- 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専門講座
データサイエンスでのキャリアをスタートさせる. Use artificial intelligence to discover and test hypothesis.
提供:


学習内容
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
習得するスキル
この専門講座について
応用学習プロジェクト
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.
専門講座の仕組み
コースを受講しましょう。
Courseraの専門講座は、一連のコース群であり、技術を身に付ける手助けとなります。開始するには、専門講座に直接登録するか、コースを確認して受講したいコースを選択してください。専門講座の一部であるコースにサブスクライブすると、自動的にすべての専門講座にサブスクライブされます。1つのコースを修了するだけでも結構です。いつでも、学習を一時停止したり、サブスクリプションを終了することができます。コースの登録状況や進捗を追跡するには、受講生のダッシュボードにアクセスしてください。
実践型プロジェクト
すべての専門講座には、実践型プロジェクトが含まれています。専門講座を完了して修了証を獲得するには、成功裏にプロジェクトを終了させる必要があります。専門講座に実践型プロジェクトに関する別のコースが含まれている場合、専門講座を開始するには、それら他のコースをそれぞれ終了させる必要があります。
修了証を取得
すべてのコースを終了し、実践型プロジェクトを完了すると、修了証を獲得します。この修了証は、今後採用企業やあなたの職業ネットワークと共有できます。

この専門講座には4コースあります。
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.
提供:

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.
よくある質問
返金ポリシーについて教えてください。
1つのコースだけに登録することは可能ですか?
学資援助はありますか?
無料でコースを受講できますか?
このコースは100%オンラインで提供されますか?実際に出席する必要のあるクラスはありますか?
専門講座を修了するのにどのくらいの期間かかりますか?
What background knowledge is necessary?
Do I need to take the courses in a specific order?
専門講座を修了することで大学の単位は付与されますか?
What will I be able to do upon completing the Specialization?
さらに質問がある場合は、受講者ヘルプセンターにアクセスしてください。