掌握关键的人工智能算法，让你的 Python 编程技能飞速发展
发表于 2022 年 11 月
MP4 | 视频：h264、1280×720 | 音频：AAC，44.1 KHz，2 Ch
类型：eLearning | 语言：英语 | 时长：32 节课（5 小时）| 大小：2.2 GB
您将学到什么 将
朴素贝叶斯应用于图像分类（计算机视觉）
将朴素贝叶斯应用于文本分类 (NLP)
将朴素贝叶斯应用于疾病预测、基因组学和财务分析
了解朴素贝叶斯概念和算法
从头开始实施多个朴素贝叶斯模型
要求
良好的Python 编程技能
使用 Numpy、Matplotlib 和 Pandas 的经验（我们将使用这些）
对于高级部分：了解概率
描述
在这门自学课程中，您将学习如何将朴素贝叶斯应用于各个领域的许多真实数据集，例如
计算机视觉
自然语言处理
财务分析
卫生保健
基因组学
你为什么要参加这门课程？朴素贝叶斯是机器学习、数据科学和人工智能中的基本算法之一。没有掌握它的练习者是不完整的。
本课程旨在适合所有级别的学生，无论您是初学者、中级还是高级。您将了解朴素贝叶斯如何工作的直觉以及如何有效地应用它，同时考虑朴素贝叶斯算法的独特特征。您将了解何时以及为何使用 Scikit-Learn 中包含的不同版本的朴素贝叶斯，包括 GaussianNB、BernoulliNB 和 MultinomialNB。
在课程的高级部分，您将了解朴素贝叶斯是如何真正在幕后工作的。您还将学习如何从头开始实现朴素贝叶斯的几种变体，包括高斯朴素贝叶斯、伯努利朴素贝叶斯和多项朴素贝叶斯。高级部分将需要概率知识，所以要做好准备！
感谢您的阅读，希望很快能见到您！
建议的先决条件
不错的 Python 编程技能
熟悉 Numpy 和 Matplotlib 等数据科学库
进阶部分需要概率知识
我应该按什么顺序上课？
查看讲座“机器学习和 AI 先决条件路线图”（可在我的任何课程的常见问题解答中找到，包括我的免费课程）
独特的功能
每行代码都详细解释 – 如果您不同意，请随时给我发电子邮件
平均不到 24 小时的问答时间
不怕大学水平的数学——获取其他课程遗漏的算法的重要细节
本课程面向
对数据科学和机器学习感兴趣的初学者 Python 开发人员
对机器学习基础知识感兴趣的学生和专业人士
Master a crucial artificial intelligence algorithm and skyrocket your Python programming skills
Published 11/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 32 lectures (5h) | Size: 2.2 GB
What you’ll learn
Apply Naive Bayes to image classification (Computer Vision)
Apply Naive Bayes to text classification (NLP)
Apply Naive Bayes to Disease Prediction, Genomics, and Financial Analysis
Understand Naive Bayes concepts and algorithm
Implement multiple Naive Bayes models from scratch
Requirements
Decent Python programming skills
Experience with Numpy, Matplotlib, and Pandas (we’ll be using these)
For advanced portions: know probability
Description
In this self-paced course, you will learn how to apply Naive Bayes to many real-world datasets in a wide variety of areas, such as
computer vision
natural language processing
financial analysis
healthcare
genomics
Why should you take this course? Naive Bayes is one of the fundamental algorithms in machine learning, data science, and artificial intelligence. No practitioner is complete without mastering it.
This course is designed to be appropriate for all levels of students, whether you are beginner, intermediate, or advanced. You’ll learn both the intuition for how Naive Bayes works and how to apply it effectively while accounting for the unique characteristics of the Naive Bayes algorithm. You’ll learn about when and why to use the different versions of Naive Bayes included in Scikit-Learn, including GaussianNB, BernoulliNB, and MultinomialNB.
In the advanced section of the course, you will learn about how Naive Bayes really works under the hood. You will also learn how to implement several variants of Naive Bayes from scratch, including Gaussian Naive Bayes, Bernoulli Naive Bayes, and Multinomial Naive Bayes. The advanced section will require knowledge of probability, so be prepared!
Thank you for reading and I hope to see you soon!
Suggested Prerequisites
Decent Python programming skill
Comfortable with data science libraries like Numpy and Matplotlib
For the advanced section, probability knowledge is required
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?
Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including my free course)
UNIQUE FEATURES
Every line of code explained in detail – email me any time if you disagree
Less than 24 hour response time on Q&A on average
Not afraid of university-level math – get important details about algorithms that other courses leave out
Who this course is for
Beginner Python developers curious about data science and machine learning
Students and professionals interested in machine learning fundamentals
原创文章，作者：steven，如若转载，请注明出处：https://rr3d.cn/38798.html
