Machine Learning

General Setup

• Create Virtual Environment using “virtualenv” and add it to Jupyter Notebook
• Create Virtual Environment using “conda” and add it to Jupyter Notebook
• 7 ways to load external data into Google Colab

Data Analysis

Pandas Tutorials

• Using Pandas pipe function to improve code readability | 📙 Notebook
• Using Pandas method chaining to improve code readability | 📙 Notebook
• What is One-Hot Encoding and how to use Pandas get_dummies function | 📙 Notebook
• 7 setups you should include at the beginning of a data science project | 📙 Notebook
• 6 Pandas Tricks you should know to speed up your data analysis | 📙 Notebook
• 4 tricks you should know to parse date columns with Pandas read_csv() | 📙 Notebook
• Pandas read_csv() tricks you should know | 📙 Notebook
• Working with datetime in Pandas DataFrame | 📙 Notebook
• When to use Pandas transform() function | 📙 Notebook
• Difference between apply() and transform() in Pandas | 📙 Notebook
• Introduction to Pandas apply(), applymap(), and map() | TBA soon
• Working with missing values in Pandas | TBA soon
• Pandas Equivalents of various SQL queries | TBA soon
• A Practical Introduction to Pandas pivot_table() | 📙 Notebook
• Creating conditional columns on Pandas with Numpy select() and where() methods | 📙 Notebook
• How to do a Custom Sort on Pandas DataFrame | 📙 Notebook
• Pandas concat() tricks you should know to speed up your data analysis | 📙 Notebook
• Pandas resample() tricks you should know for manipulating time-series data | 📙 Notebook
• All the Pandas merge() you should know for combining datasets | 📙 Notebook
• All the Pandas shift() you should know | 📙 Notebook
• All Pandas read_html() you should know for scraping data from HTML tables | 📙 Notebook
• How to convert JSON into a Pandas DataFrame? | 📙 Notebook
• A Practical Introduction to Pandas Series | 📙 Notebook
• Pandas cut() to transform numerical data into categorical data | 📙 Notebook
• Pandas qcut() for binning numerical data based on sample quantiles | 📙 Notebook
• Pandas json_normalize() for flattening JSON | 📙 Notebook
• Renaming columns in a Pandas DataFrame | 📙 Notebook
• Pandas groupby() for grouping data and performing operations | 📙 Notebook
• Pandas loc and iloc for selecting data | 📙 Notebook
• Accessing data in a MultiIndex DataFrame | 📙 Notebook
• Finding and removing duplicate rows in Pandas DataFrame | 📙 Notebook
• 10 tricks for Converting data to a numeric type in Pandas | 📙 Notebook
• 10 tricks for Converting numbers and strings to datetime in Pandas | 📙 Notebook
• 8 Commonly used Pandas display options you should know | 📙 Notebook

Applied Data Analysis and EDA

• COVID-19 data processing with Pandas DataFrame | TBA soon

Web scraping

• Scraping tables from a JavaScript webpage using Selenium, BeautifulSoup, and Pandas | 📙 Notebook

Data Visualization

Altair

• Python Interactive Data Visualization with Altair | Gist
• Interactive Data Visualization for exploring Coronavirus Spreads | Gist

Matplotlib

• Matplotlib animation in Jupyter Notebook | 📙 Notebook

TensorFlow in practice

• The Google's 7 steps of Machine Learing in Practice | Notebook
• 3 ways to create a Machine Learning model with Keras and TensorFlow 2.0 | Notebook
• Model Regularization in practice | Notebook
• Batch Normalization in practice | Notebook
• Early Stopping in practice | Notebook
• Learning Rate schedules in Practice | Notebook
• Keras Callbacks in Practice | Notebook
• Keras Custom Callbacks | Notebook
• 7 popular activation functions in Deep Learning | Notebook
• Why ReLU in Deep Learning and the best practice | Notebook

Scikit-Learn in practice

PyTorch in practice