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scikit learn

Track :

Programming
Course Presenter :

Data School

Lessons no : 51

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What will you learn in this course?
  • Master machine learning classification, regression, clustering, and dimensionality reduction using scikit-learn in Python
  • Implement data preprocessing, dataset splitting, and feature engineering for robust machine learning models
  • Select appropriate algorithms and optimize model performance with cross-validation and grid search techniques
  • Build and evaluate machine learning models for real-world datasets in classification, regression, and clustering tasks
  • Create efficient workflows using pipelines to streamline complex machine learning processes
  • Apply model evaluation metrics to assess accuracy, precision, recall, and F1-score in predictive modeling
  • Perform hyperparameter tuning to enhance model performance and prevent overfitting in scikit-learn projects
  • Utilize model selection techniques to compare and choose the best algorithms for specific data science problems
  • Handle imbalanced datasets and perform feature scaling to improve model reliability and accuracy
  • Implement dimensionality reduction methods like PCA to simplify data and improve model efficiency
  • Develop end-to-end machine learning projects with scikit-learn, from data preprocessing to deployment
  • Gain practical skills to confidently apply scikit-learn in data science, analytics, and AI projects

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Lessons | 51
Use ColumnTransformer to apply different preprocessing to different columns
00:03:24

Use ColumnTransformer to apply different preprocessing to different columns
Seven ways to select columns using ColumnTransformer
00:04:07

Seven ways to select columns using ColumnTransformer
What is the difference between fit and transform
00:02:44

What is the difference between fit and transform
Use fit transform on training data but transform only on testing new data
00:04:20

Use fit transform on training data but transform only on testing new data
Four reasons to use scikit learn not pandas for ML preprocessing
00:04:04

Four reasons to use scikit learn not pandas for ML preprocessing
Encode categorical features using OneHotEncoder or OrdinalEncoder
00:05:16

Encode categorical features using OneHotEncoder or OrdinalEncoder
Handle unknown categories with OneHotEncoder by encoding them as zeros
00:03:17

Handle unknown categories with OneHotEncoder by encoding them as zeros
Use Pipeline to chain together multiple steps
00:03:12

Use Pipeline to chain together multiple steps
Add a missing indicator to encode missingness as a feature
00:03:02

Add a missing indicator to encode missingness as a feature
Set a random state to make your code reproducible
00:02:36

Set a random state to make your code reproducible
Impute missing values using KNNImputer or IterativeImputer
00:05:50

Impute missing values using KNNImputer or IterativeImputer
What is the difference between Pipeline and make pipeline
00:02:55

What is the difference between Pipeline and make pipeline
Examine the intermediate steps in a Pipeline
00:03:04

Examine the intermediate steps in a Pipeline
HistGradientBoostingClassifier natively supports missing values
00:02:52

HistGradientBoostingClassifier natively supports missing values
Three reasons not to use drop first with OneHotEncoder
00:04:37

Three reasons not to use drop first with OneHotEncoder
Use cross val score and GridSearchCV on a Pipeline
00:07:02

Use cross val score and GridSearchCV on a Pipeline
Try RandomizedSearchCV if GridSearchCV is taking too long
00:04:42

Try RandomizedSearchCV if GridSearchCV is taking too long
Display GridSearchCV or RandomizedSearchCV results in a DataFrame
00:02:36

Display GridSearchCV or RandomizedSearchCV results in a DataFrame
Important tuning parameters for LogisticRegression
00:04:40

Important tuning parameters for LogisticRegression
Plot a confusion matrix
00:03:04

Plot a confusion matrix
Compare multiple ROC curves in a single plot
00:01:52

Compare multiple ROC curves in a single plot
Use the correct methods for each type of Pipeline
00:01:46

Use the correct methods for each type of Pipeline
Display the intercept and coefficients for a linear model
00:01:21

Display the intercept and coefficients for a linear model
Visualize a decision tree two different ways
00:03:54

Visualize a decision tree two different ways
Prune a decision tree to avoid overfitting
00:01:35

Prune a decision tree to avoid overfitting
Use stratified sampling with train test split
00:04:26

Use stratified sampling with train test split
Two ways to impute missing values for a categorical feature
00:02:38

Two ways to impute missing values for a categorical feature
Save a model or Pipeline using joblib
00:01:45

Save a model or Pipeline using joblib
Vectorize two text columns in a ColumnTransformer
00:01:57

Vectorize two text columns in a ColumnTransformer
Four ways to examine the steps of a Pipeline
00:02:02

Four ways to examine the steps of a Pipeline
Shuffle your dataset when using cross val score
00:05:12

Shuffle your dataset when using cross val score
Use AUC to evaluate multiclass problems
00:03:40

Use AUC to evaluate multiclass problems
Use FunctionTransformer to convert functions into transformers
00:04:09

Use FunctionTransformer to convert functions into transformers
Add feature selection to a Pipeline
00:02:29

Add feature selection to a Pipeline
Don t use values when passing a pandas object to scikit learn
00:01:23

Don t use values when passing a pandas object to scikit learn
Most parameters should be passed as keyword arguments
00:01:56

Most parameters should be passed as keyword arguments
Create an interactive diagram of a Pipeline in Jupyter
00:02:08

Create an interactive diagram of a Pipeline in Jupyter
Get the feature names output by a ColumnTransformer
00:02:19

Get the feature names output by a ColumnTransformer
Load a toy dataset into a DataFrame
00:02:11

Load a toy dataset into a DataFrame
Estimators only print parameters that have been changed
00:01:36

Estimators only print parameters that have been changed
Show More Lessons

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scikit-learn, in this course we will learn about scikit-learn, one of the most powerful and widely used machine learning libraries in Python. You will explore how to build and evaluate machine learning models using tools for classification, regression, clustering, dimensionality reduction, and model selection. The course will guide you through essential steps like data preprocessing, splitting datasets, choosing the right algorithms, and fine-tuning model performance with cross-validation and grid search. You will also work with pipelines to streamline workflows and manage complex ML tasks more efficiently. With hands-on examples and real-world datasets, you will gain practical skills to implement models that make accurate predictions. Whether you're a beginner or looking to solidify your ML foundation, this course will help you master machine learning with scikit-learn and apply it confidently in data science projects. Basic Python and data handling knowledge are recommended. Data School