Chapter 2. Classifying with scikit-learn Estimators
$pip install matplotlib
Nearest neighbors Classification

• ?(Categorical-based datasets vs. Numerical-based datasets)
• Distance Metrics (Which one do you prefer?): Eucliedean Distance, Manhattan distance and Cosine distance
(Normalization?)
Example: DataSet (Data Folder :ionosphere.data,ionosphere.names )

Chapter 3. Predicting Sports Winners with Decision Trees
Using "pandas" to load the dataset
$ conda install pandas The Website http://basketball-reference.com
2015-16 NBA Schedule and Results
NBA_2015_10_Basketball.csv(Share&more, get CSV (for excel))
2014-15 NBA Standings (Expanded Standing, Export linl, get "standings.csv")
On-Line HomeWork
NBA_2015-2016_Basketball.csv

Chapter 5. Features and scikit-learn Transformers
DataMining_FeatureSelecionAndTransformation_jdwang2018_3_21.pdf

Chapter 11 : Object Detection in Images using Deep Neural Networks(code example (Moodle))
DataMining_Chapter11_ObjectDetectionInImages_jdwang2018_4_18.pdf
The CIFAR- and CIFAR-100 dataset

What is the class of this image ?

Data Set for practice Image Classification : >CIFAR-10 and CIFAR-100 datasets
TensorFlowAndKeras_jdwang2018_5_5.pdf
DeepLearning_TensorFlowAndKeras_jdwang2018_5_13.zip (code example (Moodle))

SentimentAnalysis(OpinionMining)_jdwang2018_5_20.pdf
Python_Keras_Imdb_jdwang2018_5_20.zip (code example (Moodle))

Meachine Learning Crash Course
Google (GPU): Colaboratory