General Information

• For more information regarding our club please visit the official SDSU AI Club webstite
• For further information regarding the workshops you can contact me through my email: hnevarez1285@sdsu.edu
• For general club questions you can contact our club email: sdsuaiclub@gmail.com

Table of Contents

• Workshop 1 (Python 1)
• Workshop 2 (Python 2)
• Workshop 3 (Data Science 1)
• Workshop 4 (Data Science 2)
• Workshop 5 (Data Science 3)
• Workshop 6 (Computer Vision 1)
• Workshop 7 (Computer Vision 2)
• Workshop 8 (Computer Vision 3)
• Workshop 9 (K means clustering)
• Workshop 10 (Convolutional Neural Network)

Workshop 1

Python Introduction

In this workshop we will be going over the basics of using python

• Click Here to be taken to the beginner python workshop

Beginner Python tutorial summary:

• Data Types: Integer, Float, Complex, Boolean, and Strings
• List: Overview of a list, list methods, iterating through lists
• Dictionaries: Overview of a dictionary, dictionary methods, iterating through a dictionary
• If Statements: Declaring if statements, if-else chains, if boolean statements

Activity

• For this weeks activity we will be doing coding questions that apply our basic knowledge of the fundamentals we learned in workshop 1
• Sign up Here in order to get started with the activity
• Hints: These hints will be useful to you solving the coding problems

Workshop 2

In this workshop we will be going over more advanced python concepts

• Click Here to be taken to the advanced python workshop
  
• Google Colab Introduction

Advanced Python tutorial summary:

• Loops: For loops and while loops, loop use case, looping through data structures
• modules: Importing modules, giving modules an alias usnig “as”
• Functions: defining a functions, arguements and parameters, arbitrary arguements, returning values
• Lambda Functions: Syntax, use case, if-else chains
• Main Function: __name__ == “__main__”, creating main function

Workshop 3 (Data Science 1)

Our goal with this 3 part workshop is to create a tool that can estimate software engineer salaries. The main purpose of this 3 part workshop is to develop and understand the different roles involved in a typical data science job.

Cleaning data

• Data collection and data cleaning are said to be the most important and time consuming parts of a data scientists role
• in this workshop we will be going over how to clean the data
• The data was previously collected so we can easily use it for this project

If you are interested in the scripts I used to collect the data Click Here but we won’t be going over that in this workshop

Let’s get started with the workshop!

This workshop is broken down into two parts

• Part 1: For this first part we will go into a very introductory tutorial on pandas that will prepare us enough to begin cleaning our data
• Part 2: In this part we will be coding from scratch how to clean the data

Resources Used

• Pandas
• GlassDoor data Make sure to download the data so we can use it for our workshop
• Reference Sheet This reference sheet goes over all the pandas functions we used in depth for better understanding during the workshop

Code used in the workshop

• Data Cleaning: This will take you to the python script we coded during this workshop just incase you need to look over it after the workshop

Workshop 4 (Data Science 2)

This is the second part of our data science multi-week workshop. In this workshop we will be going over how to plot our data and analyze for our model building

Exploratory Data Analaysis

• We will analyze our data for better understanding of the features and their correlation
• We will use our previously cleaned data for this workshop

Resources Used

• Pandas
• Matplotlib
• Numpy
• Cleaned Data

Let’s get Started with the workshop!

• Numpy First we’re going to go over the numpy library to become familiar with numpy arrays
• Matplotlib Next, we’ll go over matplotlibs pyplot module in order to develop an understanding for how we’ll use our data to perform exploratory data anlysis
• Google Colab Finally we will analyze our data using both numpy and matplotlib in order to create graphs that will help us understand relationships between our data

Code Used in this workshop

• Explortatory Data Analysis: This will take you to the python script we coded during this workshop just incase you need to look over it after the workshop

Workshop 5 (Data Science 3)

This is the third and final part to the data science multi-week project. In this workshop we will do create the train and test the models

Model Building

• We will use the features we decided on in our EDA and create models that will predict how much someone will get payed based on job listing
• The machine learning models we’ll use are Linear Regression, Lasso Regression, and Random Forest

Resources used

• Pandas
• Sklearn
• Pickly
• Reference Sheet

Let’s get started with the workshop

• Google Colab
• Support Vector Regressor
• Random Forest

Code Used in this workshop

• Model Building: This will take you to the python script we coded during this workshop just incase you need to look over it after the workshop

Workshop 6 (Computer Vision 1)

This is the first part of a 4 week-long computer vision workshop. In this workshop we will go over the basics of using opencv for image processing.

Opencv basic tutorial

In this workshop we will be going over the basic functions of opencv

• Opencv Workshop

Resources used

• Opencv
• imutils
• matplotlib
• Tetris Image
• Reference Sheet

Beginner opencv tutorial summary:

• Loading images
• Cropping images
• resizing images
• rotating images
• Drawing on images: rectangle, circle, line, and text

Activity

• In this activity we will be detecting objects on an image through the use of various built in opencv functions
• Click here to be taken to google colab in order to get started

Code Used

• Object Detection This is the script we coded during this workshop just incase you need to look over it after the workshop

Workshop 7 (Computer Vision 2)

This is the second part of the computer vision workshop. In this workshop we will expand our knowledge of OpenCV and work with real-time video. We will be creating an application that can track motion allow us to draw by using our webcam.

Resources Used

• OpenCV
• Numpy
• Reference Sheet

Code Used

AirDraw explained
AirDraw Code Complete

Workshop 8 (Computer Vision 3)

This is the third and final part of our computer vision workshops. In this workshop we will be learning about neural networks and creating a neural network that can be trained to recognize hand written numbers

Resources Used

• Tensorflow
• Keras
• Matplotlib

Neural Network introduction

• Neural Networks

Activity

• We wil be creating a neural network and train it on the MNIST dataset in order to accurately predict hand written numbers
• Click Here to begin the activity

Code Used

MNIST model building

Workshop 9 (K means clustering)

In this workshop we will be going over unsupervised learning. We will then learn a clustering machine learning model and use it in order to cluster different types of shoppers in a mall.

Resources Used

• Pandas
• Matplotlib
• Seaborn
• Scikit Learn

Neural Network introduction

• K-means Clustering Introduction

Activity

• We will be implementing K-means to cluster different types of mall shoppers
• Click Here to begin the activity

Code Used

• K-means

Workshop 10 (Convolutional Neural Network)

In this workshop I will be introducing Convolutional Neural Networks. To do so we will first go over the theory of how a CNN is structured and after we will have a live coding session where we try and classify different types of objects using the Cifar-10 dataset.

Resources Used

• Tensorflow
• Keras
• Numpy
• Matplotlib

Neural Network introduction

• Convolutional Neural Networks

Activity

• We wil be creating a Convolutional Neural Network and train it on the Cifar-10 dataset in order to accurately predict different objects
• Click Here to begin the activity

Code Used

Convolutional Neural Network applied to Cifar-10