an emphasis on the principles and applications of AI.
Develop and evaluate a neural network that can identify handwritten numerals, implement One Hot Encoding for classification, and evaluate the efficacy of the model through practical exercises.
Explore the use of generative AI for creating and optimizing code, employing tools like IBM Watsonx Code Assistant and GitHub CoPilot, while addressing associated ethical considerations and challenges.
Comprehend generative AI basics, practical applications, ethical implications, and potential impacts on student learning in education.