Introduction to Kaggle and Jupyter for Data Science

#Python #Kaggle #numpy #pandas #Jupyter #DataScience #MachineLearning #IPython #Flask #Regression #Classification #ANN #DeepLearning
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Introduction to Kaggle and Python-Jupyter for Data Science (Entry Level)

Welcome to our exclusive workshop, Introduction to Kaggle & Jupyter for Data Science (Entry Level) designed to gap you with the essential tools and skills for a exploratory journey in data science.

In this session, you will:

know what Kaggle is:
Learn how to create a Kaggle account, navigate its rich environment, and leverage its features for competitions and datasets.


Navigate Python-Jupyter Notebook:
Gain hands-on experience with Anaconda installation and explore the Jupyter Notebook interface.


Understand Data Science Tasks:
Delve into core data science tasks such as classification, regression, clustering, etc.

 
Python Package Manager: Discover how to install and utilize key Python libraries for effective data manipulation using PIP and Anaconda.


Explore Machine Learning Workflow: Acquire foundational knowledge on building and training machine learning models, address common challenges, and implement methods for evaluating model performance.


Engage in Practical Application: Put your learning into practice with a guided example, including the creation and submission of predictions in a Kaggle competition.



This workshop is meticulously designed for those at the entry level, offering a simple introduction to Kaggle and Python-Jupyter.
Join us to refine your skills and gain practical insights from an experienced data scientist.

Date: Saturday, August 17, 2024
Location: Virtually via Google Meet


  Date and Time

  Location

  Hosts

  Registration


  • Date: 17 Aug 2024
  • Time: 05:00 PM to 06:30 PM
  • All times are (UTC+03:00) Cairo
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  • Contact Event Host
  • Starts 10 August 2024 12:00 AM
  • Ends 17 August 2024 04:30 PM
  • All times are (UTC+03:00) Cairo
  • No Admission Charge

  Speakers

Ahmad Al-qaisi

Topic:

Lecturer

Ahmad Al-Qaisi

 

Ahmed Al-Qaisi is a dedicated third-year student specializing in Data Science and Artificial Intelligence at Al al-Bayt University. He holds a professional certification in Data Analytics and Business Intelligence from Google, demonstrating his expertise and commitment to the field.


Ahmed is currently employed as a Data Scientist at Binary Numbers Company. In this position, he leverages his analytical prowess to interpret data, develop predictive models, and implement solutions that enhance operational efficiency and business performance.


Beyond his academic and professional roles, Ahmed is the Chairman of this Computer Society at Al al-Bayt University. His leadership focuses on organizing events and workshops that promote technological advancement and collaborative learning, equipping members with the knowledge and skills necessary for success in the rapidly evolving tech landscape.

 

Email:

Address:Hawara, Al-Zaytouna Street, , Irbid, Jordan, Jordan




Agenda

Workshop Outline: Introduction to Kaggle and Jupyter for Data Science (Entry Level)

1. Kaggle (10 minutes)

  • Create Account

    • Step-by-step guide on setting up a Kaggle account.

  • Getting started with Kaggle Environment

    • Overview of Kaggle's features, competitions, and datasets.

2. Python-Jupyter (20 minutes)

  • Introduction to IPython

  • Install Anaconda on Windows

    • Instructions on downloading and installing Anaconda.

  • Getting started with Jupyter-Notebook Environment

    • Introduction to Jupyter Notebook interface and functionalities.

3. Data Science Processes Overview (10 minutes)

  • Types of Data Science Tasks

    • Explanation of various data science tasks (e.g., classification, regression, clustering).

  • Install Required Libraries Using PIP and Anaconda

    • Instructions on installing essential Python libraries: pandas, numpy, matplotlib, scikit-learn, etc.

4. Machine Learning Workflow (10 minutes)

  • Machine Learning Training

    • Introduction to building and training machine learning models.

  • Model Challenges

    • Discussion on common issues such as overfitting and underfitting.

  • Model Testing

    • Methods for evaluating model performance.

5. Practical Example and Simple Submission in a Kaggle Competition (10 minutes)

  • Submission

    • Guide on creating and submitting predictions to Kaggle.

6. Q&A Session

  • Open Discussion

    • Participants can ask questions and get answers related to the workshop topics.

 


Date: Saturday, 2024/8/17 5:00 PM
Workshop meeting link: https://meet.google.com/ift-xbgq-cfm?authuser=0