Supervised Machine Learning in Python

Supervised Machine Learning in Python, available at $64.99, has an average rating of 4.15, with 79 lectures, based on 25 reviews, and has 218 subscribers.

You will learn about Regression and classification models Linear models Decision trees Naive Bayes k-nearest neighbors Support Vector Machines Neural networks Random Forest Gradient Boosting XGBoost Voting Stacking Performance metrics (RMSE, MAPE, Accuracy, Precision, ROC Curve) Feature importance SHAP Recursive Feature Elimination Hyperparameter tuning Cross-validation This course is ideal for individuals who are Python developers or Data Scientists or Computer engineers or Researchers or Students It is particularly useful for Python developers or Data Scientists or Computer engineers or Researchers or Students.

Enroll now: Supervised Machine Learning in Python

Summary

Title: Supervised Machine Learning in Python

Price: $64.99

Average Rating: 4.15

Number of Lectures: 79

Number of Published Lectures: 79

Number of Curriculum Items: 79

Number of Published Curriculum Objects: 79

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will Learn

Regression and classification models

Linear models

Decision trees

Naive Bayes

k-nearest neighbors

Support Vector Machines

Neural networks

Random Forest

Gradient Boosting

XGBoost

Voting

Stacking

Performance metrics (RMSE, MAPE, Accuracy, Precision, ROC Curve)

Feature importance

SHAP

Recursive Feature Elimination

Hyperparameter tuning

Cross-validation

Who Should Attend

Python developers

Data Scientists

Computer engineers

Researchers

Students

Target Audiences

Python developers

Data Scientists

Computer engineers

Researchers

Students

In this practicalcourse, we are going to focus on supervised machine learningand how to apply it in Python programming language.

Supervised machine learning is a branch of artificial intelligence whose goal is to create predictive modelsstarting from a dataset. With the proper optimization of the models, it is possible to create mathematical representationsof our data in order to extract the informationthat is hidden inside our database and use it for making inferencesand predictions.

A very powerful use of supervised machine learning is the calculation of feature importance, which makes us better understand the information behind data and allows us to reduce the dimensionalityof our problem considering only the relevant information, discarding all the useless variables. A common approach for calculating feature importance is the SHAPtechnique.

Finally, the proper optimization of a model is possible using some hyperparameter tuningtechniques that make use of cross-validation.

With this course, you are going to learn:

1. What supervised machine learning is

2. What overfitting and underfitting are and how to avoid them

3. The difference between regression and classification models

4. Linear models

   1. Linear regression

   2. Lasso regression

   3. Ridge regression

   4. Elastic Net regression

   5. Logistic regression

5. Decision trees

6. Naive Bayes

7. K-nearest neighbors

8. Support Vector Machines

   1. Linear SVM

   2. Non-linear SVM

9. Feedforward neural networks

10. Ensemble models

    1. Bias-variance tradeoff

    2. Bagging and Random Forest

    3. Boosting and Gradient Boosting

    4. Voting

    5. Stacking

11. Performance metrics

    1. Regression

       1. Root Mean Squared Error

       2. Mean Absolute Error

       3. Mean Absolute Percentage Error

    2. Classification

       1. Confusion matrix

       2. Accuracy and balanced accuracy

       3. Precision

       4. Recall

       5. ROC Curve and the area under it

       6. Multi-class metrics

12. Feature importance

    1. How to calculate feature importance according to a model

    2. SHAP technique for calculating feature importance according to every model

    3. Recursive Feature Elimination for dimensionality reduction

13. Hyperparameter tuning

    1. k-fold cross-validation

    2. Grid search

    3. Random search

All the lessons of this course start with a brief introduction and end with a practical example in Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the Jupyter notebooks are downloadable.

Course Curriculum

Chapter 1: Introduction to supervised machine learning

Lecture 1: Introduction to the course

Lecture 2: What is supervised machine learning?

