Description

Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.

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. All the resources for this course are available at: https://github.com/PacktPublishing/Python-Machine-Learning-Bootcamp

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.

Approach

This course focuses on covering first the theoretical background of how the model works so that you can build a proper intuition around its behavior. Then we will have the practical component, where we will implement the machine learning model and use it on actual data. In this way, you gain both hands-on as well as a solid theoretical foundation of how the different machine learning models work, and you will be able to use this knowledge to better choose and fix models, depending on the situation.

Key Features

Gain technical skills to use machine learning on the job or for your own projects * Dive deep into classification, regression, ensembles, dimensionality reduction, and unsupervised learning * Get ready to start applying machine learning on the job or in technical interviews

Github Repo

https://github.com/PacktPublishing/Python-Machine-Learning-Bootcamp

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.

Course Outline

1. Pre-Machine Learning Steps

1. Course Introduction

Welcome to the course. In this video, we will get introduced to the course objective learning goals.

2. Setup and Installation

In this video, you will learn how to download, install, and set up our coding environment

3. Loading Datasets

In this video, you will learn how to load datasets in Jupyter Notebook.

4. Data Format

In this video, you will learn about data format.

5. Train Test Splitting

In this video, you will learn how to split train test.

6. Stratified Splitting

In this video, you will learn how to split out data.

7. Data Preparation and Exploration

In this video, you will learn about data preparation and exploration.

2. Machine Learning Workflow

1. Supervised Learning Introduction

In this video, we will get introduced to supervised learning.

2. Classification Introduction

In this video, we will get introduced to classification.

3. Logistic Regression Theory

In this video, we will understand what logistic regression is.

4. Gradient Descent

In this video, you will learn about gradient descent.

5. Types of Classification Problems

In this video, you will learn about types of classification problems.

6. Creating and Training a Binary Classifier

In this video, you will learn how to create and train a binary classifier.

7. Creating and Training a Multiclass Classifier

In this video, you will learn how to create and train a multiclass classifier.

8. Evaluating Classifiers Theory

In this video, we will understand what evaluating classifiers is.

9. Precision and Recall Theory

In this video, we will understand what precision and recall are.

10. ROC, Confusion Matrix, and Support Theory

In this video, we will understand what ROC, confusion matrix, and support are.

11. MNIST Dataset Introduction

In this video, we will get introduced to the MNIST dataset.

12. Evaluating Classifiers Practical

In this demo video, we will practice evaluating classifiers with the help of an example.

13. Validation Set

In this video, you will learn about validation set.

14. Cross-Validation

In this video, you will learn about cross-validation.

15. Hyperparameters

In this video, you will learn about hyperparameters.

16. Regularization Theory

In this video, we will understand what regularization is.

17. Generalization Error Sources

In this video, you will learn about generalization error sources.

18. Regularization Practical

In this demo video, we will practice regularization with the help of an example.

19. Grid and Randomized Search

In this video, you will learn about grid and randomized search.

20. Handling Missing Values

In this video, you will learn how to handle missing values.

21. Feature Scaling Theory

In this video, we will understand what feature scaling is.

22. Feature Scaling Practical

In this demo video, we will practice feature scaling with the help of an example.

23. Text and Categorical Data

In this video, you will learn about text and categorical data.

24. Transformation Pipelines

In this video, you will learn about transformation pipelines.

25. Custom Transformers

In this video, you will learn about custom transformers.

26. Column Specific Pipelines

In this video, you will learn about column specific pipelines.

27. Over and Undersampling

In this video, we will understand oversampling and undersampling.

28. Feature Importance

In this video, we will understand about feature importance.

29. Saving and Loading Models and Pipelines

In this video, you will learn how to save and load models and pipelines.

30. Post Prototyping

In this video, we will cover post prototyping.

3. Classification

1. Multilabel Classification

In this video, we will cover multilabel classification.

2. Polynomial Features

In this video, we will cover polynomial features.

3. SVM Theory

In this video, we will understand what Support Vector Machine (SVM) is, how it works, and more.

4. SVM Classification Practical

In this demo video, we will practice SVM classification with the help of an example.

5. KNN Classification Theory

In this video, we will understand KNN classification.

6. KNN Classification Practical

In this demo video, we will practice KNN classification with the help of an example.

7. Decision Tree Classifier Theory

In this video, we will understand decision tree classifier.

8. Decision Tree Pruning

In this video, we will cover decision tree pruning.

9. Decision Tree Practical

In this demo video, we will practice decision tree with the help of an example.

10. Random Forest Theory

In this video, we will understand what random forest is.

11. Random Forest Practical

In this demo video, we will practice random forest with the help of an example.

12. Naive Bayes Theory

In this video, we will understand what Naive Bayes is.

