Which Machine Learning course is right for you?

Data School offers four Machine Learning courses:

Find out below which course is right for you!

  • Free

Introduction to Machine Learning with scikit-learn

Learn the fundamentals of Machine Learning in Python with this FREE 4-hour course!

This is the perfect course for you if:

  • You're brand new to Machine Learning

  • You have Machine Learning experience, but you're new to scikit-learn

  • You've used scikit-learn, but you don't really know if you're doing things the "right" way

Topics covered:

  • What is Machine Learning?

  • Why use scikit-learn?

  • Installing scikit-learn & Jupyter notebook

  • Jupyter notebook basics

  • Machine Learning terminology

  • Machine Learning workflow

  • Loading a dataset using pandas

  • Preprocessing categorical features

  • Model training & prediction

  • Regression with Linear Regression

  • Classification with KNN & Logistic Regression

  • Model evaluation with train/test split & cross-validation

  • Metrics for regression & classification

  • Hyperparameter tuning with grid search & randomized search

Length: 4 hours
Cost: FREE
Course includes: Jupyter notebooks with detailed lesson notes, interactive quizzes, 80+ resources to help you deepen your understanding of course topics, certificate of completion, lifetime course access

Rafael K.

This is the best introduction to Machine Learning I have *EVER* seen. Thank you for fueling my confidence that I can master this subject!

Mo Daghlas

The way you break down steps and deliver new information is fantastic! It doesn’t feel rushed at all, and you take the time to explain all the new terminology, steps and methodology concisely.

Diogo G.

A M A Z I N G ! In one day I've learned what I need to get into Machine Learning in Python and scikit-learn.

  • Free

50 scikit-learn tips

Sharpen your Machine Learning skills with this FREE 3-hour course!

This is the perfect course for you if:

  • You've taken my introductory ML course and you're ready to go deeper into scikit-learn

  • You want to work more efficiently using scikit-learn's latest features

  • You want to learn best practices for Machine Learning code

  • You learn best through short, focused lessons

Topics covered:

  • How to build, evaluate, and tune a Pipeline

  • Two easy ways to visualize a decision tree

  • How to benefit from missing values using a "missing indicator"

  • How to plot an ROC curve in one line of code

  • How to speed up a grid search

  • How to add feature selection to a Pipeline

  • Why you should use scikit-learn (not pandas) for preprocessing

  • How to create an interactive diagram of a Pipeline

  • How to save your best Pipeline for future predictions

  • Why dropping a level when one-hot encoding is usually a bad idea

  • How to create custom transformers for feature engineering

  • Why you should use stratified sampling with train/test split

  • How to build and tune an ensemble of models

  • Why you should try ordinal encoding with tree-based models

  • And much, much more!

Length: 3 hours
Cost: FREE
Course includes: Jupyter notebooks, certificate of completion, lifetime course access

Neil Dias (ML Engineer)

Your new videos are great! I find them as excellent and concise refreshers on ML implementation topics.

Beltran Rovira (Master's student)

Thanks so much for your videos! They have helped me to optimize the Machine Learning workflow and to understand what’s going on underneath the hood!

Lautaro Cisterna (Data Scientist)

This course is a great guide and resource, where you can come back and check how something was done in a very clear and easy way.

  • $299

Master Machine Learning with scikit-learn

Get ready for your dream job in Machine Learning!

This is the perfect course for you if:

  • You want to set yourself apart from the competition when looking for a job in Machine Learning

  • You want to be more confident when tackling new Machine Learning problems

  • You want to write better, faster, and more readable scikit-learn code

  • You want to master the wide range of topics that are critical to effective Machine Learning (but are rarely covered by other courses)

Topics covered:

  • Review of the basic Machine Learning workflow

  • Encoding categorical features

  • Encoding text data

  • Handling missing values

  • Preparing complex datasets

  • Creating an efficient workflow for preprocessing and model building

  • Tuning your workflow for maximum performance

  • Avoiding data leakage

  • Proper model evaluation

  • Automatic feature selection

  • Feature standardization

  • Feature engineering using custom transformers

  • Linear and non-linear models

  • Model ensembling

  • Model persistence

  • Handling high-cardinality categorical features

  • Handling class imbalance

Length: 7.5 hours
Cost: $299
Course includes: Jupyter notebooks with detailed lesson notes, interactive quizzes, certificate of completion, lifetime course access

Maggie Tang (Machine Learning Engineer)

Your courses and videos helped me a lot to further understand ML, which I believe is the reason I landed my dream job.

