Announcing The MGTA AI Academy

Google+ Pinterest LinkedIn Tumblr +

Provided by MGTA

Intro to Python for ML/AI

Machine-Learning/AI is the newest most exciting sub-field of computer science that is used today to recommend movies and music based on previous selections, to allow a Tesla to self-drive and steer you home autonomously, and to allow an iRobot vacuum cleaner to self-map your home for future precise cleaning.

This Introduction to Python for Machine-Learning/AI week-long course prepares high school students to learn the Python programming language from scratch, but also includes an overview of data science and machine-learning/AI using Python. Moreover, learners are given a range of simple to increasingly difficult programming projects in class through an introduction of a range of open-source Python tools, modules, and libraries used in data analysis and machine-learning. This course culminates in a fun and challenging project to build a classification machine-learning model to analyze movie reviews.

Dates for this class:

Week 1 June 29 – July 3 (Ages 14-18) Sign up here.

Week 4 July 20 – July 24 (Ages 14-18) Sign up here.

Machine Learning/AI with Python

Machine-Learning/AI with Python is the second course of a two weeks series that introduces high school students to machine-learning programming with Python.This course assumes students have a solid grasp of intermediate-to-advanced Python, as dataset analysis and machine-learning projects are introduced in the first two days.

As all machine-learning models must be trained, trimmed, and corrected using clean and complete datasets, so this course begins with an introduction to data science, data classification, data analytics, and dataset compilation. A series of machine-learning/AI algorithms and techniques such as Random Forest, SVM, SVP, Naïve Bayes, nearest neighbor variants, and TensorFlow will be presented through discussions and projects. Moreover, a description and related projects will be assigned using a series of related open-source modules and libraries, such as Scikit-learn, NumPy, Matplotlib, Pandas, Pygame, Keras, NLTK, BeautifulSoup, and VADER. This course culminates in a capstone project to build and customize a neural-network machine-learning powered arcade space shooter.

Dates for this class:

Week 5 July 27 – July 31 (Ages 14-18) Sign up here.



Comments are closed.