EugenioRJ/machine-learning

Practical Full-Stack Machine Learning

Machine Learning

Practical Machine Learning topics for articles in my Medium blog

Content

1. General Setup
2. Data Analysis
   • Pandas
   • Applied Data Analysis and EDA
3. Web scraping
4. Data Visualization
5. TensorFlow
6. PyTorch
7. Scikit-Learn

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

• Reading & Writing data
  • 4 tricks to parse date columns with Pandas read_csv() |
  • Pandas read_csv() tricks you should know |
  • All Pandas read_html() you should know for scraping data from HTML tables |
  • How to convert JSON into a Pandas DataFrame? |
  • Pandas json_normalize() for flattening JSON |
• Data Profiling
  • 9 Pandas value_counts() tricks |
  • Getting more value from the Pandas count() |
• Data Preprocessing
  • What is One-Hot Encoding and how to use get_dummies() |
  • Working with missing values in Pandas | TBA soon
  • Working with datetime in Pandas DataFrame |
  • 11 Tricks to Master sort_values() in Pandas |
  • How to do a Custom Sort on Pandas DataFrame |
  • Pandas cut() to transform numerical data into categorical data |
  • Pandas qcut() for binning numerical data based on sample quantiles |
  • Finding and removing duplicate rows in Pandas DataFrame |
  • Renaming columns in a Pandas DataFrame |
  • 10 tricks for Converting data to a numeric type |
  • 10 tricks for Converting numbers and strings to datetime |
  • Pandas resample() tricks for manipulating time-series data |
  • When to use Pandas transform() function |
  • Difference between apply() and transform() in Pandas |
  • Introduction to Pandas apply(), applymap(), and map() | TBA soon
  • All the Pandas shift() you should know |
  • Delete rows/columns from a DataFrame using drop() |
  • Flatten MultiIndex columns and rows |
• Combining data
  • All the Pandas merge() for combining datasets |
  • Pandas concat() tricks to speed up your data analysis |
  • 5 Tricks to master Pandas append() |
• Selecting and Querying
  • Pandas loc and iloc for selecting data |
  • Pandas Equivalents of various SQL queries | TBA soon
  • Creating conditional columns with Numpy select() and where() methods |
  • Accessing data in a MultiIndex DataFrame |
• Reshaping
  • Reshaping a DataFrame from wide to long format using melt() |
  • Reshaping a DataFrame from long to wide format using pivot() |
  • Reshaping a DataFrame/Series with stack() and unstack() |
  • Exploding a list-like column with Pandas explode() method |
• Grouping and Summarizing
  • Pandas groupby() for grouping data and performing operations |
  • A Practical Introduction to Pandas pivot_table() |
  • Summarizing data with Pandas crosstab() |
• Best Practice & Code Readability
  • Using Pandas pipe() to improve code readability |
  • Using Pandas method chaining to improve code readability |
  • 7 setups you should include at the beginning of a data science project |
  • 6 Pandas Tricks you should know to speed up your data analysis |
  • 8 Commonly used Pandas display options you should know |
• Introduction & Others
  • A Practical Introduction to Pandas Series |

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 |

Data Visualization

• Dual-axis combo chart in Python - Matplotlib, Seaborn, and Pandas plot() |
• Adding 3rd Y-axis to combo chart in Python - Matplotlib, Seaborn, and Pandas plot() |

Altair

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

Matplotlib

• Matplotlib animation in Jupyter Notebook |
• Matplotlib Linear Regression animation in Jupyter Notebook |

TensorFlow

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

PyTorch

TBA

Scikit-Learn and General Machine Learning

• A Practical Introduction to Grid Search, Random Search, and Bayes Search |
• A Practical Introduction to 9 Regression Algorithms |
• Train-Test split and Cross-Validation you should know in Machine Learning (TBA) |