University of Pennsylvania

AI and Machine Learning Essentials with Python 专项课程

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University of Pennsylvania

AI and Machine Learning Essentials with Python 专项课程

Explore AI and Machine Learning. Go further with your Python skills while exploring the transformative fields of artificial intelligence, machine learning and deep learning.

Victor Preciado
Hamed Hassani
Pratik Chaudhari

位教师:Victor Preciado

4,446 人已注册

包含在 Coursera Plus

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4 月 完成
在 8 小时 一周
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深入学习学科知识
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中级 等级

推荐体验

4 月 完成
在 8 小时 一周
灵活的计划
自行安排学习进度

您将学到什么

  • Understand the philosophical, scientific, and historical foundations of artificial intelligence and machine learning.

  • Apply Python programming concepts to implement AI algorithms, machine learning models, and deep learning architectures.

  • Use essential statistics concepts, like probability, linear algebra and statistical learning theory, to develop and analyze machine learning models.

  • Implement and compare machine learning algorithms, such as linear regression and logistic regression, for both regression and classification tasks.

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授课语言:英语(English)

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专业化 - 4门课程系列

Artificial Intelligence Essentials

Artificial Intelligence Essentials

第 1 门课程 18小时

您将学到什么

  • Understand the history and context of artificial intelligence through the lenses of philosophy and science fiction.

  • Explore different kinds of common search algorithms like A* Search, depth first search, breadth first search and more.

您将获得的技能

类别:Artificial Intelligence
类别:Algorithms
类别:Python Programming
类别:Agentic systems
类别:Computer Programming
类别:Theoretical Computer Science
类别:Programming Principles
Statistics for Data Science Essentials

Statistics for Data Science Essentials

第 2 门课程 19小时

您将学到什么

  • Comprehensively review probability and understand its role as a building block of data science.

  • Apply the central limit theorem, confidence intervals and the method of maximum likelihood to solving data science problems.

您将获得的技能

类别:Probability & Statistics
类别:Probability
类别:Statistics
类别:Statistical Inference
类别:Sampling (Statistics)
类别:Data Science
类别:Probability Distribution
类别:Statistical Analysis
类别:General Mathematics
类别:Descriptive Statistics
Machine Learning Essentials

Machine Learning Essentials

第 3 门课程 17小时

您将学到什么

  • Review probability basics and understand essential theoretical framework to analyze statistical learning problems.

  • Use linear regression and Python programming to solve machine learning problems.

您将获得的技能

类别:Regression Analysis
类别:Logistic Regression
类别:Statistical Machine Learning
类别:Classification Algorithms
类别:Statistical Analysis
类别:Probability & Statistics
类别:Python Programming
类别:Statistical Hypothesis Testing
类别:Model Evaluation
类别:Machine Learning
类别:Data Science
类别:Applied Machine Learning
类别:Supervised Learning
类别:Statistical Methods
类别:Dimensionality Reduction
类别:Machine Learning Methods
Deep Learning Essentials

Deep Learning Essentials

第 4 门课程 15小时

您将学到什么

  • Understand the history and context of the deep learning field, and explore what "intelligence" really means.

  • Explore deep learning models like the perceptron, neural networks and backpropagation, and study the techniques that drive them.

  • Code a project using Python where you will preprocess data and use your data to train a Support Vector Machine (SVM.)

您将获得的技能

类别:Deep Learning
类别:Artificial Neural Networks
类别:Machine Learning Algorithms
类别:Supervised Learning
类别:Machine Learning
类别:PyTorch (Machine Learning Library)
类别:Artificial Intelligence
类别:Data Preprocessing

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位教师

Victor Preciado
2 门课程 1,423 名学生

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