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.

DURATION
41 hours
COURSES
9
PROJECTS
6
DIFFICULTY
Intermediate
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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.

Calculus For Machine Learning

Explore the key ideas from calculus for understanding how mathematical functions behave and prepare for intermediate machine learning techniques.

Linear Algebra For Machine Learning

Explore the key ideas from linear algebra for understanding linear systems 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.

Learn intermediate linear regression and logistic regression concepts and how to prevent overfitting, a common problem in machine learning.

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.

Kaggle Fundamentals

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.

It's not just what you'll learn
but how
you'll apply it.

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Retain Data Skills

Retain

Learn by writing and validating code, not by watching videos.

Reinforce Data Learning

Reinforce

Challenge yourself
with dozens of practice problems.

Realize your goals

Realize

Apply your new skills to projects with complete confidence.

Connect with a community of data learners

Relate

Get support and feedback from the Dataquest community.

Join over 1 million data learners!

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It’s great! So many options; R, SQL, Python. And they take you on guided projects where you understand the reasoning behind the code and are able to apply it to future projects.

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I've been learning data science with Dataquest — and it's a game changer! No boring videos or fill-in-the-blank exercises. Try it for yourself!

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If you want to get into a career in data, choose DQ. The education they offer would stretch you and prepare you for real life work.

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