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S1
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README.md
cross_validation.ipynb
statics recods.xlsx

README.md

Applied Data Science Workshop (ADS)

A comprehensive 8-day training program for hands-on introduction to big data, data science, and machine learning models, methods and algorithms.

The workshop will take participants through the conceptual and applied foundations of the subject. Topics covered include:

  • Data Science techniques, models, methods and best practices.
  • Machine Learning theory, types of learning, and models
  • Practical examples of applying most frequently used Machine Learning industry models

Labs are developed to practically learn how to use the R and Python programming languages and packages for applying the main concepts and techniques of data science and machine learning.

The Workshop will provide participants with an applied introduction to data science industry practices and models of machine learning. The workshop has a strong focus on gaining hands-on experience implementing algorithms and building predictive models on real datasets. By the end of the workshop, participants will be ready to implement the machine learning algorithms using data science on their own data, and immediately generate business value.

workshop Course outline

Module 1: Introduction to the course

  • Recap of Data Science Fundamentas
  • Comparing R and Python: read this Infoworld article.

Session Outline related to R Basics

Used Resources during session

Module 2: Manchine learning algorithms and techniques

For this assignment you will be working with the Titanic Data Set from Kaggle. This is a very famous data set and very often is a student's first step in machine learning!

you will be trying to predict a classification- survival or deceased by using python this time. you need to do some additional cleaning in able to use the kaggle data in your learning model.

after your finish upload your work in the notebook format into your github account

Module 3 & 4: Supervised learning

Capstone Project 1

In this project we will be learning how do we combine everything we learn about your R and Python programming skills and your data visualization, data engineering (dataframe operations) and apply machine learning methods in order to solve a historical data problem.and in this case we will explore a local data taken from civil engineering department at An-Najah National University (ANNU) statics record.xlsx

Module 5- Special Topics: Natural language processing (NLP)

Module 6: Support Victor Machine (SVM) & Principle Component Analysis

Module 7: Neural Networks and deep learning

Deep Learning with Tensorflow and Keras

Module 8: Data Science ethics