Lecture 3: Regression and classification models

Lecture 4: Overfitting and underfitting

Chapter 2: The tools used in this course

Lecture 1: Required Python packages

Lecture 2: Jupyter notebook

Lecture 3: Sklearn API

Chapter 3: Linear models

Lecture 1: Introduction to Linear Regression

Lecture 2: Linear regression in Python

Lecture 3: Introduction to Ridge Regression

Lecture 4: Ridge regression in Python

Lecture 5: Introduction to Lasso Regression

Lecture 6: Lasso regression in Python

Lecture 7: Introduction to Elastic Net Regression

Lecture 8: Elastic Net Regression in Python

Lecture 9: Introduction to Logistic Regression for classification

Lecture 10: Logistic regression in Python

Chapter 4: Decision trees

Lecture 1: Introduction to decision trees

Lecture 2: Decision trees in Python

Chapter 5: K-nearest neighbors

Lecture 1: Introduction to KNN

Lecture 2: KNN in Python

Chapter 6: Naive Bayes

Lecture 1: Introduction to Naive Bayes

Lecture 2: Categorical Naive Bayes in Python

Lecture 3: Bernoulli Naive Bayes in Python

Lecture 4: Gaussian Naive Bayes in Python

Chapter 7: Support Vector Machines

Lecture 1: Introduction to SVM

Lecture 2: Linear SVM in Python

Lecture 3: Non-linear SVM in Python

Chapter 8: Neural Networks

Lecture 1: Introduction to Neural Networks

Lecture 2: Neural Networks in Python

Chapter 9: Introduction to ensemble models

Lecture 1: Ensemble models and bias-variance tradeoff

Chapter 10: Ensemble models: bagging

Lecture 1: Introduction to bagging

Lecture 2: Bagging in Python

Lecture 3: Introduction to Random Forest

Lecture 4: Random Forest in Python

Lecture 5: Introduction to Extremely Randomized Trees

Lecture 6: Extremely Randomized Trees in Python

Chapter 11: Ensemble models: boosting

Lecture 1: Introduction to boosting

Lecture 2: Boosting in Python

Lecture 3: Introduction to Gradient Boosting

Lecture 4: Gradient Boosting in Python

Lecture 5: XGBoost in Python

Chapter 12: Ensemble models: voting

Lecture 1: Introduction to voting

Lecture 2: Voting in Python

Chapter 13: Ensemble models: stacking

Lecture 1: Introduction to stacking

Lecture 2: Stacking in Python

Chapter 14: Performance evaluation

Lecture 1: Regression performance metrics

Lecture 2: Regression performance metrics in Python

Lecture 3: Pairplot in Python

Lecture 4: Binary classification performance metrics

Lecture 5: Binary classification performance metrics in Python

Lecture 6: Introduction to ROC curve

Lecture 7: ROC curve in Python

Lecture 8: Multi-class classification performance metrics

Lecture 9: Multi-class classification performance metrics in Python

Lecture 10: When to use classification performance metrics

Chapter 15: Cross-Validation and hyperparameter tuning

Lecture 1: Introduction to k-fold cross-validation

Lecture 2: k-fold cross-validation in Python

Lecture 3: The need for hyperparameter tuning

Lecture 4: Introduction to grid search

Lecture 5: Grid search in Python

Lecture 6: Introduction to Random Search

Lecture 7: Random Search in Python

Chapter 16: Feature importance and model interpretation

Lecture 1: What is feature importance?

Lecture 2: Models that calculate feature importance in Python

Lecture 3: Introduction to SHAP

Lecture 4: Using SHAP with tree-based models in Python

Lecture 5: Using SHAP with every model in Python

Chapter 17: Recursive Feature Elimination

Lecture 1: Introduction to RFE

Lecture 2: RFE in Python

Chapter 18: Practical examples in Python

Lecture 1: A complete pipeline: model selection and hyperparameter tuning

Lecture 2: Feature selection with Lasso

Lecture 3: Dimensionality reduction with RFE

Lecture 4: How to choose the right scaler

Chapter 19: Persisting our model

Lecture 1: Pickle library

Chapter 20: Practical approaches

Lecture 1: The curse of dimensionality

Lecture 2: The importance of pre-processing

Lecture 3: The importance of the right features against the model

Lecture 4: Interpretability of a model

Instructors

Gianluca Malato
Your Data Teacher

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 3 votes

4 stars: 8 votes

5 stars: 13 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!