What you’ll learn
- Students will understand the intuition behind some of the most widely used machine learning techniques.
- Students will understand how to interpret output from machine learning algorithms.
- Students will understand the appropriateness of machine learning tools.
- Students will understand the intuition behind Python codes for machine learning.
Are you new to machine learning? Does the math seem overwhelming? If yes, this course is for you!
In this course, you’ll start from the basics. You’ll download Python to your local machine and connect it with VS Code. You’ll learn foundational Python skills – how to open .csv files, explore and select data, and apply basic data functions.
From there, you’ll dive into powerful machine learning techniques without diving into the math. You’ll begin with supervised learning for classification, covering:
- Decision Trees
- K-Nearest Neighbors
- Random Forests
- Regression Trees
Then, you’ll move to more advanced models like:
- Ridge, Lasso, and ElasticNet Regression
- Support Vector Machines
Along the way, you’ll intuitively understand concepts like gradient descent and cost functions – no tough math, just insight.
You’ll also get:
- Practice quizzes
- Downloadable videos
- Jupyter notebooks to code along
In the final section, you’ll explore unsupervised learning, including:
- Clustering
- Market Basket Analysis
- Principal Component Analysis (PCA)
These topics involve heavy math, but here, you’ll just focus on the core intuition and practical use. You’ll learn which technique to use and when, without memorizing formulas.
With just 2.5 hours of content, this course is designed to be concise, giving you maximum intuition, hands-on practice, and real insights in the least amount of time.
Who this course is for:
- This course is suited for people who do not have a computer science background – ideally business students who want to gather an intuition and just enough knowledge to use machine learning.
