Karamagi Robert. Machine Learning in Python

Independently published, 2021. — 674 p. — ISBN B08V4VZSCH.Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Contents
Data Mining
History of Data Mining
Data Mining Techniques
Data Mining Implementation Process
Data Mining Architecture
Data Mining Tools
Data Mining vs. Data Warehousing
Data Mining vs. Big Data
Data Mining vs. Machine Learning
Data Mining Applications
Facebook Data Mining
Social Media Data Mining
Text Data Mining
Bitcoin Data Mining
Orange Data Mining
Educational Data Mining
Data Mining in Healthcare
Data Mining in the World Wide Web
Machine Learning
What is Machine Learning
Applications of Machine learning
Machine learning Life cycle
Installing Anaconda and Python
Difference between Artificial intelligence and Machine learning
How to get datasets for Machine Learning
Data Preprocessing in Machine learning
Supervised Machine Learning
Unsupervised Machine Learning
Reinforcement Learning
Regression
Regression Analysis in Machine learning
Metrics for Evaluating Regression Model Performance
Linear Regression in Machine Learning
Simple Linear Regression in Machine Learning
Multiple Linear Regression
Polynomial Regression
Support Vector Machine Regression
Decision Tree Regression
Random Forest Regression
Lasso Regression
Ridge Regression
Classification
Logistic Regression Classification
K-Nearest Neighbor Classification
Support Vector Machine Classification
Kernel Support Vector Machine Classification
Naïve Bayes Classification
Decision Tree Classification
Random Forest Classification
Evaluating the Classification Model
Clustering
Clustering Performance Evaluation
Affinity Propagation Clustering
K-means Clustering
Mini Batch K-Means Clustering
Mean Shift Clustering
Spectral Clustering
Hierarchichal Clustering
DBSCAN Clustering
OPTICS Clustering
BIRCH Clustering
Gaussian Mixture Models Clustering
Association Rule Learning
Apriori algorithm
Frequent Pattern Growth
Eclat algorithm
Reinforcement Learning Algorithms
Upper Confidence Bound
Thompson Sampling
Natural Language Processing
Deep Learning
Artificial Neural Network
Convolution Neural Network
Recurrent Nueral Network
Dimensionality Reduction
Principal Component Analysis
Kernel Principal Component Analysis
Linear Discriminant Analysis
Quadratic Discriminant Analysis
Model Selection
K-fold Cross Validation
Grid Search
Ensemble Models
Bagging
Boosting
Bagging vs. Boosting
Stacking and Blending
Voting