用Python和项目进行机器学习(初学者) Machine Learning A-Z with Python with Project (Beginner)

初学者用Python完成机器学习课程

你会学到:
Python上的主机器学习
进行有力的分析
做出准确的预测
制作健壮的机器学习模型
将机器学习用于个人目的
建立一支强大的机器学习模型大军，并知道如何将它们结合起来解决任何问题
使用K-均值聚类、支持向量机(SVM)、KNN、决策树、朴素贝叶斯和主成分分析对数据进行分类
清理您的输入数据以移除异常值

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2 Ch
语言：英语+中英文字幕（云桥CG资源站 机译） |时长:33节课(13h 11m) |大小解压后:5.29 GB

要求
不需要以前的经验，你会知道需要什么。(基本的python知识肯定会增加你快速学习的机会)

描述
机器学习和人工智能(AI)无处不在；如果你想知道像谷歌、亚马逊，甚至Udemy这样的公司是如何从海量数据集中提取意义和见解的，那么这门数据科学课程将为你提供所需的基础知识。数据科学家是收入最高的工作之一，根据Glassdoor和the的数据，他们的平均工资为12万美元。那只是平均水平！这不仅仅是钱的问题，这也是一项有趣的工作！


如果你有一些编程或脚本经验，本课程将教你技术行业中真实数据科学家和机器学习从业者使用的技术，并为你进入这一热门职业道路做准备。

每个概念都用简单的英语介绍，避免混淆数学符号和行话。然后使用您可以实验和构建的Python代码以及您可以保存以供将来参考的注释来演示它。在这门课程中，你不会发现这些算法的学术的、深入的数学覆盖——重点是对它们的实际理解和应用。最后，你会有一个最终项目来应用你所学的知识！

我们的学习回顾:非常好的课程。精确而有条理的陈述。完整的课程充满了大量的学习，不仅是理论上的，还有实际的例子。Risabh先生非常友好地分享了他的实践经验和数据科学家/ML工程师面临的实际问题。“深度学习的伦理”这个话题真的是每个人都必须遵循的金块。谢谢1室和自拍学院的精彩课程。

这门课是给谁的
初学Python的开发人员热衷于学习机器学习和数据科学
任何对机器学习感兴趣的人。
至少有高中数学知识并想开始学习机器学习的学生。
任何中级水平的人，他们知道机器学习的基础，包括像线性回归或逻辑回归这样的经典算法，但是他们想了解更多关于机器学习的知识，探索机器学习的所有不同领域。
任何不太擅长编码，但对机器学习感兴趣并希望轻松将其应用于数据集的人。
任何想在数据科学领域开始职业生涯的大学生。
任何想要提升机器学习水平的数据分析师。
任何想通过使用强大的机器学习工具为业务创造附加值的人。

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 33 lectures (13h 11m) | Size: 3.9 GB

Complete Machine Learning Course with Python for beginners

What you’ll learn:
Master Machine Learning on Python
Make powerful analysis
Make accurate predictions
Make robust Machine Learning models
Use Machine Learning for personal purpose
Build an army of powerful Machine Learning models and know how to combine them to solve any problem
Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
Clean your input data to remove outliers

Requirements
No prior experience needed, you will learn what is needed. (A basic python knowledge will definetly increase your chances of learning fast))

Description
Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That’s just the average! And it’s not just about money – it’s interesting work too!

If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career path.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won’t find academic, deeply mathematical coverage of these algorithms in this course – the focus is on practical understanding and application of them. At the end, you’ll be given a final project to apply what you’ve learned!

Our Learner’s Review: Excellent course. Precise and well-organized presentation. The complete course is filled with a lot of learning not only theoretical but also practical examples. Mr. Risabh is kind enough to share his practical experiences and actual problems faced by data scientists/ML engineers. The topic of “The ethics of deep learning” is really a gold nugget that everyone must follow. Thank you, 1stMentor and SelfCode Academy for this wonderful course.

Who this course is for
Beginner Python Developers enthusiastic about Learning Machine Learning and Data Science
Anyone interested in Machine Learning.
Students who have at least high school knowledge in math and who want to start learning Machine Learning.
Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
Any students in college who want to start a career in Data Science.
Any data analysts who want to level up in Machine Learning.
Any people who want to create added value to their business by using powerful Machine Learning tools.
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