Foundations in Python & Machine Learning course

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.

This course is equal to a bit more than a full semester of school instruction.

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 

This course is given over the 4 month semester during the school year, with weekend-only and mid-week class times available. Typically 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. 

Tuition is $3996 for the full semester course

Why take this course? 

1. If your student plans to be competitive—at the highest levels—in any area of STEM, including engineering, science, or medicine, you will be expected to know machine learning (ML); we're not referring to Chat-LLM-based Generative-AI, but rather predictive machine learning as the newly foundational computational tool that it has become in STEM. This course is carefully designed to get students started in programming and ML with a quick track to deep learning models that can be used in high school and even middle school research projects in all STEM domains, such as EE/Physics, CS/ML, MatSci, MCB/Biochem, Biomed Eng., Applied Math, Mech Eng, Chem Eng, and more.
2.  Using ML and Deep Learning (DL), although not simple, is feasible to learn much earlier than advanced math and is therefore the most accessible way to start STEM research very early at an age where laboratory access is unlikely (such a middle school). Especially in physical sciences and engineering ML/DL methods are becoming integrated into undergrad and graduate curricula as mandatory skills similar to solving differential equations or signals analysis. Getting started as early as 5-6th grade is possible and this can give you literally a 4-8 year head start  in your ability to do early research in STEM. 
3. This course is an excellent introduction to Python programming which is the most prevalent STEM programming language and a critical skill in these domains, having become the ubiquitous language of  AI/ML/DL and STEM coding.
4. As a separate topic, if you do wish to learn how to develop Gen-AI-based LLM applications (not explicitly taught in this course), Python mastery is the starting point and after this course you can take our Gen-AI / LLM Programming course, which is for those interested in a CS software application development track, rather than a STEM research track (of course you could also do both).
5. This course is for serious students interested in learning this material for their future career track and to enable early research opportunities. It is not intended for students whose only goal is for resume building for college applications.