MachineLearningWithPython

Chapter 1 : Preprocessing Data
Chapter 2 : [Supervised Learning - Regression]
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Lasso-Ridge Regression
- Model Performance - Regression
Chapter 3 : [Supervised Learning - Classification]
- Logistic Regression
- K-Nearest Neighbour
- Support Vector Machine
- Decision Tree
- Naive Bayes
Chapter 4 : UnSupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- Apriori Association
- Eclat - Association
Chapter 5 : [Deep Learning/Neural Networks]
- Artificial Neural Networks
- Convolutional Neural Networks
Chapter 6 : Dimensionality Reduction
- PCA, LDA
Chapter 7 : [Natural Language Processing]
- Text Processing
- Autosummarize Article
- Classify Article
Chapter 8 : [Reinforcement Learning]
- [Upper Confidence Bound]
- [Thompson Sampling]
Chapter 9 : [Ensemble Algorithm]
- Random Forest
- [XGBoost]
Chapter 10 : [Computer Vision]
- ImageFeatureExtraction