Python for Data Science & Machine Learning: Zero to Hero

本机器学习课程将为您提供 Google、Amazon 甚至 Udemy 等公司如何利用机器学习和人工智能 (AI) 从海量数据集中收集意义和见解的基础知识。Glassdoor 和 Indeed 均报告称，​​数据科学家的平均工资为 120,000 美元。这是标准，不是例外。

数据科学家已经很受欢迎了。在当今紧张的劳动力市场中，很难留住他们。具有科学训练、计算机专业知识和分析才能的罕见组合的人才严重短缺。

今天的数据科学家与 80 年代和 90 年代的华尔街“量化专家”有着相同的标准。当需要创新算法和数据方法时，物理学家和数学家纷纷涌向投资银行和对冲基金。

因此，毫不奇怪，数据科学正在成为现代充满希望的职业道路。它以分析为重点，由代码驱动，并在计算机上执行。因此，过去几年工作场所对数据科学家的需求一直在稳步增长，这并不令人震惊。

另一方面，可用性一直很低。获得被聘为数据科学家所需的教育和经验是很困难的。这就是为什么我们首先制作了这门课程！

每个主题都用简单的英语描述，并且课程尽量避免使用数学符号和行话。一旦您可以访问源代码，您就可以对其进行试验和改进。在现实世界中学习和应用这些算法，而不是在理论或学术环境中，是本课程的重点。

每个视频都将为您提供一个可以立即实施的新视角！

如果您没有统计学背景，请不要让这阻止您参加本课程；我们欢迎各级学生。

Master Data Science & Machine Learning in Python: Numpy, Pandas, Matplotlib, Scikit-Learn, Machine Learning, and more!

Published 10/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 185 lectures (6h) | Size: 2 GB

What you’ll learn

Gain familiarity with Pandas, a data analysis tool
Get a grasp on the theory behind basic and multiple linear regression
Tackle regression problems easily
Discover the logic behind decision trees
Acquaint yourself with the various clustering algorithms

Requirements

The ability to do simple math
No programming experience needed
No prior data science knowledge required
Readiness, flexibility, and passion for learning

Description

This machine learning course will provide you the fundamentals of how companies like Google, Amazon, and even Udemy utilize machine learning and artificial intelligence (AI) to glean meaning and insights from massive data sets. Glassdoor and Indeed both report that the average salary for a data scientist is $120,000. This is the standard, not the exception.

Data scientists are already quite desirable. It’s difficult to keep them on staff in today’s tight labor market. There is a severe shortage of people who possess the rare combination of scientific training, computer expertise, and analytical talents.

Today’s data scientists are held to the same standards as the Wall Street “quants” of the ’80s and ’90s. When the need arose for innovative algorithms and data approaches, physicists and mathematicians flocked to investment banks and hedge funds.

So, it’s no surprise that data science is rising to prominence as a promising career path in the modern day. It is analytic in focus, driven by code, and performed on a computer. As a result, it shouldn’t be a shock that the demand for data scientists has been growing steadily in the workplace for the past few years.

On the other hand, availability has been low. Obtaining the education and experience necessary to be hired as a data scientist is tough. And that’s why we made this course in the first place!

Each topic is described in plain English, and the course does its best to avoid mathematical notations and jargon. Once you have access to the source code, you can experiment with it and improve upon it. Learning and applying these algorithms in the real world, rather than in a theoretical or academic setting, is the focus of this course.

Each video will leave you with a new perspective that you can implement right away!

If you have no background in statistics, don’t let that stop you from enrolling in this course; we welcome students of all levels.

Who this course is for
Aspiring Machine Learning Professionals
Anyone interested in expanding their skill set with machine learning and Python
Inquisitive technologists interested in seeing Machine Learning in action
Those who are already proficient in programming and want to expand their capabilities by learning about machine learning