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.
提供方
AI for Scientific Research 專項課程LearnQuest課程信息
1,719 次近期查看
可靈活調整截止日期
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可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 3 門課程（共 4 門）
中級
It is reccomended that you complete the first two courses in the specialization before starting this one.
完成時間大約為10 小時
英語（English）
字幕：英語（English）
您將獲得的技能
- Artificial Neural Network
- identifying specieis
- Random Forest
- predictions in science
- machine learniing
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 3 門課程（共 4 門）
中級
It is reccomended that you complete the first two courses in the specialization before starting this one.
完成時間大約為10 小時
英語（English）
字幕：英語（English）
提供方
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.
授課大綱 - 您將從這門課程中學到什麼
完成時間為 2 小時
Introduction to Neural Networks
In this module, we'll go through neural networks and how to use them in Python. We'll start by describing what a neural network is and how to construct one by combining a sequence of linear models. Then, we'll talk about converge of neural networks in the hopes of minimizing a loss function. Finally, we'll learn how to code a neural network in Python.
完成時間為 2 小時
5 個視頻 （總計 15 分鐘）, 3 個閱讀材料, 2 個測驗
完成時間為 3 小時
Deep Dive into Neural Networks
In this module, we'll take a more detailed look into neural network and the considerations we should be having when using them. We'll start by adding layers to our 2-layer network, exploring the different options and their effects. Then, we'll explore some more advanced Python libraries for neural networks in TensorFlow and Keras. Finally, we'll discuss the implications to science and how to apply the models in the space.
完成時間為 3 小時
3 個視頻 （總計 8 分鐘）, 5 個閱讀材料, 1 個測驗
完成時間為 3 小時
Exploring Random Forests
In this module, we'll build up our knowledge of random forests and their uses in science. We'll start by exploring decision trees and how they operate as models in isolation. Next, we'll look at the impact of combining decision trees to create random forests. From here, we'll talk about the similarities and differences between regression and classification with random forests before concluding with a final project predicting species from lineage.
完成時間為 3 小時
2 個視頻 （總計 4 分鐘）, 2 個閱讀材料, 1 個測驗
完成時間為 3 小時
Final Project: Comparing Models to Predict Sepal Width
In this final project, we'll be comparing a suite of models to find the one that best predicts sepal width.
完成時間為 3 小時
關於 AI for Scientific Research 專項課程
In the AI for Scientific Research specialization, we'll learn how to use AI in scientific situations to discover trends and patterns within datasets. Course 1 teaches a little bit about the Python language as it relates to data science. We'll share some existing libraries to help analyze your datasets. By the end of the course, you'll apply a classification model to predict the presence or absence of heart disease from a patient's health data. Course 2 covers the complete machine learning pipeline, from reading in, cleaning, and transforming data to running basic and advanced machine learning algorithms.In the final project, we'll apply our skills to compare different machine learning models in Python. In Course 3, we will build on our knowledge of basic models and explore more advanced AI techniques. We’ll describe the differences between the two techniques and explore how they differ. Then, we’ll complete a project predicting similarity between health patients using random forests. In Course 4, a capstone project course, we'll compare genome sequences of COVID-19 mutations to identify potential areas a drug therapy can look to target. By the end, you'll be well on your way to discovering ways to combat disease with genome sequencing.
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