Data Science with Python
Data Science with Python: From Data Manipulation to Machine Learning
by Aria B
English | December 17, 2024 | ASIN: B0DQV54QNW | 146 pages | PDF | 31 Mb
by Aria B
English | December 17, 2024 | ASIN: B0DQV54QNW | 146 pages | PDF | 31 Mb
"Data Science with Python: From Data Manipulation to Machine Learning" is a comprehensive guide designed for aspiring data scientists and professionals looking to enhance their data science skills using Python. This ebook covers the entire data science workflow, from data manipulation and visualization to building and deploying machine learning models. Whether you're a beginner or an experienced practitioner, this guide provides valuable insights and practical examples to help you master data science with Python.
Chapter 1: Introduction to Data Science and Python
Understanding Data Science
Importance of Python in Data Science
Setting Up the Python Environment
Essential Python Libraries for Data Science
Chapter 2: Data Manipulation with Pandas
Introduction to Pandas
Loading and Inspecting Data
Data Cleaning and Preprocessing
Data Transformation and Aggregation
Chapter 3: Data Visualization with Matplotlib and Seaborn
Introduction to Data Visualization
Creating Basic Plots with Matplotlib
Advanced Visualizations with Seaborn
Customizing and Styling Plots
Chapter 4: Exploratory Data Analysis (EDA)
Introduction to EDA
Descriptive Statistics
Identifying Patterns and Outliers
Visualizing Relationships and Distributions
Chapter 5: Introduction to Machine Learning
Understanding Machine Learning
Supervised vs. Unsupervised Learning
Key Machine Learning Algorithms
Setting Up Scikit-Learn for Machine Learning
Chapter 6: Supervised Learning with Scikit-Learn
Regression Algorithms
Classification Algorithms
Model Evaluation and Selection
Hyperparameter Tuning
Chapter 7: Unsupervised Learning with Scikit-Learn
Clustering Algorithms
Dimensionality Reduction Techniques
Anomaly Detection
Practical Examples and Applications
Chapter 8: Advanced Machine Learning Techniques
Ensemble Methods
Gradient Boosting and XGBoost
Neural Networks and Deep Learning
Time Series Analysis
Chapter 9: Model Deployment and Optimization
Saving and Loading Models
Deploying Machine Learning Models with Flask
Model Optimization and Performance Tuning
Monitoring and Updating Models in Production
Understanding Data Science
Importance of Python in Data Science
Setting Up the Python Environment
Essential Python Libraries for Data Science
Chapter 2: Data Manipulation with Pandas
Introduction to Pandas
Loading and Inspecting Data
Data Cleaning and Preprocessing
Data Transformation and Aggregation
Chapter 3: Data Visualization with Matplotlib and Seaborn
Introduction to Data Visualization
Creating Basic Plots with Matplotlib
Advanced Visualizations with Seaborn
Customizing and Styling Plots
Chapter 4: Exploratory Data Analysis (EDA)
Introduction to EDA
Descriptive Statistics
Identifying Patterns and Outliers
Visualizing Relationships and Distributions
Chapter 5: Introduction to Machine Learning
Understanding Machine Learning
Supervised vs. Unsupervised Learning
Key Machine Learning Algorithms
Setting Up Scikit-Learn for Machine Learning
Chapter 6: Supervised Learning with Scikit-Learn
Regression Algorithms
Classification Algorithms
Model Evaluation and Selection
Hyperparameter Tuning
Chapter 7: Unsupervised Learning with Scikit-Learn
Clustering Algorithms
Dimensionality Reduction Techniques
Anomaly Detection
Practical Examples and Applications
Chapter 8: Advanced Machine Learning Techniques
Ensemble Methods
Gradient Boosting and XGBoost
Neural Networks and Deep Learning
Time Series Analysis
Chapter 9: Model Deployment and Optimization
Saving and Loading Models
Deploying Machine Learning Models with Flask
Model Optimization and Performance Tuning
Monitoring and Updating Models in Production