Which Machine Learning course is right for you?

Data School offers three Machine Learning courses:

• Course 1: Introduction to Machine Learning in Python with scikit-learn
• Course 2: Building an Effective Machine Learning Workflow with scikit-learn
• Course 3: Machine Learning with Text in Python

Keep reading to find out which course is right for you!

Course 1: Introduction to Machine Learning in Python with scikit-learn

This is the perfect course for you if:

• You're brand new to Machine Learning
• You have some Machine Learning experience, but you're new to scikit-learn
• You have some scikit-learn experience, but you don't yet feel comfortable using it

Topics covered:

• What is Machine Learning?
• Why use scikit-learn?
• Installing scikit-learn & Jupyter notebook
• Jupyter notebook basics
• Machine Learning terminology
• Loading a dataset using pandas
• Data preprocessing basics
• Model training & prediction
• Regression with Linear Regression
• Classification with KNN & Logistic Regression
• Model evaluation with train/test split & cross-validation
• Hyperparameter tuning with grid search & randomized search
• Regression & classification metrics

Length: 4 hours
Cost: FREE
Course includes: Jupyter notebooks with detailed lesson notes

>>> Click here to begin the course <<<

"This was a FANTASTIC video series. You are very easy to follow and this was the first resource I found that really walked through the Python language basics in terms of Machine Learning. Also, this really helped me understand the documentation on scikit-learn so that I can apply it to more complicated models." - Robin B.

Course 2: Building an Effective Machine Learning Workflow with scikit-learn

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 write efficient, readable, and reusable scikit-learn code that integrates well with pandas
• You want to properly handle common data issues such as missing values, text data, and categorical data
• You want to tune your entire workflow for maximum performance
• You want to take advantage of the latest scikit-learn features

Topics covered:

• Review of the basic Machine Learning workflow
• Encoding categorical features (OneHotEncoder, OrdinalEncoder)
• Encoding text data (CountVectorizer)
• Handling missing values (SimpleImputer, KNNImputer, IterativeImputer)
• Creating an efficient workflow for preprocessing and model building (Pipeline, ColumnTransformer)
• Tuning your workflow for maximum performance (GridSearchCV, RandomizedSearchCV)
• Avoiding data leakage
• Proper model evaluation
• Model persistence (pickle, joblib)
• Feature selection (SelectPercentile, SelectFromModel)
• Feature standardization (StandardScaler, MaxAbsScaler)
• Feature engineering using custom transformers (FunctionTransformer)

Length: 8 hours
Cost: $99/$129
Course includes: Jupyter notebooks with detailed lesson notes, lifetime course access, 30-day refund policy

>>> Click here to enroll in the course <<<

"I've already used the learnings from the course in a Machine Learning competition and got impressive results, while keeping the code clean and easy to understand. Also, I'm much more confident at tackling Machine Learning problems and I'm sure this will contribute a lot to my career." - João Vítor Franco, Data Scientist

Course 3: Machine Learning with Text in Python

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: $295
Course includes: Jupyter notebooks with detailed lesson notes, substantial practice projects with provided solutions, access to a private Slack team, 100+ resources to help you deepen your understanding of course topics, lifetime course access, 30-day refund policy

>>> Click here to enroll in the course <<<

"You won't find a better course to learn about NLP and Machine Learning in Python anywhere else! Kevin has a way of making difficult topics very accessible and understandable. I was able to quickly apply much of the theory and code regarding NLP and Machine Learning from this course to my own job." - Cliff Baker, Statistician

Frequently Asked Questions

What are the main differences between these courses?

Introduction to Machine Learning in Python 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.

Building an Effective Machine Learning Workflow with scikit-learn: The goal of this 8-hour course is to help you work extremely efficiently in scikit-learn with complex, real-world datasets. It teaches you how to move your entire Machine Learning workflow (including data preprocessing, feature engineering, and feature selection) into a scikit-learn pipeline in order to maximize model performance and eliminate data leakage. The course is completely up-to-date (released April 2020) and can be applied to any supervised learning problem using any scikit-learn model.

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 free course, Introduction to Machine Learning in Python with scikit-learn. After that, you will be ready to take my other two courses, and you can take them in either 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 three of my courses and have been quite satisfied!

I have another question...

Please email me and I'd be happy to help!