Scikit-Learn Using Python Course
Scikit-learn or sklearn is a Python library that can be used for solving various machine learning problems. In the scikit-learn using Python course, we have discussed how to perform data preprocessing, feature selection, model selection, and model evaluation using the Python sklearn library. We have also discussed how to solve regression and classification problems using the sklearn Python library. This course is good for beginners who want to learn sklearn for machine learning.
Details of Section 1
1.1 What are estimators, transformers and predictors in sklearn?
1.2 How to perform mean or median imputation using the sklearn Python library?
1.3 How to perform label encoding using sklearn?
1.4 How to perform ordinal encoding using sklearn?
1.5 How to perform One-Hot Encoding using sklearn?
1.6 How to use Column Transformer in sklearn?
1.7 How to perform min-max scaling using sklearn?
1.8 How to perform Maximum Absolute Scaling using sklearn?
1.9 How to use Robust Scaler in sklearn?
1.10 Vector Unit Length Scaling using sklearn
1.11 How to standardize data using sklearn?
1.12 Normalization using sklearn
1.13 Binarization using sklearn
1.2 How to perform mean or median imputation using the sklearn Python library?
1.3 How to perform label encoding using sklearn?
1.4 How to perform ordinal encoding using sklearn?
1.5 How to perform One-Hot Encoding using sklearn?
1.6 How to use Column Transformer in sklearn?
1.7 How to perform min-max scaling using sklearn?
1.8 How to perform Maximum Absolute Scaling using sklearn?
1.9 How to use Robust Scaler in sklearn?
1.10 Vector Unit Length Scaling using sklearn
1.11 How to standardize data using sklearn?
1.12 Normalization using sklearn
1.13 Binarization using sklearn
Details of Section 2
2.1 Feature Selection Based on Variance in Machine Learning
2.2 Feature Selection Based On Correlation In Machine Learning
2.3 Recursive Feature Elimination (RFE) using sklearn
2.4 Feature selection based on model performance using sklearn
2.5 Select K best features using SelectKBest in sklearn
2.6 Feature Importances using Extra Trees in sklearn
2.2 Feature Selection Based On Correlation In Machine Learning
2.3 Recursive Feature Elimination (RFE) using sklearn
2.4 Feature selection based on model performance using sklearn
2.5 Select K best features using SelectKBest in sklearn
2.6 Feature Importances using Extra Trees in sklearn
Details of Section 3
3.1 What is Train Test Split and how to do it using the sklearn Python library?
3.2 Repeated Random Train-Test Split using sklearn in Python
3.3 K-Fold Cross-Validation using sklearn in Python
3.4 Repeated K-Fold Cross-Validation using Python sklearn
3.5 Repeated Stratified K-Fold Cross-Validation using sklearn in Python
3.6 Leave One Out Cross Validation using sklearn in Python
3.8 Stratified K-Fold Cross-Validation using sklearn in Python
3.2 Repeated Random Train-Test Split using sklearn in Python
3.3 K-Fold Cross-Validation using sklearn in Python
3.4 Repeated K-Fold Cross-Validation using Python sklearn
3.5 Repeated Stratified K-Fold Cross-Validation using sklearn in Python
3.6 Leave One Out Cross Validation using sklearn in Python
3.8 Stratified K-Fold Cross-Validation using sklearn in Python
Details of Section 4
4.1 Calculating Mean Absolute Error, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) using Python
4.2 What is R-squared and how to calculate it in sklearn?
4.3 Calculating Confusion Matrix using Python
4.4 Calculating Accuracy Score in Machine Learning using Python
4.5 Calculating Precision and Recall in Machine Learning using Python
4.6 Calculating F1 score in machine learning using Python
4.7 Calculate The Logistic Loss Score using sklearn in Python
4.8 Calculate AUC: Area Under The ROC Curve Using sklearn in Python
4.9 How to calculate the classification report using sklearn in Python?
4.2 What is R-squared and how to calculate it in sklearn?
