**Welcome to ClaoudML****Free Data Science & Machine Learning Resources****Do you want free resources?**Do you want more resources on Data Science, Analytics, and Machine Learning? Simply enter your email below and I'll send you weekly updates.

**Free resources won't hurt you!**Who is Randy Lao?

**You're on a journey to learn Data Science, and I'm here to help you along the way!**My name is

**Randy Lao**☁️**.**I like to contribute my share of resources with you all. After realizing that most data science resources are hard to find to those new to the field, I want to make this site an easily accessible way for you to get your materials.**These resources should come in handy to any data scientists at all levels of experience.****Pass the baton**–**Share this site with your friends!**Data Science☁️

**"Data Science is watching you"**Data Science Resources

The best curated list of Data Science materials on the web!

This is a must read to prepare yourself for the Data Science Interview!

All the resources you need to learn Web Scraping in Python + BeautifulSoup

Machine Learning ☁️

**"Machine Learning is the future"**Machine Learning Resources

All of the best Machine Learning resources.

The foundations of Machine Learning is a must. Here are some of the most essential concepts you need to know for Machine Learning.

A new approach to learn Machine Learning Google Style. This is a crash course series!

Start Here if you are new to Machine Learning! This article will cover the basics in an easy manner.

A nice slide presentation about Deep Learning and Machine Learning. It's so GOOD! By Tess Ferranandez

Free Books ☁️

"Reading one book is like eating one potato chip"

Data Science & Machine Learning Books

They are all free!

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Math and Statistics ☁️

"There are three kinds of lies - lies, damned lies and statistics."

Essential Math & Statistics

The essential maths to understand Data Science and Machine Learning

*Matrices, Vectors, Eigenvalues/Eigenvectors, Linear Transformations & Equations**Sampling Distribution, Central Limit Theorem, Hypothesis Testing, Types of Errors, ANOVA, Chi-Square, T-Test**Random Variables, Types of Distributions, Sampling, Conf. Intervals, Z Scores**Formulating a Real Problem to a Mathematical Model**Gradient Algorithms & Objective Functions*Trees, Nodes, Edges

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