This mini bootcamp is a great opportunity to start a data science career. We are offering a FREE mini bootcamp to prepare you data scientist positions.
This mini bootcamp will teach you Python basics and machine learning algorithms. During this mini bootcamp, you will explore a hands-on experience that ignites your enthusiasm, gain the confidence to learn data science.
Your learning experience will be supported by office hours and a Slack channel, where you can ask your questions and get help when you got stuck.
- Python and Jupyter Notebook Basics
- What is Data Science?
- Python Foundations
- Setup the Jupyter Notebook
- Introduction to Python with Jupyter Notebook
- Linear Regression
- Logistic Regression
- Evaluating Model Fit
- Data PreProcessing with Python
- Feature extraction with Python
- Support Vector Machines
Start learning the Machine Learning with our convenient schedule!
The 4 weeks – 8 hours of schedule is as follows:
May 1 – 8 – 15- 22
Saturdays, from 9:00 am to 11:00 am
Machine Learning Mini Bootcamp is now available online. Anyone who wants to attend this mini bootcamp can join online live webinars where the course content will be taught. Online sessions will be distributed through zoom conferences. Students will have access to the screen of the instructor and be able to ask questions through chat. You may attend this mini bootcamp no matter where you are.
Early Bird: $ (between , – , )
After you finish filling your application form, the website will direct you to the payment page. There, you can select available payment options.
If you’re not satisfied with the course you may cancel your application.
Siobhán Mcnamara is a data scientist working at Agari, a cybersecurity company in the Bay Area. Originally Siobhán studied Psychology & Economics and had an interest in the intersection of the two. This has lent itself to her current role, they analyze behaviors and use that for identity verification, that is to determine if someone online is who they claim to be. Earlier in her career, Siobhán had a number of research roles in economics and later on learned to code so that she could apply that skillset to a data science position.