Pragnya R.
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Online Course
free
Machine Learning with K-Nearest Neighbors
with
Hristina Hristova
Master K-Nearest Neighbors using Python’s scikit-learn library: from theoretical foundations to practical applications
3968 students already have enrolled
Skill level:
Intermediate
Duration:
2 hours
-
Lessons (1 hour)
-
Practice exams (25 minutes)
- Lessons (1 hour)
- Practice exams (25 minutes)
CPE credits:
2
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited:
certificate
What You Learn
- Fully grasp the inner workings and application of the KNN algorithm.
- Get acquainted with different distance metrics.
- Generate random datasets for practice and experimentation.
- Understand the pros and cons of the KNN algorithm.
- Plan, execute, and deliver a complete ML project independently.
Topics & tools
theorypythonmachine learningprogrammingmachine and deep learning
Your instructor
Head of Data Content at 365 Data Science | Theoretical Physicist | Educator in Physics, Mathematics, and Programming
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Theoretical and Mathematical Physics
Course OVERVIEW
Description
CPE Credits: 2
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
Practical knowledge of various machine learning algorithms is essential for machine learning enthusiasts and experts alike. In this course, we focus extensively on one of the most intuitive and easy-to-implement ML algorithms out there – K-nearest neighbors, or KNN for short. Step by step, we will first lay the foundations and expand your mathematical toolbox. Then, you will progress to coding and using Python’s scikit-learn library to solve a randomly generated classification problem. Finally, you will apply KNN to a couple of regression tasks. In other words – you will learn all the subtleties that should be considered when applying the KNN algorithm in your future practice.
Prerequisites
- Python (version 3.8 or later), Streamlit library, OpenAI API key, and a code editor or IDE (e.g., VS Code or Jupyter Notebook)
- Intermediate Python skills are required.
- Familiarity with basic statistics and linear algebra is helpful but not mandatory.
Advanced preparation
Curriculum
17 lessons
3 exercises
2 exams
- 2. Setting up the Environment5 MinIn advance of the hands-on part of the course, this section guides you through the installation process of relevant Python packages.
- 3. KNN Classifier – Practical Example37 MinTo apply your skills in practice, you will first learn how to generate a random set of points, distribute them into 3 classes, and place them on the coordinate system. We will then use this dataset to train and test a KNN classification algorithm with the help of Python’s scikit-learn library. We will look at some edge cases that can arise during the classification process and discover how to handle them. Next, we will guide you through the process of building the so-called decision regions, which are a great way of visualizing the performance of your model. Finally, we will find out how to choose the best model parameters using a technique called ‘grid search’.Random Dataset: Generating the Dataset FreeRandom Dataset: Visualizing the Dataset FreeRandom Dataset: Classification FreeRandom Dataset: How to Break a Tie FreeRandom Dataset: Decision Regions FreeRandom Dataset: Choosing the Best K-value FreeRandom Dataset: Grid Search FreeRandom Dataset: Model Performance Free
- 4. KNN Regressor19 MinContinuing the practical part of the course, we will dive into solving regression tasks using the K-Nearest Neighbors method. Similar to what we did in the previous section, we will tackle this problem by generating 2 random datasets. One would represent a linear problem, while the other would be non-linear. We will apply a linear (parametric) model and a KNN (non-parametric model) on both datasets and argue which one performs better.
- 5. Pros and Cons of the KNN Algorithm7 MinIn this final section of the course, the pros and cons of the KNN algorithm are discussed at length. We will study this method’s limitations, together with its strong sides.
- 6. Course exam30 Min
Free preview
1.1 What does the course cover?
7 min
1.2 Motivation
2 min
1.4 Math Prerequisites: Distance Metrics
4 min
2.1 Setting up the Environment
1 min
2.2 Installing the Relevant Packages
4 min
3.1 Random Dataset: Generating the Dataset
3 min
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of our students recommend
365 Data Science.
9 in 10
of our graduates landed a new AI & data job
after enrollment
9 in 10
people walk away career-ready
with practical data and AI skills.
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