Gain insights into data science fundamentals, explore machine learning and big data, and delve into real-time projects. Discover systematic approaches to data acquisition, wrangling, and solving diverse problems.

new.tar.gz

data_science_notebooks

docker_env

data_science_notebooks-copy

There is a lot of dispersed, and somewhat conflicting information on the internet when it comes to data science, making it tough to know where to start. Don't worry. 

This course will get you familiar with the state of data science and the related fields such as machine learning and big data. You will be going through the fundamental concepts and libraries which are essential to solve any problem in this field. 

You will work on real-time projects from Kaggle while also honing your mathematical skills which will be used extensively in most problems you face. You will also be taken through a systematic approach to learning about data acquisition to data wrangling and everything in between. This is your all-in-one guide to becoming a confident data scientist.

An Introductory Guide to Data Science and Machine Learning

Among the most common types of data available out there are Image and Video data. We can apply computer vision and Deep Learning in Data Science for the betterment of society. Some applications are listed below.

# 1. Image classification

Image classification refers to classifying an image into one of the categories on which a model is trained (We will expand on models in the upcoming lessons). Massive work has been done on image classification, and models inspired by the working of the brain are being used to do this task.




In this lesson, you’ll learn about Data Science and Image Analysis. Data Science specifically computer vision with deep learning, is being used extensively in image analysis for surveillance, image classification, object detection, object tracking, segmentation, self-driving cars and many more. 

What is Data Science ?

Applications of Data Science

Overview of Libraries

Probability and Statistics

Machine Learning Part-1

Machine Learning Part-2

Machine Learning Part-3

Deep Learning

Machine Learning Tools and Libraries

Big Data Tools and Technologies

Where to go next ?

Image Analysis

1.