Applied Machine Learning

Chapter 1 Introduction  
Data of Week 1 
1.1 Central Statistical Values 
1.2 Variation Statistical Values 
1.3 Other Statistical Values 
1.4 Machine Learning 
1.5 Python Tutorial 1 
Practice 1

Chapter 2 Data Exploration 
Data of Week 2 
2.1 Data Exploration 
2.2 Data Characteristics  
2.3 Data Preprocessing 
2.4 Visualization 
2.5 Python Tutorial 2 
Practice 2 

Chapter 3 Feature Engineering 
Data of Week 3 
3.1 Feature Engineering 
3.2 Feature Selection 
3.3 Model Evaluation Factors 
3.4 Python Tutorial 3 
Practice 3 

Chapter 4 Regression  
Data of Week 4 
4.1 Linear Regression  
4.2 Logistic Regression 
4.3 Python Tutorial 4  
Practice 4 

Chapter 5 Classification  
Data of Week 5 
5.1 Building Models  
5.2 Classification 
5.3 Python Tutorial 5 
Practice 5 

Chapter 6 Decision Tree 
Data of Week 6 
6.1 Introduction 
6.2 Model Evaluation 
6.3 Python Tutorial 6 
Practice 6 

Chapter 7 K-Nearest Neighbor 
Data of Week 7 
Depression and Anxiety Data 
7.1 Introduction 
7.2 Elbow Graph 
7.3 Python Tutorial 7 
Practice 7

Chapter 8 Support Vector Machine 
Data of Week 8 
Flight Price Prediction 
8.1 Kernels 
8.2 Support Vector Machine 
8.3 Python Tutorial 8 
Practice 8

Chapter 9 K-means 
Data of Week 9 
9.1 Clustering 
9.2 K-means 
9.3 Python Tutorial 9 
Practice 9 

Chapter 10 Hierarchical Clustering 
Data of Week 10 
10.1 Introduction 
10.2 Clustering Method Evaluation 
10.3 Python Tutorial 10 
Practice 10 

Chapter 11 Ensemble Methods  
Data of Week 11 
11.1 Learning Methods 
11.2 AdaBoost Algorithm 
11.3 Random Forest 
11.4 Python Tutorial 11 
Practice 11