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UTMIST - Machine Learning Fundamentals

BeginnerCourse

The Machine Learning Fundamentals (MLF) Program is a beginner-friendly initiative offering foundational technical and theoretical knowledge in ML/AI. Through structured workshops covering topics like linear regression and neural networks, and a mentored group project, you will build practical skills and gain the confidence needed to start your journey in this field. It's designed to equip you with a solid understanding of core concepts, preparing you for future ML/AI endeavors.

Language

  • English

Topic

  • Machine Learning

Skills You Will Learn

  • Machine Learning, PyTorch, Python, Artificial Intelligence

Offered By

  • IND

Estimated Effort

  • 20 hours

Platform

  • SkillsNetwork

Last Update

  • October 11, 2025
About this Course
Embark on your Machine Learning (ML) and Artificial Intelligence (AI) journey with the Machine Learning Fundamentals (MLF) Program! This course is your essential starting block for navigating the exciting, high-demand field of ML/AI.

Why is this course important? The power of machine learning is transforming every industry, from tech and finance to healthcare and entertainment. Having a solid, fundamental understanding of ML concepts is no longer just a specialization—it's becoming a crucial skill for innovators of tomorrow. This program is important because it provides a beginner-friendly, guided path through the foundational technical and theoretical knowledge that most self-learners struggle to piece together.

By taking this course, you will gain a kickstart and the confidence to explore this field on your own. You'll gain a solid understanding of core concepts, build practical skills through workshops and a guided project, and have access to valuable mentorship from the University of Toronto Machine Intelligence Student Team (UTMIST) academics. Upon completion, you won't just know about ML; you'll be equipped to tackle your own ML/AI initiatives and have a clear vision for your next steps in this domain.


Course Syllabus

Upon completing this program, you will be able to:
  • Define and explain the core concepts of foundational machine learning, including linear and logistic regression.
  • Implement and interpret fundamental algorithms like Gradient Descent and various loss functions.
  • Describe the architecture of basic neural networks (Multi-Layer Perceptrons) and the role of activation functions.
  • Apply key concepts of the ML Development Cycle, such as identifying and mitigating issues like overfitting, underfitting, and managing bias/variance trade-offs.
  • Analyze practical problems and apply specialized ML techniques like K-means clustering and Decision Trees.
  • Collaborate on a group project, applying your learned skills in a structured, professional environment.

Module 1: Foundational ML Concepts  
Topic 1: Introduction to Basic Concepts for ML (Linear Regression, Logistic Regression, Loss Functions, Gradient Descent) |
Topic 2: The ML Development Cycle (Overfitting/Underfitting, Bias/Variance)

Module 2: Introduction to Neural Networks and Specialized Topics 
Topic 1: Intro to Neural Networks (Activation Functions, Multi-Layer Perceptrons (MLP), Neural Network Implementation (e.g., in DL libraries or NumPy, Backpropagation) 
Topic 2: "Special" Topics (Decision Trees, Unsupervised Learning - Anomaly Detection, K-means Clustering, Recommender Systems - Collaborative Filtering, Content-Based Filtering) 

General Information

This program is designed to be completed over a single semester, with a recommended timeline of approximately 7 to 8 weeks of focused workshops and learning, followed by the guided group project phase. The learning experience is highly engaging, utilizing technical workshops/lectures for concept delivery.

Assessment Style: Your progress and learning will be evaluated through the successful completion and presentation of a guided group project. This project is structured similarly to UTMIST's own initiatives, giving you valuable real-world experience. Mentors will provide continuous support and feedback throughout the learning process and project development.

Recommended Skills Prior to Taking this Course

This program is specifically designed for Beginners—individuals who are new to the field of ML/AI or are seeking mentorship to develop foundational skills.
  • Programming Language: A basic familiarity with Python is highly recommended, as it is the primary language used in the ML field.
  • Mathematics: A general understanding of high-school level algebra is helpful for grasping concepts like linear functions and gradients.
  • Technology: No advanced technology setup is required. The program will leverage environments that often have necessary tools (like Docker) pre-installed. The learning platform works best with current versions of modern web browsers, including Chrome, Edge, Firefox, or Safari.

Instructors

UTMIST Academics

Machine Learning Educator

Founded in 2017, UTMIST (University of Toronto Machine Intelligence Student Team) is North America’s largest undergrad machine learning-focused community. As a student-led club, UTMIST exists to inspire, educate, and elevate the next generation of engineers, researchers and leaders in artificial intelligence and machine learning. With 4000+ Instagram followers, 3000+ LinkedIn followers, 2700+ community members on Discord, 150+ active volunteers and executives every year, as well as 500+ developers, UTMIST is one the most influential student societies within the STEM community. We bridge theory and application, hosting various workshops, conferences, and hackathons each year to provide enriching opportunities to students. Signature initiatives like the EigenAI conference, the GenAI Genesis Hackathon and the AI-squared Tournament have brought together thousands of attendees from across the UofT campuses and beyond. We also produce in-house academic content and spearhead design teams to collaborate on innovative projects, including partnerships with industry and contributions to papers published in top-tier conferences. UTMIST encourages students to lead their own research ideas and teams, fostering a culture of innovation where members can take initiative, develop original projects, and make meaningful contributions to the field. Our community is interdisciplinary, with machine learning applications spanning finance, medicine, chemistry, and more. Past UTMIST members have gone on to pursue graduate studies at world-renowned institutions, launch startups, and intern or work full-time at leading tech companies, including Google, Microsoft, Amazon, TikTok, Intel, Tesla and Cohere. We’re a space where beginners are welcomed, experts are challenged, and bold ideas become reality. Whether you're training your first model or building state-of-the-art systems, UTMIST is where you’ll find the people, tools, and inspiration to go further.

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