Applied Data Science: Techniques and Implementation
Chapter 1: Advanced Data Acquisition and Preparation
Connecting to Databases and Data Warehouses
Working with Web APIs for Data Retrieval
Techniques for Web Scraping Structured Data
Advanced Data Cleaning Methods
Strategies for Handling Missing Values
Data Transformation and Normalization Techniques
Merging and Joining Diverse Datasets
Hands-on: Data Acquisition and Wrangling Practice
Quiz for Chapter 1
Chapter 2: Practical Feature Engineering
Generating Features from Numerical Data
Encoding Categorical Variables Effectively
Creating Features from Text Data
Interaction Terms and Polynomial Features
Dimensionality Reduction with PCA
Selecting Features using Statistical Methods
Hands-on: Feature Creation and Selection
Quiz for Chapter 2
Chapter 3: Building and Tuning Predictive Models
Review of Common Supervised Learning Algorithms
Implementing Linear and Logistic Regression
Applying Tree-Based Models
Introduction to Gradient Boosting Machines
Hyperparameter Tuning using Grid Search and Randomized Search
Model Evaluation of Accuracy
Cross-Validation Strategies
Hands-on: Model Training and Hyperparameter Optimization
Quiz for Chapter 3
Chapter 4: Applied Unsupervised Learning
Understanding Clustering Concepts
Implementing K-Means Clustering
Applying DBSCAN for Density-Based Clustering
Introduction to Anomaly Detection Methods
Dimensionality Reduction for Visualization
Hands-on: Clustering and Anomaly Detection Practice
Quiz for Chapter 4
Chapter 5: Model Deployment Fundamentals
Saving and Loading Trained Models
Introduction to Model Serving Frameworks
Building a REST API for Model Prediction
Containerizing Applications with Docker
Basic Model Monitoring Concepts
Hands-on: Creating and Containerizing a Model API
Quiz for Chapter 5
Review of Common Supervised Learning Algorithms
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- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - A standard textbook that provides a statistical framework for many machine learning algorithms, including linear models, decision trees, and ensemble methods like bagging and boosting.
- Random Forests, Leo Breiman, 2001 Machine Learning, Vol. 45 (Springer) DOI: 10.1023/A:1010933404324 - The original academic paper introducing the Random Forest algorithm, detailing its construction and benefits in reducing overfitting.
- XGBoost: A Scalable Tree Boosting System, Tianqi Chen, Carlos Guestrin, 2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM) DOI: 10.1145/2939672.2939785 - This paper presents XGBoost, a popular and efficient implementation of gradient boosting with a focus on scalability and regularization.
- LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu, 2017 Advances in Neural Information Processing Systems 30 (NIPS 2017), Vol. 30 (Curran Associates, Inc.) - The research paper introducing LightGBM, highlighting its advancements in training speed and efficiency for gradient boosting models, especially on large datasets.