Clustering with K-Means

4 minute read

In this exercise you’ll explore our first unsupervised learning technique for creating features

Creating Featues

7 minute read

In this exercise you’ll start developing the features

Tips for Creating Features

2 minute read

Tips from kaggle’s instructor ‘Ryan Holbrook’.

Mutual Information

5 minute read

Mutual information describes relationships in terms of uncertainty.

Machine Learning Competitions

5 minute read

You will create and submit predictions for a Kaggle competition.

Random Forest

1 minute read

Random Forest is going to be an easy win.

Underfitting and Overfitting

2 minute read

optimize the size of the tree to make better predictions.

Mode Validation

3 minute read

You will test how good your model is.

Your First Machine Learning Model

4 minute read

Select the target variable, which corresponds to the sales price.

Explore Your Data

3 minute read

Filter the data, build model, and improve model.

Distributions

8 minute read

Create histograms and density plots

Scatter Plots

8 minute read

Leverage the coordinate plane to explore relationships between variables

Bar Charts and Heatmaps

7 minute read

Use color or length to compare categories in a dataset

Line Charts

6 minute read

Visualize trends over time

Hello, Seaborn

6 minute read

Your first introduction to coding for data visualization

IntermediateML data-leakage

8 minute read

You will learn what data leakage is and how to prevent it.

IntermediateML XGBoost

5 minute read

you will learn how to build and optimize models with gradient boosting

IntermediateML cross-validation

5 minute read

you will learn how to use cross-validation for better measures of model performance.

Pipelines

less than 1 minute read

Pipelines are a simple way to keep your data preprocessing and modeling code organized.

IntermediateML pipelines

8 minute read

This notebook is an exercise in the Intermediate Machine Learning course. You can reference the tutorial at this link.

IntermediateML categorical-variables

13 minute read

Notice that the dataset contains both numerical and categorical variables.

IntermediateML missing-values

10 minute read

You’ll obtain a more comprehensive understanding of the missing values in the dataset.

IntermediateML introduction

5 minute read

Now it’s time to go through the modeling process and make predictions.

Pandas Renaming and Combining

11 minute read

Run the following cell to load your data and some utility functions.

Pandas Data-types and Missing

3 minute read

Run the following cell to load your data and some utility functions.

Pandas grouping and Sorting

6 minute read

In these exercises we’ll apply groupwise analysis to our dataset.

Pandas Summary, Functions and Maps

4 minute read

Now you are ready to get a deeper understanding of your data.

Pandas Indexing, Selecting and assigning

6 minute read

In this set of exercises we will work with the Wine Reviews dataset.

Pandas Creating, Reading and Writing

2 minute read

The first step in most data analytics projects is reading the data file.

Save Your Time

1 minute read

introduce useful Pandas Snippets

Building Your Model

less than 1 minute read

The steps to building and using a model are: Define: What type of model will it be? A decison tree? Some other type of model? Some other parameters...

[RMSE] BusRidersPredic

6 minute read

```python import pandas as pd

[GAN] Training Handwriting

3 minute read

Handwritten Digits - MNIST GAN With Seed Experiments Make Your First GAN With PyTorch, 20202

[Notion] First Notion

less than 1 minute read

[GAN] Simple Pattern

2 minute read

import torch import torch.nn as nn import pandas as pd import matplotlib.pyplot as plt import random

첫 포스팅~^^

less than 1 minute read

오늘 첫 블로그를 시작합니다. 저의 세계를 열어볼께요.

[GAN] Enhancing the Performance

2 minute read

# GAN 03 Enhancing the Performance from google.colab import drive drive.mount('./mount')