Skill Path: 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 the essential programming and statistics skills required for getting started in machine learning. From there we learn common machine learning techniques, including k-nearest neighbors, k-means clustering, and decision trees.

COURSES
18
PROJECTS
14
DIFFICULTY
Beginner
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Learn Machine Learning with Python

Here's what you'll learn to do.

Dataquest Skill Paths teach you job-ready skills that can be immediately applied to your current or future data roles and projects.

  • How to program in Python, including how to clean and visualize data
  • How to make predictions using statistics and machine learning
  • The basics of building a machine learning project from start to finish
  • Common machine learning techniques, including k-nearest neighbors, k-means clustering, and decision trees


Course Structure:

Machine Learning Intro with Python

Python for Data Science: Fundamentals

Learn the basics of Python programming and data science.

Python for Data Science: Intermediate

Learn important tools for your Python data science toolbox.

Pandas and NumPy Fundamentals

Learn how to analyze data using the pandas and NumPy libraries.

Data Visualization Fundamentals

Learn the fundamentals of data visualization in Python by striking a good balance between graph interpretation (statistics) and tooling (Matplotlib and Seaborn).

Data Cleaning and Analysis

Learn data cleaning and analysis with pandas, how to combine data sets, and how to clean string and handle missing data.

Statistics Fundamentals

Learn about sampling, variables and distributions.

Statistics Intermediate: Averages and Variability

Learn how to summarize distributions using the mean, the median, and the mode. Learn to measure variability using variance or standard deviation, and how to locate and compare values using z-scores.

Probability: Fundamentals

Learn the fundamentals of probability theory using Python.

Machine Learning Fundamentals

Learn the basics of machine learning and explore how to avoid common pitfalls in machine learning.

Conditional Probability

Learn about conditional probability, Bayes' theorem, and Naive Bayes.

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.

Linear Regression For 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.

Machine Learning in Python: Intermediate

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

Decision Trees

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.

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 submission 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.

97% of learners recommend Dataquest's teaching method.

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!

Rated 4.85/5 on Switchup + Voted Best Bootcamp Winner of 2021

David Rodrigues @davidorodrigues

I left datacamp for @dataquestio. 10x better I wish I had started Dataquest 2 years ago.

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Lysdel Tellez @LysdelTellez

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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Cesar Jr @ceeezthedata

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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ChukwuSom @legally_6lack 

Studying with @dataquestio has been the best decision I have made this year.

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Ijeoma Benson @ijeybenson

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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Matthew Madden @mattmadden

I've found @dataquestio super helpful in levelling up my data skills. #100daysofcode #DataScience 

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We Learn Better Together

When you join Dataquest, you join a community of committed learners.


Get fast help with your technical questions 


Share your projects and get supportive feedback


Chat one-on-one with developing data experts


Get career tips from career moderators


Achieve your data career goals with confidence.

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