Takshashila Institute (TI) eSeminar Series: Topics in Data Science, AI, and Machine Learning

Date Starting on Monday, September 14, 2020: 2 one-hour webinars per week (Mondays and Wednesdays) for 4 consecutive weeks
Time: 1pm-2pm PDT (4pm-5pm EDT) with Q&A afterwards
Director Professor Rao Vemuri, PhD, Chair of the Taksha Center for Machine Learning and Hydrology (TCMLH – www.taksha.org/divisions/TCMLH), and Distinguished Member of the Technical Advisory Committee (TAC) for the Taksha Center of Data Science (TCDS – www.taksha.org/divisions/TCDS); Emeritus Professor, University of California, Davis (See bio at www.taksha.org/advisors/vemuri.)


Are you interested to know about the revolutionary changes taking place in the world of Data Science, AI and Machine Learning? Do you feel left out because your formal training is not in IT related fields? Are you trying to make a change in the direction of your career? Then this introductory course is for you. All  we ask of you is to invest an equal amount of time outside the “classroom” as you do in the webinars. If you are facile with computers and programming also, then the sky is the limit for your marketability! We assume that you are familiar with the use of computers and programming; if you are familiar with Python, even better!

This seminar series will consist of four consecutive weeks of two hour-long webinars per week. It provides an overview of the entire field of Data Science, Artificial Intelligence, and Machine Learning, starting from data collection to selection of a method to solve and evaluation of the method. Tentatively, the topics to be covered are:

  1. Data Science and its relation to AI, ML, and Deep Learning
  2. Big Data, Pre-processing, Data Wrangling, Cleansing, Exploratory Data Analysis Visualization
  3. Introduction to Programming in Python, Python libraries, Scikit-Learn, Tensor Flow, Keras. Database concepts like SQL, No SQL. Distributed frameworks like Spark
  4. Some important Learning Algorithms (Minimum 6 webinars)
  5. Decision Trees vs. Random Forests
  6. Recommendation Systems and Collaborative Filtering
  7. Supervised Learning: Linear Regression
  8. Classification: Logistic Regression
  9. Neural Nets and Back Propagation
  10. Bayes’ classifier
  11. Feature engineering
  12. Given a problem, how to get started? How to select and evaluate an algorithm?

Pre-requisites: Basic background in calculus, vector notation, and a passing familiarity with statistical concepts. Facility with using computers and ability to read code even if you do not have expertise in writing Python code. You are expected to have access to a computer with Anaconda distribution of Python installed on it. You are expected to know how to use Jupyter Notebook. If you are not familiar with this, ask a colleague to help you get started with Python and Jupyter Notebook. Then you can read and understand code segments shown in the class. Knowledge of programming is not essential to follow the lectures, but it is essential to get a job!

Certificates of Attendance are available on request for $40.

Click on the icon below to register. You will leave this website, and register for the eSeminar at www.brownpapertickets.com. There is no cost for this eSeminar series, but to help support Taksha’s continued ability to present this and other eSeminar series, please consider a donation to Taksha by clicking HERE or above, at the top right of this page.


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Please note that Takshashila Institute (dba Taksha Institute or TI) is an independent, not-for-profit organization, tax-exempt under IRS Code 501(c)3, founded in 1976 in Virginia, US. All proceeds from donations, registration fees, sales, grants, and/or contracts support Taksha’s educational and research activities.