- Data Science
- Artificial Neural Network
- Artificial Intelligence (AI)
- Machine Learning
- Random Forest
- regression
- Statistical Hypothesis Testing
- medical data
- Python Programming
- PCA
- identifying specieis
- predictions in science
AI for Scientific Research 특화 과정
데이터 과학 부문의 경력을 시작하세요. Use artificial intelligence to discover and test hypothesis.
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배울 내용
How to use AI in scientific situations to discover trends and patterns within datasets
The complete machine learning process
Use artificial intelligence to predict sequences in datasets
Employ artificial intelligence techniques to test hypothesis in Python
귀하가 습득할 기술
이 전문 분야 정보
응용 학습 프로젝트
Each course in this specialization contains practice labs built on the Coursera lab platform. You will use the provided libraries and models to perform machine learning and AI instructions that help answer important questions in your dataset. The final course is a capstone project where you will compare genome sequences of COVID-19 mutations to identify potential areas a drug therapy can look to target. It begins with the basic setup and walks through the entire analysis process.
There are no specific background requirements; however, it is very helpful to understand scientific methods, mathematics and general computer logic.
There are no specific background requirements; however, it is very helpful to understand scientific methods, mathematics and general computer logic.
특화 과정 이용 방법
강좌 수강
Coursera 특화 과정은 한 가지 기술을 완벽하게 습득하는 데 도움이 되는 일련의 강좌입니다. 시작하려면 특화 과정에 직접 등록하거나 강좌를 둘러보고 원하는 강좌를 선택하세요. 특화 과정에 속하는 강좌에 등록하면 해당 특화 과정 전체에 자동으로 등록됩니다. 단 하나의 강좌만 수료할 수도 있으며, 학습을 일시 중지하거나 언제든 구독을 종료할 수 있습니다. 학습자 대시보드를 방문하여 강좌 등록 상태와 진도를 추적해 보세요.
실습 프로젝트
모든 특화 과정에는 실습 프로젝트가 포함되어 있습니다. 특화 과정을 완료하고 수료증을 받으려면 프로젝트를 성공적으로 마쳐야 합니다. 특화 과정에 별도의 실습 프로젝트 강좌가 포함되어 있는 경우, 다른 모든 강좌를 완료해야 프로젝트 강좌를 시작할 수 있습니다.
수료증 취득
모든 강좌를 마치고 실습 프로젝트를 완료하면 취업할 때나 전문가 네트워크에 진입할 때 제시할 수 있는 수료증을 취득할 수 있습니다.

이 전문 분야에는 4개의 강좌가 있습니다.
Introduction to Data Science and scikit-learn in Python
This course will teach you how to leverage the power of Python and artificial intelligence to create and test hypothesis. We'll start for the ground up, learning some basic Python for data science before diving into some of its richer applications to test our created hypothesis. We'll learn some of the most important libraries for exploratory data analysis (EDA) and machine learning such as Numpy, Pandas, and Sci-kit learn. After learning some of the theory (and math) behind linear regression, we'll go through and full pipeline of reading data, cleaning it, and applying a regression model to estimate the progression of diabetes. By the end of the course, you'll apply a classification model to predict the presence/absence of heart disease from a patient's health data.
Machine Learning Models in Science
This course is aimed at anyone interested in applying machine learning techniques to scientific problems. In this course, we'll learn about the complete machine learning pipeline, from reading in, cleaning, and transforming data to running basic and advanced machine learning algorithms. We'll start with data preprocessing techniques, such as PCA and LDA. Then, we'll dive into the fundamental AI algorithms: SVMs and K-means clustering. Along the way, we'll build our mathematical and programming toolbox to prepare ourselves to work with more complicated models. Finally, we'll explored advanced methods such as random forests and neural networks. Throughout the way, we'll be using medical and astronomical datasets. In the final project, we'll apply our skills to compare different machine learning models in Python.
Neural Networks and Random Forests
In this course, we will build on our knowledge of basic models and explore advanced AI techniques. We’ll start with a deep dive into neural networks, building our knowledge from the ground up by examining the structure and properties. Then we’ll code some simple neural network models and learn to avoid overfitting, regularization, and other hyper-parameter tricks. After a project predicting likelihood of heart disease given health characteristics, we’ll move to random forests. We’ll describe the differences between the two techniques and explore their differing origins in detail. Finally, we’ll complete a project predicting similarity between health patients using random forests.
Capstone Project: Advanced AI for Drug Discovery
In this capstone project course, we'll compare genome sequences of COVID-19 mutations to identify potential areas a drug therapy can look to target. The first step in drug discovery involves identifying target subsequences of theirs genome to target. We'll start by comparing the genomes of virus mutations to look for similarities. Then, we'll perform PCA to cut down our number of dimensions and identify the most common features. Next, we'll use K-means clustering in Python to find the optimal number of groups and trace the lineage of the virus. Finally, we'll predict similarity between the sequences and use this to pick a target subsequence. Throughout the course, each section will consist of a programming assignment coupled with a guide video and helpful hints. By the end, you'll be well on your way to discovering ways to combat disease with genome sequencing.
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LearnQuest
LearnQuest is the preferred training partner to the world’s leading companies, organizations, and government agencies. Our team boasts 20+ years of experience designing, developing and delivering a full suite industry-leading technology education classes and training solutions across the globe. Our trainers, equipped with expert industry experience and an unparalleled commitment to quality, facilitate classes that are offered in various delivery formats so our clients can obtain the training they need when and where they need it.
자주 묻는 질문
환불 규정은 어떻게 되나요?
하나의 강좌에만 등록할 수 있나요?
재정 지원을 받을 수 있나요?
해당 강좌를 무료로 수강할 수 있나요?
이 강좌는 100% 온라인으로 진행되나요? 직접 참석해야 하는 수업이 있나요?
전문 분야를 완료하는 데 얼마나 걸리나요?
What background knowledge is necessary?
Do I need to take the courses in a specific order?
전문 분야를 완료하면 대학 학점을 받을 수 있나요?
What will I be able to do upon completing the Specialization?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.