Skill Path: Machine Learning Intermediate 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. This intermediate path covers essential machine learning techniques, including k-nearest neighbors, k-means clustering, and decision trees.
Learn Machine Learning Intermediate with Python
Here's what you'll learn to do.
- The basics of machine learning, including avoiding common pitfalls and evaluating model performance
- Common machine learning techniques, including k-nearest neighbors, k-means clustering, decision trees, and neural networks
- Foundational mathematics for machine learning, including linear and logistic regression, calculus, and linear algebra
- The basics of building a machine learning project from start to finish
- How to select the best algorithm and tune your model for the best performance
Course Structure: Machine Learning Intermediate with Python
Machine Learning Fundamentals
Learn the basics of machine learning and explore how to avoid common pitfalls in machine learning.
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 random forests machine learning model.
Deep Learning Fundamentals
Learn how neural networks are represented, how neural networks capture nonlinearity in the data, and how adding hidden layers can provide improved model performance.
Machine Learning Project
Walk through a machine learning project start to finish.
Build a simple machine learning model and make your first Kaggle sublesson and create new features and select the best-performing features to improve your score.