Data School offers four Machine Learning courses:
Course 1: Introduction to Machine Learning with scikit-learn
Course 2: 50 scikit-learn tips
Course 4: Machine Learning with Text in Python
Find out below which course is right for you!
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
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
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
Your courses and videos helped me a lot to further understand ML, which I believe is the reason I landed my dream job.
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.
I need to thank you for your videos that let me find my dream job. Thank you so much!!
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
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.
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.
Please email me at kevin@dataschool.io and I'd be happy to answer your question!
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! 🙌