Data Science and Machine Learning with Python and Libraries (Update)

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Data Science and Machine Learning with Python and Libraries (Update)
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz
Language: English | VTT | Size: 1.77 GB | Duration: 12 section | 54 lectures | (5h 30m)
What you'll learn
Data Science with Python

Using Python Libraries for Data Science
Data Analysis
Python Libraries such as Numpy, Matplotlib, Pandas
Visualizing Data
Linear Algebra
Statistics
Probability
Hypothesis and Inference
Gradient Descent
Machine Learning
k-Nearest Neighbors
Naive Bayes
Simple Linear Regression
Multiple Regression
Logistic Regression
And Much More!

Requirements
A little bit of Python understanding will be good (not necessary)
Description

WHAT IS DATA SCIENCE

As the world is progressing in science and technology, there is an enormous increase in the need for advanced tools to store information and mine data that is being produced indefinitely. And the key to this problem is data science. Data science is a field of study that develops scientific and systematic methods to record, process and analyze data to withdraw significant and useful information that can be both structured and unstructured. Unstructured data is the one that is generated by mobile devices and websites while structured data is an organized data which is mostly generated by the users e.g. emails, chats, telephone calls etc. Data science uses scientific methods and algorithms to extract knowledge. Industries require the use of this field immensely and the industrialists now realize the value of data science and the benefits it can provide to the business, thus, it has become very popular currently.

The need for data science

An immediate question that rises in the mind after hearing about data science is why is there a need to dig into depths for such a tool? So, it's important to understand that previously the data produced used to be structured and thus it was somehow easy to extract the meaningful information and process it. However, contemporarily the data that is being produced is mostly unstructured as there are multiple sources of its generation such as multimedia files, logs, documents etc. and data science provides aid in turning raw data into consequential one.

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