Machine Learning Introduction with Python
Machine learning is an exciting and in-demand aspect of the artificial intelligence world. It enables systems to learn and improve without direct instructions from users. In this path, we cover a few essentials for getting started in machine learning.
SKILL PATH: MACHINE LEARNING INTRO WITH PYTHON
Here's what you'll learn to do.
Even if you’ve never written a line of code in Python, you’ll catch on quickly, and we’ll have you writing code in minutes.
- Machine learning basics
- Avoiding common mistakes
- Evaluating model performance
- Common techniques like k-nearest neighbours, k-means clustering, and decision trees
- Mathematics for machine learning, including calculus and linear algebra
- Basics of linear and logistic regression
Course Structure: Machine Learning Intro with Python
Learn the basics of machine learning and explore how to avoid common pitfalls in machine learning.
Explore the key ideas from calculus for understanding how mathematical functions behave and prepare for intermediate machine learning techniques.
Learn how to make predictions using the linear regression machine learning model, two different ways of fitting a linear regression model, and how to select, clean, and transform features.
Understand the types of relationships in the data decision trees can represent, build a decision tree implementation from the ground up, and learn how to use the random forests machine learning model.
It's not just what you learn,
but how you learn it.
Learn by writing and validating code,
not by watching videos.
Challenge yourself with dozens of
Revisit what you've learned anytime you need a refresher.
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