Chapter 1 - Introduction to Machine Learning
Machine Learning Overview
An Understanding of the Data Keywords
How to Learn Machine Learning
Types of Machine Learning
A History of Data Analysis: In Perspective of Business
[Environment Setup] Python Ecosystem for Machine Learning
[Environment Setup] How to use Jupyter Notebook
[참고자료] 가상환경과 Package 활용하기
Chapter 2 - Warm Up Section: An understanding of data
The concepts of a feature
Representing a model with numpy
' Supplements - Linear algebra
Lab: Simple Linear algebra concepts
Lab: Simple Linear algebra codes
– 과제 제출 방법 : Linear algebra
- Code Assignment: Linear algebra with pythonic code
Chapter 3 - Numpy Section
Assignment: Numpy in a nutshell
[Supplements] TF-KR 첫 모임: Zen of NumPy
Chapter 4 - Pandas Section #1
Pandas builit-in functions
Assignment: Build a matrix
Chapter 5 - Pandas Section #2
Database connection & Persistance
Chapter 6 - Matplotlib Section & Miniproject
Basic functions & operations
Data Cleaning Problem Overview
Categorical Data Handling
Casestudy - KagglepProblems
Chapter 7 - Linear Regression
Linear regression overview
Lab Assignment: Normal equation
Gradient descent approach
Linear regression wtih gradient descent
Linear regression implementation wtih Numpy
Multivariate linear regression models
Performance measure for a regression model
Linear regression implementation wtih scikit-learn
Lab Assignment: Gradient descent
Chapter 8 - Linear Regression extended
Stochastic gradient descent
SGD implementation issues
Overfitting and regularization overview
sklearn Linear Model family
Kaggle project : Bike demand
Chapter 9 - Logistics Regression
Logistic regression overview
Logistic regression implementation with Numpy
Maximum Likelihood Estimation
Logistic regresion with sklearn
Performance metrics for classification
Chapter 10 - Logistics Regression extended
Multiclass Classification overview
Softmax regression with numpy
Performance metrics for classification
Multiclass classification with scikit-learn
Chapter 11 - Naive Bayesian Classifier
Single variable bayes classifier
Navie bayesian Classifier
NB Classifier Implementation
NB classifier with sklearn
20news group classifaication 1
20news group classifaication 2
' Supplements : Text handling Lab: News categorization
Lab: News categorization 1
Lab: News categorization 2
Chapter 12 - Decision Tree
The algorithme of growing decision tree
Decision Tree with sklearn
Handling a continuous attribute
Decision Tree for Regression
Chapter 13 - Ensemble
Installation guide on Windows
Chapter 14 - Performance tuning
Feature Engineering I: Generation
Feature Engineering II: Statics
Feature Engineering III: Model based
Feature Engineering #4: Iterative
Hyperparmeter searching with Distributed Machines