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   The Data Science Club presents Hands-on Machine Learning Training for Beginners in Python. This is a 6-week long training program and it is scheduled every Saturday 12:00pm - 1:00pm from  September 14th. Read through for more details -

 

Highlights 

  1. This training program is designed for beginners in machine learning. 

  2. The event is free and open for Data Science Club members. (Undergrad, graduate and doctorate students)

  3. After 6-week classes given, participants start their own projects under the guidance of mentors. 

  4. A team should consist of 3 – 5 students and there are no restrictions on the software and programming language used.

  5. The prizes and the certificates will be given to trainees through a project presentation at the beginning of 2020 Spring semester. 

Curriculum (Every Saturday 12:00pm - 1:00pm at JSOM)

September 14th - 1. Introduction to Machine Learning 

    This module introduces basic machine learning concepts, tasks, and workflow using an example problem. , and implemented using the scikit-learn library. Don't worry, we will start from setting up the environment for all the codes we will go through. 

 Learning Objectives:

  1.  Demonstrate the understanding of basic machine learning concepts and workflow

  2.  Distinguish between different types of machine learning tasks, based on examples of how they are applied to real-world problems

 

 

September 21st – 2. Data Manipulation with Pandas and Numpy

   In this module you'll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing -- Pandas. You'll learn how to read data into DataFrame structures, how to query these structures, and the details about such structures are indexed.

      

Learning Objectives:

  1. Apply fundamentals of data manipulation using Pandas

  2. Use Python and Pandas to take tabular data, clean it, and run basic inferential statistical analyses

 

 

September 28th, October 19th – 3. Supervised Learning 

   This module dives into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting.

Learning Objectives:

  1. Demonstrate the understanding on how different supervised learning algorithms - in particular, those based on linear models - estimate their own parameters from data to make new predictions

  2. Demonstrate the understanding of the strengths and weaknesses of particular supervised learning methods in order to apply the right algorithm for a given task

  3. Apply specific supervised machine learning algorithms in Python with scikit-learn

October 26th – 4. Model Evaluation

   This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models.

Learning Objectives:

  1. Demonstrate the understanding on why accuracy alone can be an inadequate metric for getting a more complete picture of a classifier's performance

  2. Demonstrate the understanding of the motivation and definition of a variety of important evaluation metrics in machine learning and how to interpret the results of using a given evaluation metric

November 2nd – 5. Ensemble Learning

   This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), bagging, pasting, voting classifier, and adaboost.

Learning Objectives:

  1. Apply the right algorithm for a given task by understanding the strengths and weaknesses of additional supervised learning methods.

  2. Apply additional types of supervised machine learning algorithms in Python with scikit-learn.

November 9th – 6. Dimension Reduction

   In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets

Learning Objectives:

  1. Learn about the curse of dimensionality and get a sense of what goes on in high-dimensional space.

  2. Learn and apply two main approaches to reduce dimensionality, PCA and LLE.

November 16th – 7. Choose Your Project 

   Based on the materials learning from modules, we group a team and start our own projects. Participants can choose whatever data and problem statements they want with your teammates. You can check projects DSC members did last semester from this page.

February 2020– 8. Project Presentation 

   Based on the materials learning from modules, we are going to present our data science projects. There will be a feedback session, during which the panel comprising of industry professionals and professors will provide their valuable opinions and insights on the projects. This is not only a good chance to see how the team’s approach and solve real-world data science problems, but it is also a good opportunity to network with experts from the industry and seek guidance from experienced students. (Details will be announced later.)