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02-Regular-Expressions.ipynb
02-Regular-Expressions.ipynb
 
 
1GridSearch1.ipynb
1GridSearch1.ipynb
 
 
Abstract class and method.py
Abstract class and method.py
 
 
Create new columns.py
Create new columns.py
 
 
Decorators.ipynb
Decorators.ipynb
 
 
Excel_Copy.ipynb
Excel_Copy.ipynb
 
 
Feature_generation_in_batches.py
Feature_generation_in_batches.py
 
 
GP_pandas
GP_pandas
 
 
GridSearch.py
GridSearch.py
 
 
HTML_image_base64.py
HTML_image_base64.py
 
 
How Boosting Works.pdf
How Boosting Works.pdf
 
 
IBM_SPSS.ipynb
IBM_SPSS.ipynb
 
 
Imputing&Encoding.py
Imputing&Encoding.py
 
 
KC_Macros.ipynb
KC_Macros.ipynb
 
 
KC_Missing_values.py
KC_Missing_values.py
 
 
KC_outliers.py
KC_outliers.py
 
 
Multi Threads.py
Multi Threads.py
 
 
Multi perf.py
Multi perf.py
 
 
Multi-Processing and Lock.ipynb
Multi-Processing and Lock.ipynb
 
 
Multiprocessing - CPUs
Multiprocessing - CPUs
 
 
Multiprocessing and Class.ipynb
Multiprocessing and Class.ipynb
 
 
Multiprocessing-number of workers.py
Multiprocessing-number of workers.py
 
 
Multiprocessing-queue.py
Multiprocessing-queue.py
 
 
Multiprocessing-save in memory.py
Multiprocessing-save in memory.py
 
 
Multiprocessing-shared memory.py
Multiprocessing-shared memory.py
 
 
MySQL on Mac
MySQL on Mac
 
 
NLP-features
NLP-features
 
 
Normalization.py
Normalization.py
 
 
Pass variable to HTML and JSON.py
Pass variable to HTML and JSON.py
 
 
Pitfall01-data type pandas.py
Pitfall01-data type pandas.py
 
 
Pitfall02-conflicted package file name.py
Pitfall02-conflicted package file name.py
 
 
Pitfall03-save plot figure.py
Pitfall03-save plot figure.py
 
 
Pitfall04-keyspace.py
Pitfall04-keyspace.py
 
 
Pitfall05-variable name.py
Pitfall05-variable name.py
 
 
Pitfall06-order of if statements
Pitfall06-order of if statements
 
 
Pitfall07-kill process.py
Pitfall07-kill process.py
 
 
Pitfall08-gunicorn SIGTERM
Pitfall08-gunicorn SIGTERM
 
 
Pitfall09-time.time()
Pitfall09-time.time()
 
 
Pitfall10 The truth value of a Series is ambiguous
Pitfall10 The truth value of a Series is ambiguous
 
 
Pitfall11 futex
Pitfall11 futex
 
 
Pitfall12 class and instance attributes
Pitfall12 class and instance attributes
 
 
Pitfall12 recursive
Pitfall12 recursive
 
 
Pitfall13 regular expression
Pitfall13 regular expression
 
 
Pitfall14 global variable
Pitfall14 global variable
 
 
Pitfall15_voice engine
Pitfall15_voice engine
 
 
Pitfall16 future
Pitfall16 future
 
 
Pitfall17 - function argument
Pitfall17 - function argument
 
 
Pitfall18 - thread safe
Pitfall18 - thread safe
 
 
Pitfall19 index
Pitfall19 index
 
 
RAG001.py
RAG001.py
 
 
RAG102.py
RAG102.py
 
 
RAG103_agent_memory.py
RAG103_agent_memory.py
 
 
RAG104_ec2.py
RAG104_ec2.py
 
 
README.md
README.md
 
 
Read data from database.py
Read data from database.py
 
 
Requests_cloud_session.py
Requests_cloud_session.py
 
 
Siden.ipynb
Siden.ipynb
 
 
Skewness.py
Skewness.py
 
 
Stratified Sampling.py
Stratified Sampling.py
 
 
Transformation Pipelines.py
Transformation Pipelines.py
 
 
Tree Tuning.ipynb
Tree Tuning.ipynb
 
 
True Explanation 001
True Explanation 001
 
 
U-git.py
U-git.py
 
 
U_Bayesian Optimization.py
U_Bayesian Optimization.py
 
 
U_hyperparameters.py
U_hyperparameters.py
 
 
U_linear model assumptions.py
U_linear model assumptions.py
 
 
U_missing indicator.py
U_missing indicator.py
 
 
U_model sensitivity.py
U_model sensitivity.py
 
 
U_validation_cross validation.py
U_validation_cross validation.py
 
 
break nested loops.py
break nested loops.py
 
 
broadcastable
broadcastable
 
 
buffer-BytesIO().py
buffer-BytesIO().py
 
 
class variable and instance variable.py
class variable and instance variable.py
 
 
cron timezone
cron timezone
 
 
datetime_query
datetime_query
 
 
design pattern_SOLID
design pattern_SOLID
 
 
design pattern_adapter_dynamic link classes
design pattern_adapter_dynamic link classes
 
 
ec2 space
ec2 space
 
 
git.py
git.py
 
 
hash-uuid
hash-uuid
 
 
intents.py
intents.py
 
 
json string.py
json string.py
 
 
kaggle image dataset_tensorflow
kaggle image dataset_tensorflow
 
 
kaggle_SparseCategoricalCrossentropy
kaggle_SparseCategoricalCrossentropy
 
 
keras
keras
 
 
logits
logits
 
 
logstic regression
logstic regression
 
 
mount.py
mount.py
 
 
move files commands
move files commands
 
 
neo4j_code.py
neo4j_code.py
 
 
neo4j_memory_chat.py
neo4j_memory_chat.py
 
 
neo4j_query.py
neo4j_query.py
 
 
neo4j_vectorization
neo4j_vectorization
 
 
neo4j_vectorization_txt
neo4j_vectorization_txt
 
 
package
package
 
 
pattern match.py
pattern match.py
 
 
pd.apply vs applymap.py
pd.apply vs applymap.py
 
 
pd.display all columns.py
pd.display all columns.py
 
 
pd.read
pd.read
 
 
pd.saitpython.py
pd.saitpython.py
 
 

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Hands-on-Machine-Learning

Highlights in Machine Learning

Data preparation: train_test split (stratified sampling) Imputing (missing data) Encoding (categorical data) Scaling (min-max or z-score/standardization) Pipelines

Train models (The goal is to shortlist a few promising models): cross-validatoin underfitting overfitting

Fine tune selected models: GridSearch RandomSearch Ensemble Voting/bagging/boosting/stacking

Analyze best models and their errors:

Evaluate your final model on test set: Usually slightly worse than the results you got with cross-validation, because you fine tuned your model to perform well on the validation data. DO NOT fine tune the model based on your test set.

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