This repository contains the resources which I have referred to while learning machine learning (ML) and deep learning (DL).

I credited a course named Introduction to Machine Learning (EE769) at IIT Bombay (Spring 2020 semester). Prof. Amit Sethi was the instructor of this course. Unfortunately, this course could not be completed due to COVID-19. There were two assignments in this course:

1. Implementing Support vector machine (SVM) from scratch
2. Predicting employee attrition using a binary classifier

The problem statements and code files for the above mentioned two assignments are available in the folder named Assignments. For the final evaluation of this course, I worked on a project titled Sales Prediction for Big Mart Outlets. This project is one of the machine learning competitions organized by Analytics Vidhya, in which I participated and could manage to get in the top 18% (630/3638) of the submissions. The detailed approach of this project is available in the folder named Sales-Prediction-for-Big-Mart-Outlets.

While crediting the course EE769, the following books were instrumental in digesting the underlying principles of ML:

• Pattern Recognition and Machine Learning by Christopher Bishop
• Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow
• The Elements of Statistical Learning by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
• Understanding Machine Learning: From Theory to Algorithms by Shai Ben-David and Shai Shalev-Shwartz

Some of these books are freely available on the Internet.

In the fall semester (August 2020), I have credited another course named Engineering Data Mining and Applications (ME781) at IIT Bombay. Prof. Asim Tiwari was the instructor of this course. The assignments and quiz of this course are available in the folder named ME781.

In the summers of 2020, I completed one career track, named Machine Learning Scientist with Python on DataCamp. Additionally, I finished a few more courses, as given below.

• Human Resources Analytics: Predicting Employee Churn in Python
• Introduction to SQL

The slides used in the courses mentioned above, code files, datasets, cheat sheets, etc. are accessible in the folder named DataCamp_Notes.

After finishing the ML/DL courses, I completed a few projects using Python on DataCamp, as given below.

• Reducing Traffic Mortality in the USA
• Which Debts Are Worth the Bank's Effort?
• Naïve Bees: Predict Species from Images
• Classify Song Genres from Audio Data
• ASL Recognition with Deep Learning
• Predicting Credit Card Approvals
• The Android App Market on Google Play
• Do Left-handed People Really Die Young?

Additionally, I did a project on visualizing COVID19 using the R programming language.

The notebooks and the datasets of these projects are accessible in the folder named DataCamp_Projects.

In July 2019, I attended the 4th Summer School on Machine Learning, hosted by IIIT Hyderabad. The slides and relevant research papers used in this summer school are accessible in the folder Summer_School_IIIT_Hyd.

While looking for regular doubts in ML/Python, I found some of the online resources very lucid. All these resources are available in the markdown file named Online-resources.md.