Ratings & reviews

4.47 ratings

Marcello S.

18 days ago

I think people learn quicker if they understand what is the problem to be solved or the challenge. For instance, you could firtly propose a project, of course nobody would be expe... Show more

Joannes M.

23 days ago

I liked it as introduction!

Saul A.

25 days ago

Described in great detail and very easy to follow and understand.

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info

Complete all chapters to get certificate

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Machine Learning Concepts

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Learn the Machine Learning concepts and the ML project workflow.

What is ML

Types of Machine Learning

Training Set

Types of Data

Machine Learning Workflow

Preprocessing Data with Scikit-learn

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Preprocessing is probably the most important stage of an ML project. This chapter covers the preprocessing steps needed for almost any dataset.

Scikit-learn Concepts

Getting Familiar with Dataset

Dealing with Missing Values

Imputing Challenge

OrdinalEncoder

One-Hot Encoder

LabelEncoder

Encoding Challenge

Why Scale the Data?

StandardScaler, MinMaxScaler, MaxAbsScaler

Scaling Challenge

Preprocessing Summary

Pipelines

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A pipeline is a neat way to combine all the preprocessing steps as well as a model. Pipelines make it much easier to train and use a model.

What is Pipeline

ColumnTransformer

Pipeline

Creating a Pipeline Challenge

Final Estimator

Final Estimator Challenge

Modeling

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Modeling is the most fun stage of an ML project. Let's learn to build, fine-tune and evaluate the model!

Models

KNeighborsClassifier

Evaluating a Model. Train-Test split.

Cross-Validation

Cross-Validation Challenge

GridSearchCV

The Flaw of GridSearchCV

RandomizedSearchCV. Hyperparameter Tuning Challenge

Modeling Summary

Putting It All Together