Machine Learning: A complete Guide
Machine Learning: A complete Guide
About the Book
ABOUT THE GUIDE
This guide is designed for learners who want to deepen their understanding of Supervised Machine Learning. It offers a comprehensive journey, starting from the basics of machine learning, diving into various supervised learning models, and expanding into advanced techniques like Ensemble Learning, Hyperparameter Tuning, and Cross-Validation. Along the way, readers will gain insights into key evaluation metrics, the importance of data preprocessing, and practical implementation strategies.
The content is structured to cater to both beginners and intermediate learners, gradually building complexity. Each section is supplemented with examples, visualizations, and Python code to reinforce learning.
PREREQUISITES
To get the most out of this guide, it is recommended that readers have:
- A basic understanding of Python programming
- Familiarity with Python libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn
- Knowledge of fundamental concepts in Linear Algebra and Statistics
- Exposure to basic data analysis and visualization techniques
For those new to these topics, introductory resources are provided in early sections to ensure a smooth learning curve.
GUIDE OBJECTIVE
By the end of this guide, readers will:
- Understand the principles of Supervised Machine Learning
- Be able to implement various models like Linear Regression, Support Vector Machines, Decision Trees, and more
- Gain hands-on experience with model evaluation, hyperparameter tuning, and cross-validation techniques
- Learn how to build pipelines to streamline their machine learning workflow
This guide is aimed at preparing readers to apply these concepts to real-world data problems, advancing their proficiency in Machine Learning and Data Science.
About the Author
Table of Contents
Table of Contents
- What is Machine Learning?
- What is Traditional Programming
- Key Difference Between Traditional Programming and Machine Learning
- Importance of Machine Learning
- Types of Machine Learning
- Supervised Machine Learning
- Introduction
- Types of Supervised Learning Problems
- Regression
- Classification
- Key Concepts in Supervised Machine Learning
- Applications of Supervised Machine Learning
- Data Preprocessing
- Supervised ML Models
- Models
- Other Important Concepts
- Linear Regression
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Linear Regression
- Ridge Regression
- Lasso Regression
- Logistic Regression
- Evaluation Metrics
- Evaluation Metrics for Regression
- Evaluation Metrics for Classification
- Support Vector Machine (SVM)
- SVM Regressor
- SVM Classifier
- Parameters of a Model
- Parameters used in SVM
- K-Nearest Neighbors (KNN)
- KNN Regressor
- KNN Classifier
- Distances used in KNN
- Parameters used in KNN
- Decision Tree
- Important Terms
- Decision Tree Regressor
- Decision Tree Classifier
- Splitting Criterion
- Ensemble Algorithms
- Bagging
- Boosting
- Stacking
- Blending
- Bagging
- Random Forest
- Random Forest Regressor
- Random Forest Classifier
- Random Forest
- Boosting
- Boosting Algorithms
- Adaboost
- Adaboost Regressor
- Adaboost Classifier
- XGBoost
- XGBoost Regressor
- XGBoost Classifier
- CatBoost
- CatBoost Regressor
- CatBoost Classifier
- Hyperparameter Tuning and Cross Validation
- Techniques for Hyperparameter Tuning
- Grid Search
- Random Search
- Bayesian Optimization
- Cross Validation
- K-Fold Cross-Validation
- Leave-One-Out Cross-Validation (LOOCV)
- Stratified K-Fold Cross-Validation
- Time Series Cross-Validation
- Group K-Fold Cross-Validation
- Techniques for Hyperparameter Tuning
- Pipeline
- Components
- Creating and Executing Pipeline in Python
- Advantages of Using Pipeline
- Probability
- Introduction
- Rules of Probability
- Bayes' Theorem
- Application of Probability
- Naive Bayes Algorithm
- Types of Naive Bayes
- Conclusion
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