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.

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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

Acknowledgements:  <br> 

* Numpy Documentation<hr>

* Scikit Learn Documentation<hr>

* TensorFlow Documentation<hr>

* Open GPU Data Science (RAPIDS)<hr>

* MapReduce Tutorial – Fundamentals of MapReduce with MapReduce Example (Edureka)<hr>

* Sampling Techniques by Seema Singh (Towards Data Science)<hr>

* Amazon Whitepapers<hr>

* Data Mining: Concepts and Techniques by Jiawei Han Micheline Kamber Jian Pei<hr>

* Decision Tree Algorithm, Explained (kdnuggets)<hr>

* What is Hierarchical Clustering? (kdnuggets)<hr>

* Normal Distribution in Statistics (Statistics By Jim)<hr>

* Naïve Bayes Classifiers (GeeksforGeeks)<hr>

* Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits<hr> 

* Standard Deviation and Variance (Mathisfun)<hr>

* Normal Distribution (Mathisfun)<hr>

* Conditional Probability (Mathgoodies)<hr>

* Understanding Support Vector Machine (SVM) algorithm from examples (along with code) (Analytics Vidhya)<hr>

* 6 Easy Steps to Learn Naive Bayes Algorithm with codes in Python and R (Analytics Vidhya)<hr>

* Categorical Encoding (One Hot Encoding) in Feature Engineering (Analytics Vidhya)<hr>

* Pseudo-labeling a simple semi-supervised learning method (datawhatnow)<hr>

* Understanding LSTM Networks (Colah's Blog)<hr>

* Association Rules Generation from Frequent Itemsets (mlxtend documentation)<hr> 

* A STEP-BY-STEP EXPLANATION OF PRINCIPAL COMPONENT ANALYSIS (PCA) (Built In Blog)<hr>

* A SIMPLE INTRODUCTION TO COLLABORATIVE FILTERING (Built In Blog)<hr>

* Semi-supervised learning (Wikipedia)<hr>  

* Machine Learning Mastery with Python<hr>

* Professor Andrew Ng<hr> 

* Stacking in Machine Learning (Supunsetunga Blog)<hr> 

* Saman Saman, The wonderful Reinforcement Learning<hr>

* Jason Brownlee, Data Preparation for Machine Learning, Machine Learning Mastery, and Statistical Methods for Machine Learning,<hr>

* Feras Fraige, Engineering Probability & Statistics (AGE 1150)   

* Soledad Galli, Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build Machine Learning models,<hr>  

* Kaggle Notebooks by Yassine Ghouzam, Ashwini swain, Robert Kwiatkowski, Fazil T, Vikum Sri Wijesinghe, Tuatini GODARD, Lavanya Shukla, Vikas Singh, Alexandru Papiu<hr>


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 ?

References and Acknowledgements