Foundations in Python & Machine Learning

This course is given over the 4 month Fall or Winter/Spring semester during the school year, with weekend-only and mid-week class times available. Typical weekly time commitment is 10-12 hours (class + homework). Summer cohorts are 3 months. All classes are conducted via Zoom with a live, synchronous instructor. We now have added "rolling start" cohorts that begin mid-semester and mid-summer, to accomodate a smaller number of students who miss the semester and summer start date deadlines.

After registration and tuition payment you will be given a choice of classes having different scheduled times with different weekly schedules (i.e. weekend, mid-week, morning versus afternoon, etc.). You will also be given a choice of the next available start dates - either the next semester/summer, or rolling start dates.

As part of onboarding, your student student completes a brief readiness & placement review form. This course does not require prior programming experience (although many have prior experience), and this review is not a test or competitive. To support effective learning, students are placed into cohorts with peers of comparable prior background experience. Please be reassured, all cohorts cover the same core curriculum meeting the same academic standards. Cohorts ensure that instruction moves at an appropriate pace, while maintaining a rigorous academic environment for all groups of students, and allow students to be grouped by similar age as well. Students have successfully taken this course at grade levels from 4th grade to high school. We believe this is the material needed to lead into serious modern scientific & ML programming mastery and don't dilute it or adjust it based on age level. 

This course is equal to a bit more than a full semester of school instruction, approximately 75 hours of direct live class instruction. 

Course description & overview:

 A rigorous foundation in Python programming and machine learning, emphasizing conceptual rigor and computational reasoning. Designed as preparation for more advanced deep learning and research-oriented STEM courses and programs. No prior programming experience required, however this course assumes intellectual curiosity, sustained serious effort, and a willingness to engage with challenging material.

What your student will learn:

• Complete introduction to procedural programming and including object-oriented programming in Python. Emphasis on data structures and algorithms needed later for deep learning 
• Course material introduces machine learning in an organic way and builds up concepts needed later in more advanced ML/Deep Learning courses, such as:
  • Feature vectors, leading to 'Tensors' as key data structures
  • Precision, Recall, Sensitivity, Specificity, F1-score, and all that 
  • Standard ML algorithms
  • Image processing libraries and methods 
  • Deep dive into what is behind convolutional filters, so that CNNs will not be a black box in later deep learning courses 
  • Supervised versus unsupervised learning (leading to PCA and dimensionality reduction)
  • Concepts of clustering, and important visualization techniques like tSNE and UMAP