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所在平台: Udemy |
课程主页: https://www.udemy.com/course/the-complete-healthcare-artificial-intelligence-course-2021/
课程评论：没有评论
课程名称：2024年全面医疗保健人工智能课程 课程概述：如果您对机器学习、深度学习和人工智能领域感兴趣，这门课程非常适合您！本课程由一名软件工程师设计，旨在通过简单易懂的方式，分享我多年来获得的知识和经验，帮助您掌握复杂的理论、算法和编码库。我将逐步引导您进入机器学习、人工智能和深度学习的世界。每节课都会让您掌握新技能，提升对这一具有挑战性但又极具前景的数据科学子领域的理解。课程内容丰富，既有趣又深入，涵盖了大量的工具和技术，包括深度学习、Google Colab、Anaconda、Jupiter Notebook、人工智能在医疗中的应用、人工神经网络、激活函数、Keras、Pandas、Seaborn、特征缩放、Matplotlib等。 课程还包括数据预处理、神经网络训练、模型构建、结果分析及模型评估等多个环节，帮助您理解机器学习算法、处理缺失数据和异常值、数据标准化、时间特征、地理位置特征、数据可视化等重要概念。此外，课程还涉及自然语言处理（NLP）和多种机器学习算法，如逻辑回归、朴素贝叶斯、支持向量机（SVM）和随机森林等。 课程内容丰富且实践性强，基于实际案例进行练习。您不仅会学习理论知识，还会通过以下五个重大医疗项目和一个小项目进行实践，培养自己的模型构建能力：预测纽约市出租车费用、DNA分类项目、心脏病分类项目、冠状动脉疾病诊断项目、乳腺癌检测项目以及使用多层感知器预测糖尿病项目。 总之，这是一门旨在帮助您深入了解人工智能在医疗保健领域应用的课程，无论您是新手还是有一定基础的学习者，都能在其中受益匪浅。
Interested in the field of Machine Learning, Deep Learning and Artificial Intelligence? Then this course is for you!This course has been designed by a software engineer. I hope with my experience and knowledge I did gain throughout years, I can share my knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.I will walk you step-by-step into the Machine Learning, Artificial Intelligence and Deep Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.This course is fun and exciting, but at the same time, we dive deep into Machine Learning, Deep Learning and Artificial Intelligence. Throughout the brand new version of the course we cover tons of tools and technologies including:Deep Learning.Google ColabAnacondaJupiter NotebookArtificial Intelligent In Healthcare.Artificial Neural Network.Neuron.Activation Function.Keras.Pandas.Seaborn.Feature scaling.Matplotlib.Generating a DNA Sequence.Data Pre-processing.Sigmoid Function.Tanh Function.ReLU Function.Leaky Relu Function.Exponential Linear Unit Function.Swish function.Markov Models.K-Nearest Neighbors Algorithms (KNN).Support Vector Machines (SVM).Importing library and data.Deep feedforward networks.Analysing Data.Exploratory Analysis.Handling Missing Data And Anomalies in Python.Data standardization.Temporal Features.Geolocation Features.Data Scaling.Data Visualization.Visualizing Geolocation Data.Understanding Machine Learning Algorithm.Splitting Data into Training Set and Test Set. Training Neural Network.Model building.Analysing Results.Model compilation.A Comparison Of Categorical And Binary Problem.Make a Prediction.Testing Accuracy.Confusion Matrix.ROC Curve.One-hot Encoding.NLP (Natural Language Processing).NLTK (Natural Language Toolkit).Logistic Regression.Naive Bayes.Response Encoding.Linear Support Vector Machines.RF with Response Coding.Random Forest Classifier.Stacking model.Maximum voting Classifier.Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are five big projects on healthcare problems and one small project to practice. These projects are listed below:Predicting Taxi Fares in New York CityDNA Classification Project.Heart Disease Classification Project.Diagnosing Coronary Artery Disease Project.Breast Cancer Detection Project.Predicting Diabetes with Multilayer Perceptrons Project.Iris Flower.Medical Treatment Project.
