Python Machine Learning Bootcamp

Python Machine Learning Bootcamp

by Maximilian Schallwig
Released December 2022
Publisher(s): Packt Publishing
ISBN: 9781804619049

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Video description

In this course, we will cover many different types of machine learning aspects.

We will start by going through a sample machine learning project from an idea to developing a final working model. You will learn many important techniques around data preparation, cleaning, feature engineering, optimization and learning techniques, and much more.

Once we have gone through the whole machine learning project, we will then dive deeper into several different areas of machine learning, to better understand each task, and how each of the models we can use to solve these tasks work, and then also using each model and understanding how we can tune all the parameters we learned about in the theory components.

We will dive deeper into classification, regression, ensembles, dimensionality reduction, and unsupervised learning.

At the end of this course, you should have a solid foundation of machine learning knowledge. You will be able to build machine learning solutions to different types of problems you will come across and be ready to start applying machine learning on the job or in technical interviews.

What You Will Learn

  • Learn how to take an ML idea and flush it out into a fully functioning project
  • Learn the different types of ML approaches and the models within each section
  • Get a theoretical and intuitive understanding of how each model works
  • See the practical application and implementation for each model we cover
  • Learn how to optimize models
  • Learn the common pitfalls and how to overcome them

Audience

This course is designed for beginner Python programmers and data scientists who want to understand ML (Machine Learning) models in depth and be able to use them in practice. Basic Python knowledge is required and some previous experience with the Pandas and Matplotlib libraries will be helpful.

About The Author

Maximilian Schallwig: Maximilian Schallwig is a data engineer and a proficient Python programmer. He holds a bachelor’s degree in physics and a master’s degree in astrophysics. He has been working on data for over five years, first as a data scientist and then as a data engineer. He can talk endlessly about big data pipelines, data infrastructure, and his unwavering devotion to Python.

Even after two unsuccessful attempts in high school, he still decided to learn Python at the University. He cautiously stepped into the realm of data, beginning with a simple Google search for “what does a data scientist do”.

He was determined to pursue a career in data science to become a data engineer by learning about big data tools and infrastructure design to build scalable systems and pipelines. He enjoys sharing his programming skills with the rest of the world.

Table of contents

  1. Chapter 1 : Pre-Machine Learning Steps
    1. Course Introduction
    2. Setup and Installation
    3. Loading Datasets
    4. Data Format
    5. Train Test Splitting
    6. Stratified Splitting
    7. Data Preparation and Exploration
  2. Chapter 2 : Machine Learning Workflow
    1. Supervised Learning Introduction
    2. Classification Introduction
    3. Logistic Regression Theory
    4. Gradient Descent
    5. Types of Classification Problems
    6. Creating and Training a Binary Classifier
    7. Creating and Training a Multiclass Classifier
    8. Evaluating Classifiers Theory
    9. Precision and Recall Theory
    10. ROC, Confusion Matrix, and Support Theory
    11. MNIST Dataset Introduction
    12. Evaluating Classifiers Practical
    13. Validation Set
    14. Cross-Validation
    15. Hyperparameters
    16. Regularization Theory
    17. Generalization Error Sources
    18. Regularization Practical
    19. Grid and Randomized Search
    20. Handling Missing Values
    21. Feature Scaling Theory
    22. Feature Scaling Practical
    23. Text and Categorical Data
    24. Transformation Pipelines
    25. Custom Transformers
    26. Column Specific Pipelines
    27. Over and Undersampling
    28. Feature Importance
    29. Saving and Loading Models and Pipelines
    30. Post Prototyping
  3. Chapter 3 : Classification
    1. Multilabel Classification
    2. Polynomial Features
    3. SVM Theory
    4. SVM Classification Practical
    5. KNN Classification Theory
    6. KNN Classification Practical
    7. Decision Tree Classifier Theory
    8. Decision Tree Pruning
    9. Decision Tree Practical
    10. Random Forest Theory
    11. Random Forest Practical
    12. Naive Bayes Theory
    13. Naive Bayes Practical
    14. How to Choose a Model
  4. Chapter 4 : Regression
    1. Regression Introduction
    2. Linear Regression Practical
    3. Regularized Linear Regression Practical
    4. Boston Housing Introduction
    5. Polynomial Regression
    6. Regression Losses and Learning Rates
    7. SGD Regression
    8. KNN Regression Theory
    9. KNN Regression Practical
    10. SVM Regression Theory
    11. SVM Regression Practical
    12. Decision Tree Regression Theory
    13. Decision Tree and Random Forest Regression Practical
    14. Additional Regression Metrics
  5. Chapter 5 : Ensembles
    1. Ensembles Introduction
    2. Voting Ensembles Theory
    3. Voting Classification Practical
    4. Voting Regression Practical
    5. Bagging and Pasting Theory
    6. Bagging and Pasting Classification Practical
    7. Bagging and Pasting Regression Practical
    8. AdaBoost Theory
    9. AdaBoost Classification Practical
    10. AdaBoost Regression Practical
    11. Gradient Boosting Theory
    12. Gradient Boosting Classification Practical
    13. Gradient Boosting Regression Practical
    14. Stacking and Blending Theory
    15. Stacking Classifiers Practical
    16. Stacking Regression Practical
  6. Chapter 6 : Dimensionality Reduction
    1. Dimensionality Reduction Introduction
    2. PCA Theory
    3. PCA Practical
    4. NNMF Theory
    5. NNMF Practical
    6. Isomap Theory
    7. Isomap Practical
    8. LLE Theory
    9. LLE Practical
    10. t-SNE Theory
    11. t-SNE Practical
  7. Chapter 7 : Unsupervised Learning
    1. Unsupervised Learning Introduction
    2. KMeans Theory
    3. KMeans Practical
    4. Choosing Number of Clusters Theory
    5. Choosing Number of Clusters Practical
    6. DBSCAN Theory
    7. DBSCAN Practical
    8. Gaussian Mixture Theory
    9. Gaussian Mixture Practical
    10. Semi-Supervised Theory
    11. Semi-Supervised Practical

Product information

  • Title: Python Machine Learning Bootcamp
  • Author(s): Maximilian Schallwig
  • Release date: December 2022
  • Publisher(s): Packt Publishing
  • ISBN: 9781804619049