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

# What is Kaggle?

Kaggle is an online community of data scientists and Machine Learning practitioners. It is a  subsidiary of Google LLC. We have been solving problems in the sections of Regression and Classification taken from Kaggle. 

# Kaggle competitions

Kaggle competitions are meant to provide the problems to solve in the field for data scientists around the world. There are the following competition types on Kaggle. 

* **Featured competitions**: Kaggle is best known for featured competitions. They are full-scale Machine Learning problems that are difficult and are generally commercially-purposed prediction problems. These competitions offer millions of dollars in prizes. 

* **Research**: These types of competitions are more experimental than featured competitions. They offer to work on problems that may not have any solution. They may or may not offer prices. 

* **Getting started**: Getting started competitions are offered for beginners. The competitions that we have been solving in this course fall under the category of "Getting Started" competitions. 

* **Playground**: Playground competitions are fun types of competitions and mainly targeted for beginners.


# Kaggle Datasets

Kaggle Datasets provide a whole list of Datasets provided by the community. They can be downloaded and used. We used the Customer Segmentation dataset in this course from Kaggle. 

# Kaggle kernels

Kaggle kernels are notebooks that also provide a GPU compute. They come up with the Python programming language pre-built into them and many more resources like RAM for writing the code and then doing a submission into the competitions.

# Kaggle discussions 

Kaggle discussion is a platform to share and learn from the community out there. Discussions involve presenting ideas for solving a problem, and sharing state-of-the-art papers. 



# Kaggle Progression System 

*Kaggle's Progression System uses performance tiers to track your growth as a data scientist on Kaggle. Along the way, you’ll earn medals for your achievements and compete for data science glory on live leaderboards.* In each of the data science expertise above (Competitions, Datasets, Notebooks and Discussions), there are five performance tiers: 
* Novice 
* Contributor 
* Expert 
* Master
* Grandmaster

Now, we will be looking into tips for making a career on Kaggle. 

# Tips 

* Compete on Kaggle to solidify your learning. 

* Take part in every competition. If the competition involves concepts that you haven't studied before, learn while you compete. 

* Kaggle demands dedication, so give time to Kaggle daily.

* Set incremental goals on Kaggle. 

* Read code on Public kernels thoroughly along with the comments. 

* Read and participate in discussion forums on Kaggle.  

* Work solo on Kaggle to build up your skills. 

* Above all, **Never give up** while competing and learning on Kaggle.






Kaggle is an online community of data scientists and Machine Learning practitioners, and it provides amazing competitions to people to plug in their skills of Data Science. This lesson provides some tips to starting a career on Kaggle.


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 ?

Starting Career on Kaggle (Tips)

What is Kaggle?