13. Naive Bayes Practical

In this demo video, we will practice Naive Bayes with the help of an example.

14. How to Choose a Model

In this video, you will learn how to choose a model.

4. Regression

1. Regression Introduction

In this video, we will get introduced to regression.

2. Linear Regression Practical

In this demo video, we will practice linear regression with the help of an example.

3. Regularized Linear Regression Practical

In this demo video, we will practice regularized linear regression with the help of an example.

4. Boston Housing Introduction

In this video, we will explore the Boston housing dataset.

5. Polynomial Regression

In this video, we will cover polynomial regression.

6. Regression Losses and Learning Rates

In this video, we will cover regression losses and learning rates.

7. SGD Regression

In this video, we will cover SGD regression.

8. KNN Regression Theory

In this video, we will understand what KNN regression is.

9. KNN Regression Practical

In this demo video, we will practice KNN regression with the help of an example.

10. SVM Regression Theory

In this video, we will understand what SVM regression is.

11. SVM Regression Practical

In this demo video, we will practice SVM regression with the help of an example.

12. Decision Tree Regression Theory

In this video, we will understand what decision tree regression is.

13. Decision Tree and Random Forest Regression Practical

In this demo video, we will practice decision tree and random forest regression with the help of an example.

14. Additional Regression Metrics

In this video, we will cover additional regression metrics.

5. Ensembles

1. Ensembles Introduction

In this video, we will get introduced to Ensembles.

2. Voting Ensembles Theory

In this video, we will understand what voting Ensembles is.

3. Voting Classification Practical

In this demo video, we will practice voting classification with the help of an example.

4. Voting Regression Practical

In this demo video, we will practice voting regression with the help of an example.

5. Bagging and Pasting Theory

In this video, we will understand what bagging and pasting are.

6. Bagging and Pasting Classification Practical

In this demo video, we will practice bagging and pasting classification with the help of an example.

7. Bagging and Pasting Regression Practical

In this demo video, we will practice bagging and pasting regression with the help of an example.

8. AdaBoost Theory

In this video, we will understand what AdaBoost is.

9. AdaBoost Classification Practical

In this demo video, we will practice AdaBoost classification with the help of an example.

10. AdaBoost Regression Practical

In this demo video, we will practice AdaBoost regression with the help of an example.

11. Gradient Boosting Theory

In this video, we will understand what gradient boosting is.

12. Gradient Boosting Classification Practical

In this demo video, you will learn how to implement gradient boosting classification with an example.

13. Gradient Boosting Regression Practical

In this demo video, we will practice gradient boosting regression with the help of an example.

14. Stacking and Blending Theory

In this video, we will understand what stacking and blending are.

15. Stacking Classifiers Practical

In this demo video, we will practice stacking classifiers with help of an example.

16. Stacking Regression Practical

In this demo video, we will practice stacking regression with help of an example.

6. Dimensionality Reduction

1. Dimensionality Reduction Introduction

In this video, we will get introduced to dimensionality reduction.

2. PCA Theory

In this video, we will understand what PCA is.

3. PCA Practical

In this demo video, we will practice PCA with the help of an example.

4. NNMF Theory

In this video, we will understand what NNMF is.

5. NNMF Practical

In this demo video, we will practice NNMF with the help of an example.

6. Isomap Theory

In this video, we will understand what Isomap is.

7. Isomap Practical

In this demo video, we will practice Isomap with the help of an example.

8. LLE Theory

In this video, we will understand what LLE is.

9. LLE Practical

In this demo video, we will practice LLE with the help of an example.

10. t-SNE Theory

In this video, we will understand what t-SNE is.

11. t-SNE Practical

In this demo video, we will practice t-SNE with the help of an example.

7. Unsupervised Learning

1. Unsupervised Learning Introduction

In this video, we will get introduced to unsupervised learning.

2. KMeans Theory

In this video, we will understand what KMeans is.

3. KMeans Practical

In this demo video, we will practice KMeans with the help of an example.

4. Choosing Number of Clusters Theory

In this video, we will understand how to choose a number of clusters.

5. Choosing Number of Clusters Practical

In this demo video, we will practice choosing a number of clusters with the help of an example.

6. DBSCAN Theory

In this video, we will understand what DBSCAN is.

7. DBSCAN Practical

In this demo video, we will practice DBSCAN with the help of an example.

8. Gaussian Mixture Theory

In this video, we will understand what Gaussian Mixture is.

9. Gaussian Mixture Practical

In this demo video, we will practice Gaussian Mixture with the help of an example.

10. Semi-Supervised Theory

In this video, we will understand semi-supervised.

11. Semi-Supervised Practical

In this demo video, we will practice semi-supervised with the help of an example.

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Python Machine Learning Bootcamp

By Packt

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