4.3 Calculating Confusion Matrix using Python
4.4 Calculating Accuracy Score in Machine Learning using Python
4.5 Calculating Precision and Recall in Machine Learning using Python
4.6 Calculating F1 score in machine learning using Python
4.7 Calculate The Logistic Loss Score using sklearn in Python
4.8 Calculate AUC: Area Under The ROC Curve Using sklearn in Python
4.9 How to calculate the classification report using sklearn in Python?
Details of Section 5
5.1 Simple Linear Regression using Python
5.2 Multiple Linear Regression using Python
5.3 Polynomial Regression using Python
5.4 How to use pipeline for polynomial regression in sklearn?
5.5 Ridge Regression using Python
5.6 Lasso Regression using Python
5.7 ElasticNet Regression using Python
5.8 K-Nearest Neighbors (KNN) Regressor using sklearn
5.9 Regression Trees using the sklearn Python library
5.10 Random Forest Regressor using sklearn
5.11 How to solve regression problems using Support Vector Machines (SVM) in sklearn?
5.12 Extra Trees Regressor using sklearn
5.2 Multiple Linear Regression using Python
5.3 Polynomial Regression using Python
5.4 How to use pipeline for polynomial regression in sklearn?
5.5 Ridge Regression using Python
5.6 Lasso Regression using Python
5.7 ElasticNet Regression using Python
5.8 K-Nearest Neighbors (KNN) Regressor using sklearn
5.9 Regression Trees using the sklearn Python library
5.10 Random Forest Regressor using sklearn
5.11 How to solve regression problems using Support Vector Machines (SVM) in sklearn?
5.12 Extra Trees Regressor using sklearn
Details of Section 6
6.1 Logistic Regression using the sklearn Python library
6.2 K-Nearest Neighbors (KNN) Classifier using sklearn
6.3 Linear Discriminant Analysis using sklearn in Python
6.4 Classification Trees using sklearn
6.5 How to plot Decision Trees using sklearn?
6.6 Random Forest Classifier using sklearn in Python
6.7 Gaussian Naive Bayes Classifier using sklearn
6.8 How to solve classification problems using Support Vector Machines (SVM) in sklearn?
6.9 Bagged Decision Trees Classifier using sklearn in Python
6.10 AdaBoost Classifier using sklearn in Python
6.11 Compare the performance of different machine learning models
6.12 Use pipeline for data preparation and modeling in sklearn
6.2 K-Nearest Neighbors (KNN) Classifier using sklearn
6.3 Linear Discriminant Analysis using sklearn in Python
6.4 Classification Trees using sklearn
6.5 How to plot Decision Trees using sklearn?
6.6 Random Forest Classifier using sklearn in Python
6.7 Gaussian Naive Bayes Classifier using sklearn
6.8 How to solve classification problems using Support Vector Machines (SVM) in sklearn?
6.9 Bagged Decision Trees Classifier using sklearn in Python
6.10 AdaBoost Classifier using sklearn in Python
6.11 Compare the performance of different machine learning models
6.12 Use pipeline for data preparation and modeling in sklearn
Details of Section 7
7.1 What is K-Means Clustering and how does it work?
7.2 K-Means Clustering using sklearn
7.3 How to find out the optimal value of k in K-Means Clustering?
7.4 How to measure clustering performance using silhouette score?
7.5 How to measure clustering performance using completeness score?
7.6 How to measure clustering performance using homogeneity score?
7.7 How to measure clustering performance using V-measure?
7.8 How to measure clustering performance using contingency matrix?
7.2 K-Means Clustering using sklearn
7.3 How to find out the optimal value of k in K-Means Clustering?
7.4 How to measure clustering performance using silhouette score?
7.5 How to measure clustering performance using completeness score?
7.6 How to measure clustering performance using homogeneity score?
7.7 How to measure clustering performance using V-measure?
7.8 How to measure clustering performance using contingency matrix?
Ready to get started?
All the courses are fully accessible to premium members.