Reuven Lerner (Python trainer)

If you think that Machine Learning is too complex for you to learn, I cannot recommend Kevin's courses enough. He'll give you the confidence you need, along with the knowledge you want.

Arrigo Coen Coria (Data Scientist)

I need to thank you for your videos that let me find my dream job. Thank you so much!!

  • $299

Machine Learning with Text in Python

Solve text-based data science problems using Machine Learning and Natural Language Processing!

This is the perfect course for you if:

  • You've taken my introductory ML course and you're ready to apply what you learned

  • You want to solve supervised Machine Learning problems using text-based data

  • You want to learn Natural Language Processing techniques that you can adapt to your own datasets

  • You want to work through data science problems from start to finish

  • You learn best through extensive practice

Topics covered:

  • What is Natural Language Processing (NLP)?

  • NLP terminology

  • Feature extraction from unstructured text

  • Modifications to basic tokenization

  • Document summarization

  • Sentiment analysis

  • Advanced text processing with regular expressions

  • Data exploration & visualization with pandas

  • Feature engineering with pandas

  • Classification with Naive Bayes & Logistic Regression

  • Proper model evaluation

  • Classification metrics

  • Multi-class classification

  • Pipeline tuning with grid search & randomized search

  • Model ensembling & stacking

Length: 14 hours
Cost: $299
Course includes: Jupyter notebooks with detailed lesson notes, substantial practice projects with provided solutions, 100+ resources to help you deepen your understanding of course topics, certificate of completion, lifetime course access, 30-day refund policy

Jose Navarro (Machine Learning Engineer)

I used to work as a software developer and your course helped me to move on. I now have a job in the NLP/Machine Learning field which I am more passionate about.

Ryan Cranfill (Data Scientist)

The course was a perfect introduction to Machine Learning with text, and I was able to apply topics covered during the first week to my work. Kevin does a great job of breaking down complex topics and providing a practical, real-world context for them.

Jeff Weakley (Creative Director)

If I had paid a lot more for this class, it would still have been worth it. After taking a lot of other online courses, I feel like I'm finally getting valuable skills, tools and info I can use and financially benefit from.

FAQs

What are the main differences between these courses?

Introduction to Machine Learning with scikit-learn: The goal of this 4-hour course is to introduce you to the basic Machine Learning process and how to implement it using scikit-learn. It teaches you the most important concepts in-depth so that you will be prepared to execute simple Machine Learning projects with clean datasets.

50 scikit-learn tips: The goal of this 3-hour course is to help you write better scikit-learn code. Through 50 short lessons, it teaches you how to work more efficiently using scikit-learn's latest features and apply Machine Learning best practices.

Master Machine Learning with scikit-learn: The goal of this 7.5-hour course is to prepare you for your dream job in Machine Learning. It teaches you a comprehensive framework for properly handling any supervised Machine Learning problem using scikit-learn. By the end of the course, you'll have a deep understanding of the wide range of topics that are critical to effective Machine Learning.

Machine Learning with Text in Python: The goal of this 14-hour course is to help you build excellent Machine Learning models when the input data is text. It teaches you a variety of Natural Language Processing and text processing techniques that support this goal. It walks you through data science problems from start to finish, making use of scikit-learn, pandas, seaborn, TextBlob, and other Python libraries. It includes substantial practice projects with provided solutions, as well as 100+ resources for digging deeper into the course topics.

In what order should I take these courses?

You should start with my beginner course, Introduction to Machine Learning with scikit-learn. After that, you will be ready to take my other three courses, and you can take them in any order.

How much overlap is there between these courses?

There is a small overlap of material (5-10%) between these courses. Many students have taken all of my courses and have been quite satisfied!

Do you offer any discounts?

Yes! I offer Purchasing Power Parity discounts (also known as location-based discounts) for all of my paid courses. If you're located in one of the 160+ qualifying countries, you should automatically see a discount code at the top of this page.

I also offer student discounts and hardship-based discounts, regardless of where you live. Please email me at kevin@dataschool.io and I'd be happy to send you the appropriate discount code.

I have another question...

Please email me at kevin@dataschool.io and I'd be happy to answer your question!

👋 Welcome to Data School!

My name is Kevin, and I've taught Data Science in Python to over a million students.

My courses explain data science topics in a clear, thorough, and step-by-step manner.

I'd love to teach you, regardless of your educational background or professional experience.

Thanks for joining me! 🙌