Machine Learning and Data Science - ML1


ML1: Machine Learning and Data Science

Welcome to the First Edition (Cohort C1) of ML-1

The objective of this module is to provide a fundamental understanding of data analysis. The course proceeds in 3 parts, following the Data Science Process:

Obtain and clean the data: we will teach you how to obtain, clean, and process data from different sources such as scraped web pages, spreadsheets, APIs, and documents.

Exploratory Data Analysis: we develop your skills in pre-modeling and post-modeling exploratory data analysis and visualization. This part is all about understanding your data.

Modeling: We choose some very specific models to cover, from the perspective of teaching techniques which are generalizable to any models. Thus we will cover classification and recommendation engines. We’ll also cover similarity and PCA, as a way of understanding structure in your data and the models you ran on them.

ContentAfter you finish this module, you will be ready to run the entire data science process, all on your own, from fetching (from the internet or databases) and cleaning the data, setting up pipelines, to exploratory data analysis, visualization, and modeling.

This page introduces you to the team, the basic instructions, the schedule, and various elements of our class.

Interested in Joining?

If you would like to apply to this course, please go here.

We also provide this course as part of our Masters and Accelerated program, check this link out to get more information and apply.

The Team

Dr. Rahul Dave

Dr. Rahul Dave, former Data Science faculty at Harvard University, will be your instructor for Data Science 1. You can read more about him here. In addition to the classes, you can schedule problem-solving classes with the faculty and mentors throughout the rest of the week.

The teaching assistants for the duration of this course are:

Anusha Sheth

Arya Mohan

The Coursework

We have very carefully designed the coursework to give you, the student, a wholesome learning experience. Each week shall include:

Session: What to expect

Before the session begins, students are expected to complete a pre-class reading assignment and attempt a quiz based on the same.

A session will have the following pedagogy layout which will be repeated a few times:

After the session, students are expected to complete a short post-class quiz based on the principal concepts covered in class and optional post-class reading will be provided.

Lab: What to expect

A lab is a TA driven 1.5 hour session that is divided into 3 major parts.

The Class

Welcome Session - Preparing for this class

There will be a Welcome Session scheduled on 25 January 2021 at 7:00 PM IST for all registered students. Please check your mail for more information.

NOTE: Below timings are in IST

Lecture Sessions:

Lab Sessions:

Office hours:

Course Pre-Requisites

You are expected to have programming experience and basic machine learning concepts such as model fitting, test-validation, regularization, etc.

You can also use, MLPrep and PyPrep provided by Univ.AI to learn the topics mentioned above.

In addition to this, you can use the book - Introduction to Statistical Learning to refresh the basic mathematical concepts and Python Data Science Handbook to refresh machine learning concepts.

Logistics: What do you need to begin?

Do this first!

We assume you have a Univ.AI account, created when you signed up at courses.univ.ai. If not, email programs@univ.ai.

Class Policies

COMING SOON

Education software we use

Diversity & Inclusion

We actively seek and welcome people of diverse identities, from across the spectrum of disciplines and methods since Artificial Intelligence (AI) increasingly mediates our social, cultural, economic, and political interactions [1].

We believe in creating and maintaining an inclusive learning environment where all members feel safe, respected, and capable of producing their best work.

We commit to an experience for all participants that is free from – Harassment, bullying, and discrimination which includes but is not limited to:

Reference:

[1] K. Stathoulopoulos and J. C. Mateos-Garcia, “Gender Diversity in AI Research,” SSRN Electronic Journal, 2019 [Online]. Available: http://dx.doi.org/10.2139/ssrn.3428240.

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