|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/artificial/
课程评论：没有评论
课程名称：人工智能训练营 - 44个项目，常春藤联盟专业 课程概述： 本课程由Gopal主讲，他在人工智能领域有深厚的背景，曾利用AI进行脑肿瘤分类，并在PubMed上发表了11篇相关论文。他曾在康奈尔大学、阿默斯特学院和加州大学旧金山分校任教，并在这些高校和国家卫生研究院工作。 人工智能与数据科学正在迅速崛起，课程旨在帮助零基础的学员掌握AI、数据分析和机器人技术等技能。Python因其简单性和灵活性，成为智能和机器学习编程的主导语言，课程将利用Python及其开源库（如TensorFlow）教授多种应用技术。 学习内容包括多个先进的人工智能项目与技术，适用于工程、生物研究、化学研究、金融、商业、社会分析、市场营销等多个行业。课程通过实践项目帮助学生提高数据分析能力，优化工作和财务效率。 课程包含如下项目： 1. 机器学习 2. 训练算法 3. SciKit 4. 数据预处理 5. 维度降低 6. 超参数优化 7. 集成学习 8. 情感分析 9. 回归分析 10. 聚类分析 11. 人工神经网络 12. TensorFlow及其工作坊 13. 卷积神经网络 14. 循环神经网络 此外，课程还涵盖了传统统计与机器学习，包括描述性统计、经典推断、贝叶斯分析等。 课程中提供执行代码，学生可以选择在Jupyter上输入代码，或直接执行以节省时间，也可以借助讲师的指导快速上手。Gopal还会分享他用本课程所学AI进行股票交易的经验，鼓励学员运用AI进行多领域探索。 总之，这是一门适合各类背景，尤其是无AI与数学基础的学员的课程，是您进入人工智能领域的理想起点。
My name is Gopal. I used AI to classify brain tumors. I have 11 publications on pubmed talking about that. I went to Cornell University and taught at Cornell, Amherst and UCSF. I worked at UCSF and NIH.AI and Data Science are taking over the world! Well sort of, and not exactly yet. This is the perfect time to hone you skills in AI, data analysis, and robotics, Artificial Intelligence has taken the world by storm as a major field of research and development. Python has surfaced as the dominant language in intelligence and machine learning programming because of its simplicity and flexibility, in addition to its great support for open source libraries and TensorFlow.This video course is built for those with a NO understanding of artificial intelligence or Calculus and linear Algebra. We will introduce you to advanced artificial intelligence projects and techniques that are valuable for engineering, biological research, chemical research, financial, business, social, analytic, marketing (KPI), and so many more industries. Knowing how to analyze data will optimize your time and your money. There is no field where having an understanding of AI will be a disadvantage. AI really is the future. We have many projects, such natural language processing , handwriting recognition, interpolation, compression, bayesian analysis, hyperplanes (and other linear algebra concepts). ALL THE CODE IS INCLUDED AND EASY TO EXECUTE. You can type along or just execute code in Jupyter if you are pressed for time and would like to have the satisfaction of having the course hold your hand. I use the AI I created in this course to trade stock. You can use AI to do whatever you want. These are the projects which we cover. For Data Science / Machine Learning / Artificial Intelligence1. Machine Learning2. Training Algorithm 3. SciKit 4. Data Preprocessing 5. Dimesionality Reduction 6. Hyperparemeter Optimization 7. Ensemble Learning 8. Sentiment Analysis 9. Regression Analysis10.Cluster Analysis11. Artificial Neural Networks 12. TensorFlow 13. TensorFlow Workshop 14. Convolutional Neural Networks 15. Recurrent Neural Networks Traditional statistics and Machine Learning1. Descriptive Statistics2.Classical Inference Proportions 3. Classical InferenceMeans4. Bayesian Analysis5. Bayesian Inference Proportions6. Bayesian Inference Means7. Correlations11. KNN12. Decision Tree 13. Random Forests 14. OLS 15. Evaluating Linear Model 16. Ridge Regression17. LASSO Regression18. Interpolation 19. Perceptron Basic20. Training Neural Network 21. Regression Neural Network 22. Clustering23. Evaluating Cluster Model24. kMeans25. Hierarchal 26. Spectral 27. PCA 28. SVD 29. Low Dimensional
