Foundations in Python & Machine Learning

This course is given over the 4 month semester during the school year, with weekend-only and mid-week class times available. Typical weekly time commitment is 10-12 hours. 3-month accelerated cohorts available in summer months. All classes are conducted via Zoom with a live, synchronous instructor. 

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.)

As part of onboarding, your student student completes a brief readiness & placement review form. This review does not require prior programming experience and is not 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 that students are grouped roughly by age as well.

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